1
|
McErlean J, Malik J, Lin YT, Talmon R, Wu HT. Unsupervised ensembling of multiple software sensors with phase synchronization: a robust approach for electrocardiogram-derived respiration. Physiol Meas 2024; 45:035008. [PMID: 38350132 DOI: 10.1088/1361-6579/ad290b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2023] [Accepted: 02/13/2024] [Indexed: 02/15/2024]
Abstract
Objective.We aimed to fuse the outputs of different electrocardiogram-derived respiration (EDR) algorithms to create one higher quality EDR signal.Methods.We viewed each EDR algorithm as a software sensor that recorded breathing activity from a different vantage point, identified high-quality software sensors based on the respiratory signal quality index, aligned the highest-quality EDRs with a phase synchronization technique based on the graph connection Laplacian, and finally fused those aligned, high-quality EDRs. We refer to the output as the sync-ensembled EDR signal. The proposed algorithm was evaluated on two large-scale databases of whole-night polysomnograms. We evaluated the performance of the proposed algorithm using three respiratory signals recorded from different hardware sensors, and compared it with other existing EDR algorithms. A sensitivity analysis was carried out for a total of five cases: fusion by taking the mean of EDR signals, and the four cases of EDR signal alignment without and with synchronization and without and with signal quality selection.Results.The sync-ensembled EDR algorithm outperforms existing EDR algorithms when evaluated by the synchronized correlation (γ-score), optimal transport (OT) distance, and estimated average respiratory rate score, all with statistical significance. The sensitivity analysis shows that the signal quality selection and EDR signal alignment are both critical for the performance, both with statistical significance.Conclusion.The sync-ensembled EDR provides robust respiratory information from electrocardiogram.Significance.Phase synchronization is not only theoretically rigorous but also practical to design a robust EDR.
Collapse
Affiliation(s)
- Jacob McErlean
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - John Malik
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
| | - Yu-Ting Lin
- School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Department of Anesthesiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Ronen Talmon
- Faculty of Electrical and Computer Engineering, Technion-Israel Institute of Technology, Haifa, Israel
| | - Hau-Tieng Wu
- Department of Mathematics, Duke University, Durham, North Carolina, United States of America
- Department of Statistical Science, Duke University, Durham, North Carolina, United States of America
| |
Collapse
|
2
|
Yamashita S, Saitoh T, Iguchi K, Suwa K, Ohtani H, Saotome M, Maekawa Y. Electrocardiogram Electrode Positioning on the Back During Echocardiography: An Exploratory Cross-Sectional Study. Cureus 2024; 16:e57967. [PMID: 38738079 PMCID: PMC11086598 DOI: 10.7759/cureus.57967] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/09/2024] [Indexed: 05/14/2024] Open
Abstract
BACKGROUND ECG interpretation is sometimes difficult due to baseline fluctuations and electrode detachments when placed on the subjects' front side, leading to misinterpretation of the rhythms and phases of the cardiac cycle. We aimed to compare the differences in the wave amplitudes and respiratory variations between conventional ECG electrode positioning on the front side of patients and an alternative position on the backs of patients. METHODS Echocardiography was performed in 85 patients lying in the left lateral position. We attached the red electrode to the right clavicle, the yellow to the left clavicle, and the green to the left lateral abdomen on the front side of the patients; on the back, we attached the electrode to the right clavicle, the right upper posterior iliac spine, and the left upper posterior iliac spine. RESULTS The ECG monitor amplitudes were greater on the front side compared to the back side, but the BF-breath values were smaller on the back side (6.0 pixels) compared to the front side (10.5 pixels, p<0.05). The P wave amplitude divided by the BF-breath on the back side was greater than that seen on the front side (2.8 vs. 1.8, p<0.05), whereas the QRS amplitude divided by the BF-breath was 15.0 and 16.3, respectively (p=ns). CONCLUSION As an alternative to front-side ECG monitoring, electrodes placed on the back can help avoid misinterpretation of the ECG rhythms and the phases of the cardiac cycle due to respiration during echocardiography.
Collapse
Affiliation(s)
- Satoshi Yamashita
- Department of Cardiology, Hamamatsu University School of Medicine, Hamamatsu, JPN
| | - Takeji Saitoh
- Next Generation Creative Education Center for Medicine, Engineering, and Informatics, Hamamatsu University School of Medicine, Hamamatsu, JPN
| | - Keisuke Iguchi
- Department of Cardiology, Hamamatsu University School of Medicine, Hamamatsu, JPN
| | - Kenichiro Suwa
- Department of Cardiology, Hamamatsu University School of Medicine, Hamamatsu, JPN
| | - Hayato Ohtani
- Department of Cardiology, Hamamatsu University School of Medicine, Hamamatsu, JPN
| | - Masao Saotome
- Department of Cardiology, Hamamatsu University School of Medicine, Hamamatsu, JPN
| | - Yuichiro Maekawa
- Department of Cardiology, Hamamatsu University School of Medicine, Hamamatsu, JPN
| |
Collapse
|
3
|
Castro Ribeiro T, García Pagès E, Ballester L, Vilagut G, García Mieres H, Suárez Aragonès V, Amigo F, Bailón R, Mortier P, Pérez Sola V, Serrano-Blanco A, Alonso J, Aguiló J. Design of a Remote Multiparametric Tool to Assess Mental Well-Being and Distress in Young People (mHealth Methods in Mental Health Research Project): Protocol for an Observational Study. JMIR Res Protoc 2024; 13:e51298. [PMID: 38551647 PMCID: PMC11015365 DOI: 10.2196/51298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Revised: 12/22/2023] [Accepted: 01/11/2024] [Indexed: 04/15/2024] Open
Abstract
BACKGROUND Mental health conditions have become a substantial cause of disability worldwide, resulting in economic burden and strain on the public health system. Incorporating cognitive and physiological biomarkers using noninvasive sensors combined with self-reported questionnaires can provide a more accurate characterization of the individual's well-being. Biomarkers such as heart rate variability or those extracted from the electrodermal activity signal are commonly considered as indices of autonomic nervous system functioning, providing objective indicators of stress response. A model combining a set of these biomarkers can constitute a comprehensive tool to remotely assess mental well-being and distress. OBJECTIVE This study aims to design and validate a remote multiparametric tool, including physiological and cognitive variables, to objectively assess mental well-being and distress. METHODS This ongoing observational study pursues to enroll 60 young participants (aged 18-34 years) in 3 groups, including participants with high mental well-being, participants with mild to moderate psychological distress, and participants diagnosed with depression or anxiety disorder. The inclusion and exclusion criteria are being evaluated through a web-based questionnaire, and for those with a mental health condition, the criteria are identified by psychologists. The assessment consists of collecting mental health self-reported measures and physiological data during a baseline state, the Stroop Color and Word Test as a stress-inducing stage, and a final recovery period. Several variables related to heart rate variability, pulse arrival time, breathing, electrodermal activity, and peripheral temperature are collected using medical and wearable devices. A second assessment is carried out after 1 month. The assessment tool will be developed using self-reported questionnaires assessing well-being (short version of Warwick-Edinburgh Mental Well-being Scale), anxiety (Generalized Anxiety Disorder-7), and depression (Patient Health Questionnaire-9) as the reference. We will perform correlation and principal component analysis to reduce the number of variables, followed by the calculation of multiple regression models. Test-retest reliability, known-group validity, and predictive validity will be assessed. RESULTS Participant recruitment is being carried out on a university campus and in mental health services. Recruitment commenced in October 2022 and is expected to be completed by June 2024. As of July 2023, we have recruited 41 participants. Most participants correspond to the group with mild to moderate psychological distress (n=20, 49%), followed by the high mental well-being group (n=13, 32%) and those diagnosed with a mental health condition (n=8, 20%). Data preprocessing is currently ongoing, and publication of the first results is expected by September 2024. CONCLUSIONS This study will establish an initial framework for a comprehensive mental health assessment tool, taking measurements from sophisticated devices, with the goal of progressing toward a remotely accessible and objectively measured approach that maintains an acceptable level of accuracy in clinical practice and epidemiological studies. TRIAL REGISTRATION OSF Registries N3GCH; https://doi.org/10.17605/OSF.IO/N3GCH. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID) DERR1-10.2196/51298.
Collapse
Affiliation(s)
- Thais Castro Ribeiro
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| | - Esther García Pagès
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| | - Laura Ballester
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Gemma Vilagut
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Helena García Mieres
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Víctor Suárez Aragonès
- Department of Clinical Psychology and Psychobiology, University of Barcelona, Barcelona, Spain
| | - Franco Amigo
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Raquel Bailón
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Aragón Institute of Engineering Research (I3A), University of Zaragoza, Zaragoza, Spain
| | - Philippe Mortier
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
| | - Víctor Pérez Sola
- CIBER en Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Institute of Neuropsychiatry and Addictions (INAD), Parc de Salut Mar (PSMAR), Barcelona, Spain
- Neurosciences Research Group, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Antoni Serrano-Blanco
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Institut de Recerca Sant Joan de Déu, Parc Sanitari Sant Joan de Déu, Barcelona, Spain
| | - Jordi Alonso
- CIBER de Epidemiología y Salud Pública (CIBERESP), Instituto de Salud Carlos III, Madrid, Spain
- Health Services Research Group, Hospital del Mar Research Institute, Barcelona, Spain
- Department of Medicine and Life Sciences, Universitat Pompeu Fabra, Barcelona, Spain
| | - Jordi Aguiló
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Instituto de Salud Carlos III, Madrid, Spain
- Departament of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Bellaterra, Spain
| |
Collapse
|
4
|
Roberts JD, Walton RD, Loyer V, Bernus O, Kulkarni K. Open-source software for respiratory rate estimation using single-lead electrocardiograms. Sci Rep 2024; 14:167. [PMID: 38168512 PMCID: PMC10762020 DOI: 10.1038/s41598-023-50470-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2023] [Accepted: 12/20/2023] [Indexed: 01/05/2024] Open
Abstract
Respiratory rate (RR) is a critical vital sign used to assess pulmonary function. Currently, RR estimating instrumentation is specialized and bulky, therefore unsuitable for remote health monitoring. Previously, RR was estimated using proprietary software that extract surface electrocardiogram (ECG) waveform features obtained at several thoracic locations. However, developing a non-proprietary method that uses minimal ECG leads, generally available from mobile cardiac monitors is highly desirable. Here, we introduce an open-source and well-documented Python-based algorithm that estimates RR requiring only single-stream ECG signals. The algorithm was first developed using ECGs from awake, spontaneously breathing adult human subjects. The algorithm-estimated RRs exhibited close linear correlation to the subjects' true RR values demonstrating an R2 of 0.9092 and root mean square error of 2.2 bpm. The algorithm robustness was then tested using ECGs generated by the ischemic hearts of anesthetized, mechanically ventilated sheep. Although the ECG waveforms during ischemia exhibited severe morphologic changes, the algorithm-determined RRs exhibited high fidelity with a resolution of 1 bpm, an absolute error of 0.07 ± 0.07 bpm, and a relative error of 0.67 ± 0.64%. This optimized Python-based RR estimation technique will likely be widely adapted for remote lung function assessment in patients with cardiopulmonary disease.
Collapse
Affiliation(s)
- Jesse D Roberts
- Departments of Anesthesia, Pediatrics, and Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Richard D Walton
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France
| | - Virginie Loyer
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France
| | - Olivier Bernus
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France
| | - Kanchan Kulkarni
- IHU-LIRYC, Heart Rhythm Disease Institute, Fondation Bordeaux Université, 33600, Pessac, Bordeaux, France.
- INSERM, Centre de Recherche Cardio-Thoracique de Bordeaux, U1045, University of Bordeaux, 33000, Bordeaux, France.
| |
Collapse
|
5
|
Sánchez C, Hernando A, Bolea J, Izquierdo D, Rodríguez G, Olea A, Lozano MT, Peláez-Coca MD. Enhancing Safety in Hyperbaric Environments through Analysis of Autonomic Nervous System Responses: A Comparison of Dry and Humid Conditions. SENSORS (BASEL, SWITZERLAND) 2023; 23:s23115289. [PMID: 37300016 DOI: 10.3390/s23115289] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Revised: 05/26/2023] [Accepted: 05/31/2023] [Indexed: 06/12/2023]
Abstract
Diving can have significant cardiovascular effects on the human body and increase the risk of developing cardiac health issues. This study aimed to investigate the autonomic nervous system (ANS) responses of healthy individuals during simulated dives in hyperbaric chambers and explore the effects of the humid environment on these responses. Electrocardiographic- and heart-rate-variability (HRV)-derived indices were analyzed, and their statistical ranges were compared at different depths during simulated immersions under dry and humid conditions. The results showed that humidity significantly affected the ANS responses of the subjects, leading to reduced parasympathetic activity and increased sympathetic dominance. The power of the high-frequency band of the HRV after removing the influence of respiration, PHF⟂¯, and the number of pairs of successive normal-to-normal intervals that differ by more than 50 ms divided by the total number of normal-to-normal intervals, pNN50¯, indices were found to be the most informative in distinguishing the ANS responses of subjects between the two datasets. Additionally, the statistical ranges of the HRV indices were calculated, and the classification of subjects as "normal" or "abnormal" was determined based on these ranges. The results showed that the ranges were effective at identifying abnormal ANS responses, indicating the potential use of these ranges as a reference for monitoring the activity of divers and avoiding future immersions if many indices are out of the normal ranges. The bagging method was also used to include some variability in the datasets' ranges, and the classification results showed that the ranges computed without proper bagging represent reality and its associated variability. Overall, this study provides valuable insights into the ANS responses of healthy individuals during simulated dives in hyperbaric chambers and the effects of humidity on these responses.
Collapse
Affiliation(s)
- Carlos Sánchez
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
| | - Alberto Hernando
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
| | - Juan Bolea
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| | - David Izquierdo
- GTF Group, I3A Institute, University of Zaragoza, 50018 Zaragoza, Spain
| | - Germán Rodríguez
- Departamento de Ingeniería y Técnicas Aplicadas, Centro Universitario de la Defensa de San Javier, Academia General del Aire, 30729 Murcia, Spain
| | - Agustín Olea
- Centro de Buceo de la Armada de Cartagena, 30205 Murcia, Spain
| | - María Teresa Lozano
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| | - María Dolores Peláez-Coca
- BSICoS Group, I3A Institute, IIS Aragón, University of Zaragoza, 50018 Zaragoza, Spain
- Departamento de Física, Centro Universitario de la Defensa de Zaragoza, Academia General Militar, 50090 Zaragoza, Spain
| |
Collapse
|
6
|
Castro Ribeiro T, Sobregrau Sangrà P, García Pagès E, Badiella L, López-Barbeito B, Aguiló S, Aguiló J. Assessing effectiveness of heart rate variability biofeedback to mitigate mental health symptoms: a pilot study. Front Physiol 2023; 14:1147260. [PMID: 37234414 PMCID: PMC10206049 DOI: 10.3389/fphys.2023.1147260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Accepted: 04/24/2023] [Indexed: 05/28/2023] Open
Abstract
Introduction: The increasing burden on mental health has become a worldwide concern especially due to its substantial negative social and economic impact. The implementation of prevention actions and psychological interventions is crucial to mitigate these consequences, and evidence supporting its effectiveness would facilitate a more assertive response. Heart rate variability biofeedback (HRV-BF) has been proposed as a potential intervention to improve mental wellbeing through mechanisms in autonomic functioning. The aim of this study is to propose and evaluate the validity of an objective procedure to assess the effectiveness of a HRV-BF protocol in mitigating mental health symptoms in a sample of frontline HCWs (healthcare workers) who worked in the COVID-19 pandemic. Methods: A prospective experimental study applying a HRV-BF protocol was conducted with 21 frontline healthcare workers in 5 weekly sessions. For PRE-POST intervention comparisons, two different approaches were used to evaluate mental health status: applying (a) gold-standard psychometric questionnaires and (b) electrophysiological multiparametric models for chronic and acute stress assessment. Results: After HRV-BF intervention, psychometric questionnaires showed a reduction in mental health symptoms and stress perception. The electrophysiological multiparametric also showed a reduction in chronic stress levels, while the acute stress levels were similar in PRE and POST conditions. A significant reduction in respiratory rate and an increase in some heart rate variability parameters, such as SDNN, LFn, and LF/HF ratio, were also observed after intervention. Conclusion: Our findings suggest that a 5-session HRV-BF protocol is an effective intervention for reducing stress and other mental health symptoms among frontline HCWs who worked during the COVID-19 pandemic. The electrophysiological multiparametric models provide relevant information about the current mental health state, being useful for objectively evaluating the effectiveness of stress-reducing interventions. Further research could replicate the proposed procedure to confirm its feasibility for different samples and specific interventions.
Collapse
Affiliation(s)
- Thais Castro Ribeiro
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Pau Sobregrau Sangrà
- Clínic Foundation for Biomedical Research, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Esther García Pagès
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| | - Llorenç Badiella
- Applied Statistics Service, Autonomous University of Barcelona, Barcelona, Spain
| | | | - Sira Aguiló
- Emergency Department, Hospital Clínic of Barcelona, Barcelona, Spain
| | - Jordi Aguiló
- Biomedical Research Network Center in Biogineering, Biomaterial and Nanomedicine (CIBER-BBN), Madrid, Spain
- Department of Microelectronics and Electronic Systems, Autonomous University of Barcelona, Barcelona, Spain
| |
Collapse
|
7
|
Liuzzi P, Grippo A, Draghi F, Hakiki B, Macchi C, Cecchi F, Mannini A. Can Respiration Complexity Help the Diagnosis of Disorders of Consciousness in Rehabilitation? Diagnostics (Basel) 2023; 13:diagnostics13030507. [PMID: 36766612 PMCID: PMC9914359 DOI: 10.3390/diagnostics13030507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2022] [Revised: 01/23/2023] [Accepted: 01/27/2023] [Indexed: 01/31/2023] Open
Abstract
BACKGROUND Autonomic Nervous System (ANS) activity, as cardiac, respiratory and electrodermal activity, has been shown to provide specific information on different consciousness states. Respiration rates (RRs) are considered indicators of ANS activity and breathing patterns are currently already included in the evaluation of patients in critical care. OBJECTIVE The aim of this work was to derive a proxy of autonomic functions via the RR variability and compare its diagnostic capability with known neurophysiological biomarkers of consciousness. METHODS In a cohort of sub-acute patients with brain injury during post-acute rehabilitation, polygraphy (ECG, EEG) recordings were collected. The EEG was labeled via descriptors based on American Clinical Neurophysiology Society terminology and the respiration variability was extracted by computing the Approximate Entropy (ApEN) of the ECG-derived respiration signal. Competing logistic regressions were applied to evaluate the improvement in model performances introduced by the RR ApEN. RESULTS Higher RR complexity was significantly associated with higher consciousness levels and improved diagnostic models' performances in contrast to the ones built with only electroencephalographic descriptors. CONCLUSIONS Adding a quantitative, instrumentally based complexity measure of RR variability to multimodal consciousness assessment protocols may improve diagnostic accuracy based only on electroencephalographic descriptors. Overall, this study promotes the integration of biomarkers derived from the central and the autonomous nervous system for the most comprehensive diagnosis of consciousness in a rehabilitation setting.
Collapse
Affiliation(s)
- Piergiuseppe Liuzzi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
- Istituto di BioRobotica, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy
| | - Antonello Grippo
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
| | - Francesca Draghi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
| | - Bahia Hakiki
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
- Correspondence: ; Tel.: +39-333-401-8388
| | - Claudio Macchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
- Dipartimento di Medicina Sperimentale e Clinica, Universita di Firenze, Largo Brambilla 3, 50134 Firenze, Italy
| | - Francesca Cecchi
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
- Istituto di BioRobotica, Scuola Superiore Sant’Anna, Viale Rinaldo Piaggio 34, 56025 Pontedera, Italy
- Dipartimento di Medicina Sperimentale e Clinica, Universita di Firenze, Largo Brambilla 3, 50134 Firenze, Italy
| | - Andrea Mannini
- IRCCS Fondazione Don Carlo Gnocchi ONLUS, Via di Scandicci 269, 50143 Firenze, Italy
| |
Collapse
|
8
|
Abdollahpur M, Engström G, Platonov PG, Sandberg F. A subspace projection approach to quantify respiratory variations in the f-wave frequency trend. Front Physiol 2022; 13:976925. [PMID: 36200057 PMCID: PMC9527347 DOI: 10.3389/fphys.2022.976925] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/23/2022] [Indexed: 11/13/2022] Open
Abstract
Background: The autonomic nervous system (ANS) is known as a potent modulator of the initiation and perpetuation of atrial fibrillation (AF), hence information about ANS activity during AF may improve treatment strategy. Respiratory induced ANS variation in the f-waves of the ECG may provide such information.Objective: This paper proposes a novel approach for improved estimation of such respiratory induced variations and investigates the impact of deep breathing on the f-wave frequency in AF patients.Methods: A harmonic model is fitted to the f-wave signal to estimate a high-resolution f-wave frequency trend, and an orthogonal subspace projection approach is employed to quantify variations in the frequency trend that are linearly related to respiration using an ECG-derived respiration signal. The performance of the proposed approach is evaluated and compared to that of a previously proposed bandpass filtering approach using simulated f-wave signals. Further, the proposed approach is applied to analyze ECG data recorded for 5 min during baseline and 1 min deep breathing from 28 AF patients from the Swedish cardiopulmonary bioimage study (SCAPIS).Results: The simulation results show that the estimates of respiratory variations obtained using the proposed approach are more accurate than estimates obtained using the previous approach. Results from the analysis of SCAPIS data show no significant differences between baseline and deep breathing in heart rate (75.5 ± 22.9 vs. 74 ± 22.3) bpm, atrial fibrillation rate (6.93 ± 1.18 vs. 6.94 ± 0.66) Hz and respiratory f-wave frequency variations (0.130 ± 0.042 vs. 0.130 ± 0.034) Hz. However, individual variations are large with changes in heart rate and atrial fibrillatory rate in response to deep breathing ranging from −9% to +5% and −8% to +6%, respectively and there is a weak correlation between changes in heart rate and changes in atrial fibrillatory rate (r = 0.38, p < 0.03).Conclusion: Respiratory induced f-wave frequency variations were observed at baseline and during deep breathing. No significant changes in the magnitude of these variations in response to deep breathing was observed in the present study population.
Collapse
Affiliation(s)
- Mostafa Abdollahpur
- Department of Biomedical Engineering, Lund University, Lund, Sweden
- *Correspondence: Mostafa Abdollahpur,
| | - Gunnar Engström
- Department of Clinical Sciences, Cardiovascular Research—Epidemiology, Malmö, Sweden
| | - Pyotr G. Platonov
- Department of Cardiology, Clinical Sciences, Lund University, Lund, Sweden
| | - Frida Sandberg
- Department of Biomedical Engineering, Lund University, Lund, Sweden
| |
Collapse
|
9
|
Soliman MM, Ganti VG, Inan OT. Towards Wearable Estimation of Tidal Volume via Electrocardiogram and Seismocardiogram Signals. IEEE SENSORS JOURNAL 2022; 22:18093-18103. [PMID: 37091042 PMCID: PMC10120872 DOI: 10.1109/jsen.2022.3196601] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
The current COVID-19 pandemic highlights the critical importance of ubiquitous respiratory health monitoring. The two fundamental elements of monitoring respiration are respiration rate (the frequency of breathing) and tidal volume (TV, the volume of air breathed by the lungs in each breath). Wearable sensing systems have been demonstrated to provide accurate measurement of respiration rate, but TV remains challenging to measure accurately with wearable and unobtrusive technology. In this work, we leveraged electrocardiogram (ECG) and seismocardiogram (SCG) measurements obtained with a custom wearable sensing patch to derive an estimate of TV from healthy human participants. Specifically, we fused both ECG-derived and SCG-derived respiratory signals (EDR and SDR) and trained a machine learning model with gas rebreathing as the ground truth to estimate TV. The respiration cycle modulates ECG and SCG signals in multiple different ways that are synergistic. Thus, here we extract EDRs and SDRs using a multitude of different demodulation techniques. The extracted features are used to train a subject independent machine learning model to accurately estimate TV. By fusing the extracted EDRs and SDRs, we were able to estimate the TV with a root-mean-square error (RMSE) of 181.45 mL and Pearson correlation coefficient (r) of 0.61, with a global subject-independent model. We further show that SDRs are better TV estimators than EDRs. Among SDRs, amplitude modulated (AM) SCG features are the most correlated to TV. We demonstrated that fusing EDRs and SDRs can result in moderately accurate estimation of TV using a subject-independent model. Additionally, we highlight the most informative features for estimating TV. This work presents a significant step towards achieving continuous, calibration free, and unobtrusive TV estimation, which could advance the state of the art in wearable respiratory monitoring.
Collapse
Affiliation(s)
- Moamen M Soliman
- School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| | - Venu G Ganti
- Bioengineering Graduate Program, Georgia Institute of Technology, Atlanta, GA 30332
| | - Omer T Inan
- School of Electrical and Computer Engineering and, by courtesy, the Wallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology, Atlanta, GA 30332
| |
Collapse
|
10
|
Chan M, Ganti VG, Inan OT. Respiratory Rate Estimation Using U-Net-Based Cascaded Framework From Electrocardiogram and Seismocardiogram Signals. IEEE J Biomed Health Inform 2022; 26:2481-2492. [PMID: 35077375 PMCID: PMC9248781 DOI: 10.1109/jbhi.2022.3144990] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/14/2023]
Abstract
OBJECTIVE At-home monitoring of respiration is of critical urgency especially in the era of the global pandemic due to COVID-19. Electrocardiogram (ECG) and seismocardiogram (SCG) signals-measured in less cumbersome contact form factors than the conventional sealed mask that measures respiratory air flow-are promising solutions for respiratory monitoring. In particular, respiratory rates (RR) can be estimated from ECG-derived respiratory (EDR) and SCG-derived respiratory (SDR) signals. Yet, non-respiratory artifacts might still be present in these surrogates of respiratory signals, hindering the accuracy of the RRs estimated. METHODS In this paper, we propose a novel U-Net-based cascaded framework to address this problem. The EDR and SDR signals were transformed to the spectro-temporal domain and subsequently denoised by a 2D U-Net to reduce the non-respiratory artifacts. MAJOR RESULTS We have shown that the U-Net that fused an EDR input and an SDR input achieved a low mean absolute error of 0.82 breaths per minute (bpm) and a coefficient of determination (R2) of 0.89 using data collected from our chest-worn wearable patch. We also qualitatively provided insights on the complementariness between EDR and SDR signals and demonstrated the generalizability of the proposed framework. CONCLUSION ECG and SCG collected from a chest-worn wearable patch can complement each other and yield reliable RR estimation using the proposed cascaded framework. SIGNIFICANCE We anticipate that convenient and comfortable ECG and SCG measurement systems can be augmented with this framework to facilitate pervasive and accurate RR measurement.
Collapse
|
11
|
Hernando A, Posada-Quintero H, Peláez-Coca MD, Gil E, Chon KH. Autonomic Nervous System characterization in hyperbaric environments considering respiratory component and non-linear analysis of Heart Rate Variability. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 214:106527. [PMID: 34879328 DOI: 10.1016/j.cmpb.2021.106527] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Accepted: 11/05/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVES an evaluation of Principal Dynamic Mode (PDM) and Orthogonal Subspace Projection (OSP) methods to characterize the Autonomic Nervous System (ANS) response in three different hyperbaric environments was performed. METHODS ECG signals were recorded in two different stages (baseline and immersion) in three different hyperbaric environments: (a) inside a hyperbaric chamber, (b) in a controlled sea immersion, (c) in a real reservoir immersion. Time-domain parameters were extracted from the RR series of the ECG. From the Heart Rate Variability signal (HRV), classic Power Spectral Density (PSD), PDM (a non-linear analysis of HRV which is able to separate sympathetic and parasympathetic activities) and OSP (an analysis of HRV which is able to extract the respiratory component) methods were used to assess the ANS response. RESULTS PDM and OSP parameters follows the same trend when compared to the PSD ones for the hyperbaric chamber dataset. Comparing the three hyperbaric scenarios, significant differences were found: i) heart rate decreased and RMSSD increased in the hyperbaric chamber and the controlled dive, but they had the opposite behavior during the uncontrolled dive; ii) power in the OSP respiratory component was lower than power in the OSP residual component in cases a and c; iii) PDM and OSP methods showed a significant increase in sympathetic activity during both dives, but parasympathetic activity increased only during the uncontrolled dive. CONCLUSIONS PDM and OSP methods could be used as an alternative measurement of ANS response instead of the PSD method. OSP results indicate that most of the variation in the heart rate variability cannot be described by changes in the respiration, so changes in ANS response can be assigned to other factors. Time-domain parameters reflect vagal activation in the hyperbaric chamber and in the controlled dive because of the effect of pressure. In the uncontrolled dive, sympathetic activity seems to be dominant, due to the effects of other factors such as physical activity, the challenging environment, and the influence of breathing through the scuba mask during immersion. In sum, a careful description of the changes in all the possible factors that could affect the ANS response between baseline and immersion stages in hyperbaric environments is needed for better interpretation of the results.
Collapse
Affiliation(s)
- Alberto Hernando
- Centro Universitario de Defensa (CUD), Academia General Militar (AGM), Zaragoza, Spain; BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain.
| | | | - María Dolores Peláez-Coca
- Centro Universitario de Defensa (CUD), Academia General Militar (AGM), Zaragoza, Spain; BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Eduardo Gil
- Centro de Investigación Biomédica en Red Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain; BSICoS Group, Aragón Institute of Engineering Research (I3A), IIS Aragón, University of Zaragoza, Zaragoza, Spain
| | - Ki H Chon
- Department of Biomedical Engineering, University of Connecticut, Storrs CT, USA
| |
Collapse
|
12
|
Yeh CY, Chang HY, Hu JY, Lin CC. Contribution of Different Subbands of ECG in Sleep Apnea Detection Evaluated Using Filter Bank Decomposition and a Convolutional Neural Network. SENSORS (BASEL, SWITZERLAND) 2022; 22:510. [PMID: 35062470 PMCID: PMC8777653 DOI: 10.3390/s22020510] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Revised: 12/31/2021] [Accepted: 01/08/2022] [Indexed: 06/14/2023]
Abstract
A variety of feature extraction and classification approaches have been proposed using electrocardiogram (ECG) and ECG-derived signals for improving the performance of detecting apnea events and diagnosing patients with obstructive sleep apnea (OSA). The purpose of this study is to further evaluate whether the reduction of lower frequency P and T waves can increase the accuracy of the detection of apnea events. This study proposed filter bank decomposition to decompose the ECG signal into 15 subband signals, and a one-dimensional (1D) convolutional neural network (CNN) model independently cooperating with each subband to extract and classify the features of the given subband signal. One-minute ECG signals obtained from the MIT PhysioNet Apnea-ECG database were used to train the CNN models and test the accuracy of detecting apnea events for different subbands. The results show that the use of the newly selected subject-independent datasets can avoid the overestimation of the accuracy of the apnea event detection and can test the difference in the accuracy of different subbands. The frequency band of 31.25-37.5 Hz can achieve 100% per-recording accuracy with 85.8% per-minute accuracy using the newly selected subject-independent datasets and is recommended as a promising subband of ECG signals that can cooperate with the proposed 1D CNN model for the diagnosis of OSA.
Collapse
Affiliation(s)
- Cheng-Yu Yeh
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan; (C.-Y.Y.); (J.-Y.H.)
| | - Hung-Yu Chang
- Heart Center, Cheng Hsin General Hospital, Taipei 112, Taiwan;
- Faculty of Medicine, School of Medicine, National Yang Ming Chiao Tung University, Taipei 112, Taiwan
| | - Jiy-Yao Hu
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan; (C.-Y.Y.); (J.-Y.H.)
| | - Chun-Cheng Lin
- Department of Electrical Engineering, National Chin-Yi University of Technology, Taichung 411, Taiwan; (C.-Y.Y.); (J.-Y.H.)
| |
Collapse
|
13
|
Bawua LK, Miaskowski C, Hu X, Rodway GW, Pelter MM. A review of the literature on the accuracy, strengths, and limitations of visual, thoracic impedance, and electrocardiographic methods used to measure respiratory rate in hospitalized patients. Ann Noninvasive Electrocardiol 2021; 26:e12885. [PMID: 34405488 PMCID: PMC8411767 DOI: 10.1111/anec.12885] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/28/2021] [Revised: 06/14/2021] [Accepted: 07/11/2021] [Indexed: 11/27/2022] Open
Abstract
Background Respiratory rate (RR) is one of the most important indicators of a patient's health. In critically ill patients, unrecognized changes in RR are associated with poorer outcomes. Visual assessment (VA), impedance pneumography (IP), and electrocardiographic‐derived respiration (EDR) are the three most commonly used methods to assess RR. While VA and IP are widely used in hospitals, the EDR method has not been validated for use in hospitalized patients. Additionally, little is known about their accuracy compared with one another. The purpose of this systematic review was to compare the accuracy, strengths, and limitations of VA of RR to two methods that use physiologic data, namely IP and EDR. Methods A systematic review of the literature was undertaken using prespecified inclusion and exclusion criteria. Each of the studies was evaluated using standardized criteria. Results Full manuscripts for 23 studies were reviewed, and four studies were included in this review. Three studies compared VA to IP and one study compared VA to EDR. In terms of accuracy, when Bland–Altman analyses were performed, the upper and lower levels of agreement were extremely poor for both the VA and IP and VA and EDR comparisons. Conclusion Given the paucity of research and the fact that no studies have compared all three methods, no definitive conclusions can be drawn about the accuracy of these three methods. The clinical importance of accurate assessment of RR warrants new research with rigorous designs to determine the accuracy, and clinically meaningful levels of agreement of these methods.
Collapse
Affiliation(s)
- Linda K Bawua
- School of Nursing, University of California, San Francisco, California, USA
| | | | - Xiao Hu
- School of Nursing, Duke University, Durham, North Carolina, USA
| | | | - Michele M Pelter
- School of Nursing, University of California, San Francisco, California, USA
| |
Collapse
|
14
|
Deviaene M, Castro ID, Borzée P, Patel A, Torfs T, Buyse B, Testelmans D, Van Huffel S, Varon C. Capacitively-coupled ECG and respiration for the unobtrusive detection of sleep apnea. Physiol Meas 2021; 42:024001. [PMID: 33482650 DOI: 10.1088/1361-6579/abdf3d] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
OBJECTIVE The performance of a novel unobtrusive system based on capacitively-coupled electrocardiography (ccECG) combined with different respiratory measurements is evaluated for the detection of sleep apnea. APPROACH A sleep apnea detection algorithm is proposed, which can be applied to electrocardiography (ECG) and ccECG, combined with different unobtrusive respiratory measurements, including ECG derived respiration (EDR), respiratory effort measured using the thoracic belt (TB) and capacitively-coupled bioimpedance (ccBioz). Several ECG, respiratory and cardiorespiratory features were defined, of which the most relevant ones were identified using a random forest based backwards wrapper. Using this relevant feature set, a least-squares support vector machine classifier was trained to decide if a one minute segment is apneic or not, based on the annotated polysomnography (PSG) data of 218 patients suspected of having sleep apnea. The obtained classifier was then tested on the PSG and capacitively-coupled data of 28 different patients. MAIN RESULTS On the PSG data, an AUC of 76.3% was obtained when the ECG was combined with the EDR. Replacing the EDR with the TB led to an AUC of 80.0%. Using the ccECG and ccBioz or the ccECG and TB resulted in similar performances as on the PSG data, while using the ccECG and ccECG-based EDR resulted in a drop in AUC to 67.4%. SIGNIFICANCE This is the first study which tests an apnea detection algorithm on capacitively-coupled ECG and bioimpedance signals and shows promising results on the capacitively-coupled data set. However, it was shown that the EDR could not be accurately estimated from the ccECG signals. Further research into the effect that respiration has on the ccECG is needed to propose alternative EDR estimates.
Collapse
Affiliation(s)
- Margot Deviaene
- Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven B-3001, Belgium. Leuven. AI - KU Leuven institute for AI, B-3000, Leuven, Belgium
| | | | | | | | | | | | | | | | | |
Collapse
|
15
|
Scebba G, Da Poian G, Karlen W. Multispectral Video Fusion for Non-Contact Monitoring of Respiratory Rate and Apnea. IEEE Trans Biomed Eng 2020; 68:350-359. [PMID: 32396069 DOI: 10.1109/tbme.2020.2993649] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
Continuous monitoring of respiratory activity is desirable in many clinical applications to detect respiratory events. Non-contact monitoring of respiration can be achieved with near- and far-infrared spectrum cameras. However, current technologies are not sufficiently robust to be used in clinical applications. For example, they fail to estimate an accurate respiratory rate (RR) during apnea. We present a novel algorithm based on multispectral data fusion that aims at estimating RR also during apnea. The algorithm independently addresses the RR estimation and apnea detection tasks. Respiratory information is extracted from multiple sources and fed into an RR estimator and an apnea detector whose results are fused into a final respiratory activity estimation. We evaluated the system retrospectively using data from 30 healthy adults who performed diverse controlled breathing tasks while lying supine in a dark room and reproduced central and obstructive apneic events. Combining multiple respiratory information from multispectral cameras improved the root mean square error (RMSE) accuracy of the RR estimation from up to 4.64 monospectral data down to 1.60 breaths/min. The median F1 scores for classifying obstructive (0.75 to 0.86) and central apnea (0.75 to 0.93) also improved. Furthermore, the independent consideration of apnea detection led to a more robust system (RMSE of 4.44 vs. 7.96 breaths/min). Our findings may represent a step towards the use of cameras for vital sign monitoring in medical applications.
Collapse
|
16
|
Lazaro J, Reljin N, Bailon R, Gil E, Noh Y, Laguna P, Chon KH. Electrocardiogram Derived Respiratory Rate Using a Wearable Armband. IEEE Trans Biomed Eng 2020; 68:1056-1065. [PMID: 32746038 DOI: 10.1109/tbme.2020.3004730] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A method for deriving respiratory rate from an armband, which records three-channel electrocardiogram (ECG) using three pairs of dry (no hydrogel) electrodes, is presented. The armband device is especially convenient for long-term (months-years) monitoring because it does not use obstructive leads nor hydrogels/adhesives, which cause skin irritation even after few days. An ECG-derived respiration (EDR) based on respiration-related modulation of QRS slopes and R-wave angle approach was used. Moreover, we modified the EDR algorithm to lower the computational cost. Respiratory rates were estimated with the armband-ECG and the reference plethysmography-based respiration signals from 15 subjects who underwent breathing experiment consisting of five stages of controlled breathing (at 0.1, 0.2, 0.3, 0.4, and 0.5 Hz) and one stage of spontaneous breathing. The respiratory rates from the armband obtained a relative error with respect to the reference (respiratory rate estimated from the plethysmography-based respiration signal) that was not higher than 2.26% in median nor interquartile range (IQR) for all stages of fixed and spontaneous breathing, and not higher than 3.57% in median nor IQR in the case when the low computational cost algorithm was applied. These results demonstrate that respiration-related modulation of the ECG morphology are also present in the armband ECG device. Furthermore, these results suggest that respiration-related modulation can be exploited by the EDR method based on QRS slopes and R-wave angles to obtain respiratory rate, which may have a wide range of applications including monitoring patients with chronic respiratory diseases, epileptic seizures detection, stress assessment, and sleep studies, among others.
Collapse
|
17
|
Wang L, Zhang F, Lu K, Abdulaziz M, Li C, Zhang C, Chen J, Li Y. Nano-copper enhanced flexible device for simultaneous measurement of human respiratory and electro-cardiac activities. J Nanobiotechnology 2020; 18:82. [PMID: 32471516 PMCID: PMC7257177 DOI: 10.1186/s12951-020-00632-3] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2020] [Accepted: 05/12/2020] [Indexed: 02/02/2023] Open
Abstract
BACKGROUND Dysfunction of human respiratory and electro-cardiac activities could affect the ability of the heart to pump blood and the lungs to inhale oxygen. Thus, a device could simultaneously measure electro-cardiac signal and respiratory pressure could provide vital signs for predicting early warning of cardio-pulmonary function-related chronic diseases such as cardiovascular disease, and respiratory system disease. RESULTS In this study, a flexible device integrated with piezo-resistive sensing element and voltage-sensing element was developed to simultaneously measure human respiration and electro-cardiac signal (including respiratory pressure, respiration frequency, and respiration rhythm; electro-cardio frequency, electro-cardio amplitude, and electro-cardio rhythm). When applied to the measurement of respiratory pressure, the piezo-resistive performance of the device was enhanced by nano-copper modification, which detection limitation of pressure can reduce to 100 Pa and the sensitivity of pressure can achieve to 0.053 ± 0.00079 kPa-1. In addition, the signal-to-noise ratio during bio-electrical measurement was increased to 10.7 ± 1.4, five times better than that of the non-modified device. CONCLUSION This paper presents a flexible device for the simultaneous detection of human respiration and cardiac electrical activity. To avoid interference between the two signals, the layout of the electrode and the strain sensor was optimized by FEA simulation analysis. To improve the piezo-resistive sensitivity and bio-electric capturing capability of the device, a feather-shaped nano-copper was modified onto the surface of carbon fiber. The operation simplicity, compact size, and portability of the device open up new possibilities for multi-parameter monitoring.
Collapse
Affiliation(s)
- Li Wang
- Advanced Micro and Nanoinstruments Center (AMNC), School of Mechanical & Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
| | - Feng Zhang
- Advanced Micro and Nanoinstruments Center (AMNC), School of Mechanical & Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
| | - Kechao Lu
- Advanced Micro and Nanoinstruments Center (AMNC), School of Mechanical & Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China
| | - Mohammed Abdulaziz
- Department of Mechanical and Process Engineering, University of Duisburg Essen, Forsthausweg, 247057, Germany
| | - Chao Li
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China
| | - Chongyu Zhang
- The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, 510260, China
| | - Jun Chen
- Advanced Micro and Nanoinstruments Center (AMNC), School of Mechanical & Automotive Engineering, Qilu University of Technology (Shandong Academy of Sciences), Jinan, 250353, China.
| | - Yunlun Li
- Experimental Center, Shandong University of Traditional Chinese Medicine, Jinan, 250355, China.
| |
Collapse
|
18
|
Kim JO, Lee D. Detection of Abnormal Respiration from Multiple-Input Respiratory Signals. SENSORS 2020; 20:s20102977. [PMID: 32456350 PMCID: PMC7285501 DOI: 10.3390/s20102977] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 05/22/2020] [Accepted: 05/22/2020] [Indexed: 11/16/2022]
Abstract
In this paper, we propose a novel approach for the detection of abnormal signals from multiple respiration signals. An ultrawide-band (UWB) radar was used to acquire respiration signals that represent a distance from the chest to the radar sensor, i.e., shape variation of the chest due to breathing (inhaling or exhaling) activity provides quantitative information (distance values) about respiratory status. Distribution, shape, and variation of values across time provide information to determine respiratory status, one of the most important indicators of human health. In this paper, respiratory status was categorized into two classes, normal and abnormal. Abnormal respiration (apnea in this paper) was emulated by interrupting breathing activity because it is difficult to acquire real apnea from patients in hospital wards. This paper considered two cases, single and multiple respiration. In the first case, a single normal- or abnormal-respiration signal was used as input, and output was the classified status of respiration. In the second case, multiple respiration signals were simultaneously used as inputs, and we focused on determining the existence of abnormal signals in multiple respiration signals. In the case of multiple inputs, filters with varying cut-off frequency were applied to input signals followed by the analysis of output signals in response to the filters. To substantiate the proposed method, experiment results are provided. In this paper, classification results showed 93% of the successful rate in the case of multiple inputs, and results are promising for applications to monitoring systems of human respiration.
Collapse
|
19
|
Varon C, Morales J, Lázaro J, Orini M, Deviaene M, Kontaxis S, Testelmans D, Buyse B, Borzée P, Sörnmo L, Laguna P, Gil E, Bailón R. A Comparative Study of ECG-derived Respiration in Ambulatory Monitoring using the Single-lead ECG. Sci Rep 2020; 10:5704. [PMID: 32235865 PMCID: PMC7109157 DOI: 10.1038/s41598-020-62624-5] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2019] [Accepted: 03/14/2020] [Indexed: 11/08/2022] Open
Abstract
Cardiorespiratory monitoring is crucial for the diagnosis and management of multiple conditions such as stress and sleep disorders. Therefore, the development of ambulatory systems providing continuous, comfortable, and inexpensive means for monitoring represents an important research topic. Several techniques have been proposed in the literature to derive respiratory information from the ECG signal. Ten methods to compute single-lead ECG-derived respiration (EDR) were compared under multiple conditions, including different recording systems, baseline wander, normal and abnormal breathing patterns, changes in breathing rate, noise, and artifacts. Respiratory rates, wave morphology, and cardiorespiratory information were derived from the ECG and compared to those extracted from a reference respiratory signal. Three datasets were considered for analysis, involving a total 59 482 one-min, single-lead ECG segments recorded from 156 subjects. The results indicate that the methods based on QRS slopes outperform the other methods. This result is particularly interesting since simplicity is crucial for the development of ECG-based ambulatory systems.
Collapse
Affiliation(s)
- Carolina Varon
- Delft University of Technology, Circuits and Systems (CAS) group, Delft, 2600 AA, the Netherlands.
- KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, 3001, Belgium.
| | - John Morales
- KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, 3001, Belgium
| | - Jesús Lázaro
- University of Connecticut, Department of Electrical Engineering, Storrs, CT, 06268, USA
- University of Zaragoza, BSICoS Group, Aragón Institute of Engineering Research (I3A), IISAragon, Zaragoza, 50015, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Michele Orini
- University College London, Institute of Cardiovascular Science, London, WC1E 6BT, UK
- University College London, Barts Heart centre at St Bartholomews Hospital, London, EC1A 7BE, UK
| | - Margot Deviaene
- KU Leuven, Department of Electrical Engineering-ESAT, STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, Leuven, 3001, Belgium
| | - Spyridon Kontaxis
- University of Zaragoza, BSICoS Group, Aragón Institute of Engineering Research (I3A), IISAragon, Zaragoza, 50015, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | | | - Bertien Buyse
- UZ Leuven, Department of Pneumology, Leuven, 3001, Belgium
| | - Pascal Borzée
- UZ Leuven, Department of Pneumology, Leuven, 3001, Belgium
| | - Leif Sörnmo
- Lund University, Department of Biomedical Engineering, Lund, 118, 221 00, Sweden
| | - Pablo Laguna
- University of Zaragoza, BSICoS Group, Aragón Institute of Engineering Research (I3A), IISAragon, Zaragoza, 50015, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Eduardo Gil
- University of Zaragoza, BSICoS Group, Aragón Institute of Engineering Research (I3A), IISAragon, Zaragoza, 50015, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| | - Raquel Bailón
- University of Zaragoza, BSICoS Group, Aragón Institute of Engineering Research (I3A), IISAragon, Zaragoza, 50015, Spain
- CIBER de Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Madrid, Spain
| |
Collapse
|
20
|
Milagro J, Gracia-Tabuenca J, Seppa VP, Karjalainen J, Paassilta M, Orini M, Bailon R, Gil E, Viik J. Noninvasive Cardiorespiratory Signals Analysis for Asthma Evolution Monitoring in Preschool Children. IEEE Trans Biomed Eng 2019; 67:1863-1871. [PMID: 31670660 DOI: 10.1109/tbme.2019.2949873] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Despite its increasing prevalence, diagnosis of asthma in children remains problematic due to their difficulties in producing repeatable spirometric maneuvers. Moreover, low adherence to inhaled corticosteroids (ICS) treatment could result in permanent airway remodeling. The growing interest in a noninvasive and objective way for monitoring asthma, together with the apparent role of autonomic nervous system (ANS) in its pathogenesis, have attracted interest towards heart rate variability (HRV) and cardiorespiratory coupling (CRC) analyses. METHODS HRV and CRC were analyzed in 68 children who were prescribed ICS treatment due to recurrent obstructive bronchitis. They underwent three different electrocardiogram and respiratory signals recordings, during and after treatment period. After treatment completion, they were followed up during 6 months and classified attending to their current asthma status. RESULTS Vagal activity, as measured from HRV, and CRC, were reduced after treatment in those children at lower risk of asthma, whereas it kept unchanged in those with a worse prognosis. CONCLUSION Results suggest that HRV analysis could be useful for the continuous monitoring of ANS anomalies present in asthma, thus contributing to evaluate the evolution of the disease, which is especially challenging in young children. SIGNIFICANCE Noninvasive ANS assessment using HRV analysis could be useful in the continuous monitoring of asthma in children.
Collapse
|
21
|
Kontaxis S, Lazaro J, Corino VDA, Sandberg F, Bailon R, Laguna P, Sornmo L. ECG-Derived Respiratory Rate in Atrial Fibrillation. IEEE Trans Biomed Eng 2019; 67:905-914. [PMID: 31226064 DOI: 10.1109/tbme.2019.2923587] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Abstract
OBJECTIVE The present study addresses the problem of estimating the respiratory rate from the morphological ECG variations in the presence of atrial fibrillatory waves (f-waves). The significance of performing f-wave suppression before respiratory rate estimation is investigated. METHODS The performance of a novel approach to ECG-derived respiration, named "slope range" (SR) and designed particularly for operation in atrial fibrillation (AF), is compared to that of two well-known methods based on either R-wave angle (RA) or QRS loop rotation angle (LA). A novel rule is proposed for spectral peak selection in respiratory rate estimation. The suppression of f-waves is accomplished using signal- and noise-dependent QRS weighted averaging. The performance evaluation embraces real as well as simulated ECG signals acquired from patients with persistent AF; the estimation error of the respiratory rate is determined for both types of signals. RESULTS Using real ECG signals and reference respiratory signals, rate estimation without f-wave suppression resulted in a median error of 0.015 ± 0.021 Hz and 0.019 ± 0.025 Hz for SR and RA, respectively, whereas LA with f-wave suppression resulted in 0.034 ± 0.039 Hz. Using simulated signals, the results also demonstrate that f-wave suppression is superfluous for SR and RA, whereas it is essential for LA. CONCLUSION The results show that SR offers the best performance as well as computational simplicity since f-wave suppression is not needed. SIGNIFICANCE The respiratory rate can be robustly estimated from the ECG in the presence of AF.
Collapse
|
22
|
Milagro J, Hernando D, Lazaro J, Casajus JA, Garatachea N, Gil E, Bailon R. Electrocardiogram-Derived Tidal Volume During Treadmill Stress Test. IEEE Trans Biomed Eng 2019; 67:193-202. [PMID: 30990416 DOI: 10.1109/tbme.2019.2911351] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
OBJECTIVE Electrocardiogram (ECG) has been regarded as a source of respiratory information with the main focus in the estimation of the respiratory rate. Although little research concerning the estimation of tidal volume (TV) has been conducted, there are several ECG-derived features that have been related with TV in the literature, such as ECG-derived respiration, heart rate variability, and respiratory rate. In this paper, we exploited these features for estimating TV using a linear model. METHODS 25 young (33.4 ± 5.2 years) healthy male volunteers were recruited for performing a maximal (MaxT) and a submaximal (SubT) treadmill stress test, which were conducted on different days. Both tests were automatically segmented in stages attending to the heart rate. Afterwards, a subject-specific TV model was calibrated for each stage, employing features from MaxT, and the model was later used for estimating the TV in SubT. RESULTS During exercise, the different proposed approaches led to relative fitting errors lower than 14% in most of the cases and 6% in some of them. CONCLUSION Low achieved fitting errors suggest that TV can be estimated from ECG during a treadmill stress test. SIGNIFICANCE The results suggest that it is possible to estimate TV during exercise using only ECG-derived features.
Collapse
|
23
|
Sandberg F, Holmer M, Olde B. Monitoring respiration using the pressure sensors in a dialysis machine. Physiol Meas 2019; 40:025001. [DOI: 10.1088/1361-6579/aaf978] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
24
|
Varon C, Lazaro J, Bolea J, Hernando A, Aguilo J, Gil E, Van Huffel S, Bailon R. Unconstrained Estimation of HRV Indices After Removing Respiratory Influences From Heart Rate. IEEE J Biomed Health Inform 2018; 23:2386-2397. [PMID: 30507541 DOI: 10.1109/jbhi.2018.2884644] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
OBJECTIVE This paper proposes an approach to better estimate the sympathovagal balance (SB) and the respiratory sinus arrhythmia (RSA) after separating respiratory influences from the heart rate (HR). METHODS The separation is performed using orthogonal subspace projections and the approach is first tested using simulated HR and respiratory signals with different spectral properties. Then, RSA and SB are estimated during autonomic blockade and stress using the proposed approach and the classical heart rate variability (HRV) analysis. Both real- and ECG-derived respiration (EDR) are used and the reliability of the EDR is evaluated. RESULTS Mean absolute percentage errors lower than [Formula: see text] were obtained after removing previously known respiratory signals from simulated HR. The proposed indices were able to improve the quantification of SB during autonomic withdrawal. In the stress data, differences ( ) among relaxed and stressful phases were found with the proposed approach, using both the real respiration and the EDR, but they disappeared when using the classical HRV. CONCLUSION A better assessment of the autonomic nervous system' response to pharmacological blockade and stress can be achieved after removing respiratory influences from HR, and this can be done using either the real respiration or the EDR. SIGNIFICANCE This work can be used to better identify vagal withdrawal and increased sympathetic activation when the classical HRV analysis fails due to the respiratory influences on HR. Furthermore, it can be computed using only the ECG, which is an advantage when developing wearable systems with limited number of sensors.
Collapse
|
25
|
Pelaez MDC, Albalate MTL, Sanz AH, Valles MA, Gil E. Photoplethysmographic Waveform Versus Heart Rate Variability to Identify Low-Stress States: Attention Test. IEEE J Biomed Health Inform 2018; 23:1940-1951. [PMID: 30452382 DOI: 10.1109/jbhi.2018.2882142] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
Our long-term goal is the development of an automatic identifier of attentional states. In order to accomplish it, we should first be able to identify different states based on physiological signals. So, the first aim of this paper is to identify the most appropriate features to detect a subject's high performance state. For that, a database of electrocardiographic (ECG) and photoplethysmographic (PPG) signals is recorded in two unequivocally defined states (rest and attention task) from up to 50 subjects as a sample of the population. Time and frequency parameters of heart/pulse rate variability have been computed from the ECG/PPG signals, respectively. Additionally, the respiratory rate has been estimated from both signals and also six morphological parameters from PPG. In total, 26 features are obtained for each subject. They provide information about the autonomic nervous system and the physiological response of the subject to an attention demand task. Results show an increase of sympathetic activation when the subjects perform the attention test. The amplitude and width of the PPG pulse were more sensitive than the classical sympathetic markers ([Formula: see text] and [Formula: see text]) for identifying this attentional state. State classification accuracy reaches a mean of [Formula: see text], a maximum of [Formula: see text], and a minimum of 85%, in the 100 classifications made by only selecting four parameters extracted from the PPG signal (pulse amplitude, pulsewidth, pulse downward slope, and mean pulse rate). These results suggest that attentional states could be identified by PPG.
Collapse
|
26
|
Janbakhshi P, Shamsollahi MB. ECG-derived respiration estimation from single-lead ECG using gaussian process and phase space reconstruction methods. Biomed Signal Process Control 2018. [DOI: 10.1016/j.bspc.2018.05.025] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
|
27
|
Hernando A, Pelaez-Coca MD, Lozano MT, Aiger M, Izquierdo D, Sanchez A, Lopez-Jurado MI, Moura I, Fidalgo J, Lazaro J, Gil E. Autonomic Nervous System Measurement in Hyperbaric Environments Using ECG and PPG Signals. IEEE J Biomed Health Inform 2018; 23:132-142. [PMID: 29994358 DOI: 10.1109/jbhi.2018.2797982] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The main aim of this paper was to characterize the Autonomic Nervous System response in hyperbaric environments using electrocardiogram (ECG) and pulse-photoplethysmogram (PPG) signals. To that end, 26 subjects were introduced into a hyperbaric chamber and five stages with different atmospheric pressures (1 atm; descent to 3 and 5 atm; ascent to 3 and 1 atm) were recorded. Respiratory information was extracted from the ECG and PPG signals and a combined respiratory rate was studied. This information was also used to analyze Heart Rate Variability (HRV) and Pulse Rate Variability (PRV). The database was cleaned by eliminating those cases where the respiratory rate dropped into the low frequency band (LF: 0.04-0.15 Hz) and those in which there was a discrepancy between the respiratory rates estimated using the ECG and PPG signals. Classical temporal and frequency indices were calculated in such cases. The ECG results showed a time-related dependency, with the heart rate and sympathetic markers (normalized power in LF and LF/HF ratio) decreasing as more time was spent inside the hyperbaric environment. A dependence between the atmospheric pressure and the parasympathetic response, as reflected in the high-frequency band power (HF: 0.15-0.40 Hz), was also found, with power increasing with atmospheric pressure. The combined respiratory rate also reached a maximum in the deepest stage; thus, highlighting a significant difference between this stage and the first one. The PPG data gave similar findings and also allowed the oxygen saturation to be computed; therefore, we propose the use of this signal for future studies in hyperbaric environments.
Collapse
|
28
|
Sharma H, Sharma KK. ECG-derived respiration based on iterated Hilbert transform and Hilbert vibration decomposition. AUSTRALASIAN PHYSICAL & ENGINEERING SCIENCES IN MEDICINE 2018; 41:429-443. [PMID: 29667117 DOI: 10.1007/s13246-018-0640-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/16/2017] [Accepted: 04/11/2018] [Indexed: 11/26/2022]
Abstract
Monitoring of the respiration using the electrocardiogram (ECG) is desirable for the simultaneous study of cardiac activities and the respiration in the aspects of comfort, mobility, and cost of the healthcare system. This paper proposes a new approach for deriving the respiration from single-lead ECG based on the iterated Hilbert transform (IHT) and the Hilbert vibration decomposition (HVD). The ECG signal is first decomposed into the multicomponent sinusoidal signals using the IHT technique. Afterward, the lower order amplitude components obtained from the IHT are filtered using the HVD to extract the respiration information. Experiments are performed on the Fantasia and Apnea-ECG datasets. The performance of the proposed ECG-derived respiration (EDR) approach is compared with the existing techniques including the principal component analysis (PCA), R-peak amplitudes (RPA), respiratory sinus arrhythmia (RSA), slopes of the QRS complex, and R-wave angle. The proposed technique showed the higher median values of correlation (first and third quartile) for both the Fantasia and Apnea-ECG datasets as 0.699 (0.55, 0.82) and 0.57 (0.40, 0.73), respectively. Also, the proposed algorithm provided the lowest values of the mean absolute error and the average percentage error computed from the EDR and reference (recorded) respiration signals for both the Fantasia and Apnea-ECG datasets as 1.27 and 9.3%, and 1.35 and 10.2%, respectively. In the experiments performed over different age group subjects of the Fantasia dataset, the proposed algorithm provided effective results in the younger population but outperformed the existing techniques in the case of elderly subjects. The proposed EDR technique has the advantages over existing techniques in terms of the better agreement in the respiratory rates and specifically, it reduces the need for an extra step required for the detection of fiducial points in the ECG for the estimation of respiration which makes the process effective and less-complex. The above performance results obtained from two different datasets validate that the proposed approach can be used for monitoring of the respiration using single-lead ECG.
Collapse
Affiliation(s)
- Hemant Sharma
- Department of Electronics and Communication Engineering, National Institute of Technology Rourkela, Rourkela, India.
| | - K K Sharma
- Department of Electronics and Communication Engineering, Malaviya National Institute of Technology Jaipur, Jaipur, India
| |
Collapse
|
29
|
|
30
|
Milagro J, Gil E, Lazaro J, Seppa VP, Malmberg LP, Pelkonen AS, Kotaniemi-Syrjanen A, Makela MJ, Viik J, Bailon R. Nocturnal Heart Rate Variability Spectrum Characterization in Preschool Children With Asthmatic Symptoms. IEEE J Biomed Health Inform 2017; 22:1332-1340. [PMID: 29990113 DOI: 10.1109/jbhi.2017.2775059] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Asthma is a chronic lung disease that usually develops during childhood. Despite that symptoms can almost be controlled with medication, early diagnosis is desirable in order to reduce permanent airway obstruction risk. It has been suggested that abnormal parasympathetic nervous system (PSNS) activity might be closely related with the pathogenesis of asthma, and that this PSNS activity could be reflected in cardiac vagal control. In this work, an index to characterize the spectral distribution of the high frequency (HF) component of heart rate variability (HRV), named peakness ($\wp$), is proposed. Three different implementations of $\wp$, based on electrocardiogram (ECG) recordings, impedance pneumography (IP) recordings and a combination of both, were employed in the characterization of a group of preschool children classified attending to their risk of developing asthma. Peakier components were observed in the HF band of those children classified as high-risk ( $p < 0.005$), who also presented reduced sympathvoagal balance. Results suggest that high-risk of developing asthma might be related with a lack of adaptability of PSNS.
Collapse
|
31
|
Charlton PH, Birrenkott DA, Bonnici T, Pimentel MAF, Johnson AEW, Alastruey J, Tarassenko L, Watkinson PJ, Beale R, Clifton DA. Breathing Rate Estimation From the Electrocardiogram and Photoplethysmogram: A Review. IEEE Rev Biomed Eng 2017; 11:2-20. [PMID: 29990026 PMCID: PMC7612521 DOI: 10.1109/rbme.2017.2763681] [Citation(s) in RCA: 124] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Breathing rate (BR) is a key physiological parameter used in a range of clinical settings. Despite its diagnostic and prognostic value, it is still widely measured by counting breaths manually. A plethora of algorithms have been proposed to estimate BR from the electrocardiogram (ECG) and pulse oximetry (photoplethysmogram, PPG) signals. These BR algorithms provide opportunity for automated, electronic, and unobtrusive measurement of BR in both healthcare and fitness monitoring. This paper presents a review of the literature on BR estimation from the ECG and PPG. First, the structure of BR algorithms and the mathematical techniques used at each stage are described. Second, the experimental methodologies that have been used to assess the performance of BR algorithms are reviewed, and a methodological framework for the assessment of BR algorithms is presented. Third, we outline the most pressing directions for future research, including the steps required to use BR algorithms in wearable sensors, remote video monitoring, and clinical practice.
Collapse
Affiliation(s)
- Peter H. Charlton
- Department of Biomedical Engineering, King’s College London, London SE1 7EH, U.K., and also with the Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Drew A. Birrenkott
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Timothy Bonnici
- Nuffield Department of Medicine, University of Oxford, Oxford OX3 9DU, U.K., and also with the Department of Asthma, Allergy, and Lung Biology, King’s College London, London SE1 7EH, U.K
| | | | - Alistair E. W. Johnson
- Laboratory for Computational Physiology, Massachusetts Institute of Technology, Cambridge, MA 02139 USA
| | - Jordi Alastruey
- Department of Biomedical Engineering, King’s College London, London SE1 7EH, U.K
| | - Lionel Tarassenko
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| | - Peter J. Watkinson
- Kadoorie Centre for Critical Care Research and Education, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, U.K
| | - Richard Beale
- Department of Asthma, Allergy and Lung Biology, King’s College London, London SE1 7EH, U.K
| | - David A. Clifton
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, U.K
| |
Collapse
|
32
|
Sinharay A, Rakshit R, Khasnobish A, Chakravarty T, Ghosh D, Pal A. The Ultrasonic Directional Tidal Breathing Pattern Sensor: Equitable Design Realization Based on Phase Information. SENSORS (BASEL, SWITZERLAND) 2017; 17:E1853. [PMID: 28800103 PMCID: PMC5579868 DOI: 10.3390/s17081853] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2017] [Revised: 07/17/2017] [Accepted: 07/18/2017] [Indexed: 12/03/2022]
Abstract
Pulmonary ailments are conventionally diagnosed by spirometry. The complex forceful breathing maneuver as well as the extreme cost of spirometry renders it unsuitable in many situations. This work is aimed to facilitate an emerging direction of tidal breathing-based pulmonary evaluation by designing a novel, equitable, precise and portable device for acquisition and analysis of directional tidal breathing patterns, in real time. The proposed system primarily uses an in-house designed blow pipe, 40-kHz air-coupled ultrasound transreceivers, and a radio frequency (RF) phase-gain integrated circuit (IC). Moreover, in order to achieve high sensitivity in a cost-effective design philosophy, we have exploited the phase measurement technique, instead of selecting the contemporary time-of-flight (TOF) measurement; since application of the TOF principle in tidal breathing assessments requires sub-micro to nanosecond time resolution. This approach, which depends on accurate phase measurement, contributed to enhanced sensitivity using a simple electronics design. The developed system has been calibrated using a standard 3-L calibration syringe. The parameters of this system are validated against a standard spirometer, with maximum percentage error below 16%. Further, the extracted respiratory parameters related to tidal breathing have been found to be comparable with relevant prior works. The error in detecting respiration rate only is 3.9% compared to manual evaluation. These encouraging insights reveal the definite potential of our tidal breathing pattern (TBP) prototype for measuring tidal breathing parameters in order to extend the reach of affordable healthcare in rural regions and developing areas.
Collapse
Affiliation(s)
| | - Raj Rakshit
- TCS Research and Innovation, Kolkata 700156, India.
| | | | | | - Deb Ghosh
- TCS Research and Innovation, Kolkata 700156, India.
| | - Arpan Pal
- TCS Research and Innovation, Kolkata 700156, India.
| |
Collapse
|
33
|
Bolea J, Lázaro J, Gil E, Rovira E, Remartínez JM, Laguna P, Pueyo E, Navarro A, Bailón R. Pulse Rate and Transit Time Analysis to Predict Hypotension Events After Spinal Anesthesia During Programmed Cesarean Labor. Ann Biomed Eng 2017; 45:2253-2263. [DOI: 10.1007/s10439-017-1864-y] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2017] [Accepted: 05/30/2017] [Indexed: 12/11/2022]
|
34
|
Charlton PH, Bonnici T, Tarassenko L, Alastruey J, Clifton DA, Beale R, Watkinson PJ. Extraction of respiratory signals from the electrocardiogram and photoplethysmogram: technical and physiological determinants. Physiol Meas 2017; 38:669-690. [PMID: 28296645 DOI: 10.1088/1361-6579/aa670e] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
OBJECTIVE Breathing rate (BR) can be estimated by extracting respiratory signals from the electrocardiogram (ECG) or photoplethysmogram (PPG). The extracted respiratory signals may be influenced by several technical and physiological factors. In this study, our aim was to determine how technical and physiological factors influence the quality of respiratory signals. APPROACH Using a variety of techniques 15 respiratory signals were extracted from the ECG, and 11 from PPG signals collected from 57 healthy subjects. The quality of each respiratory signal was assessed by calculating its correlation with a reference oral-nasal pressure respiratory signal using Pearson's correlation coefficient. MAIN RESULTS Relevant results informing device design and clinical application were obtained. The results informing device design were: (i) seven out of 11 respiratory signals were of higher quality when extracted from finger PPG compared to ear PPG; (ii) laboratory equipment did not provide higher quality of respiratory signals than a clinical monitor; (iii) the ECG provided higher quality respiratory signals than the PPG; (iv) during downsampling of the ECG and PPG significant reductions in quality were first observed at sampling frequencies of <250 Hz and <16 Hz respectively. The results informing clinical application were: (i) frequency modulation-based respiratory signals were generally of lower quality in elderly subjects compared to young subjects; (ii) the qualities of 23 out of 26 respiratory signals were reduced at elevated BRs; (iii) there were no differences associated with gender. SIGNIFICANCE Recommendations based on the results are provided regarding device designs for BR estimation, and clinical applications. The dataset and code used in this study are publicly available.
Collapse
Affiliation(s)
- Peter H Charlton
- School of Medicine, King's College London, United Kingdom. Department of Engineering Science, Institute of Biomedical Engineering, University of Oxford, United Kingdom
| | | | | | | | | | | | | |
Collapse
|
35
|
Ma Y, Zheng Q, Liu Y, Shi B, Xue X, Ji W, Liu Z, Jin Y, Zou Y, An Z, Zhang W, Wang X, Jiang W, Xu Z, Wang ZL, Li Z, Zhang H. Self-Powered, One-Stop, and Multifunctional Implantable Triboelectric Active Sensor for Real-Time Biomedical Monitoring. NANO LETTERS 2016; 16:6042-6051. [PMID: 27607151 DOI: 10.1021/acs.nanolett.6b01968] [Citation(s) in RCA: 115] [Impact Index Per Article: 14.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/25/2023]
Abstract
Operation time of implantable electronic devices is largely constrained by the lifetime of batteries, which have to be replaced periodically by surgical procedures once exhausted, causing physical and mental suffering to patients and increasing healthcare costs. Besides the efficient scavenging of the mechanical energy of internal organs, this study proposes a self-powered, flexible, and one-stop implantable triboelectric active sensor (iTEAS) that can provide continuous monitoring of multiple physiological and pathological signs. As demonstrated in human-scale animals, the device can monitor heart rates, reaching an accuracy of ∼99%. Cardiac arrhythmias such as atrial fibrillation and ventricular premature contraction can be detected in real-time. Furthermore, a novel method of monitoring respiratory rates and phases is established by analyzing variations of the output peaks of the iTEAS. Blood pressure can be independently estimated and the velocity of blood flow calculated with the aid of a separate arterial pressure catheter. With the core-shell packaging strategy, monitoring functionality remains excellent during 72 h after closure of the chest. The in vivo biocompatibility of the device is examined after 2 weeks of implantation, proving suitability for practical use. As a multifunctional biomedical monitor that is exempt from needing an external power supply, the proposed iTEAS holds great potential in the future of the healthcare industry.
Collapse
Affiliation(s)
- Ye Ma
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Qiang Zheng
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
| | - Yang Liu
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Bojin Shi
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
| | - Xiang Xue
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Weiping Ji
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Zhuo Liu
- School of Biological Science and Medical Engineering, Beihang University , Beijing 100191, PR China
| | - Yiming Jin
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
| | - Yang Zou
- School of Biological Science and Medical Engineering, Beihang University , Beijing 100191, PR China
| | - Zhao An
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Wei Zhang
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Xinxin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
| | - Wen Jiang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
| | - Zhiyun Xu
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| | - Zhong Lin Wang
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
- School of Materials Science and Engineering, Georgia Institute of Technology , Atlanta, Georgia 30332, United States
| | - Zhou Li
- Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Science , Beijing 100083, PR China
| | - Hao Zhang
- Institute of Cardiothoracic Surgery at Changhai Hospital, Second Military Medical University , Shanghai 200433, PR China
| |
Collapse
|
36
|
Quintana DS, Alvares GA, Heathers JAJ. Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH): recommendations to advance research communication. Transl Psychiatry 2016; 6:e803. [PMID: 27163204 PMCID: PMC5070064 DOI: 10.1038/tp.2016.73] [Citation(s) in RCA: 222] [Impact Index Per Article: 27.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/19/2015] [Revised: 03/18/2016] [Accepted: 03/23/2016] [Indexed: 12/11/2022] Open
Abstract
The number of publications investigating heart rate variability (HRV) in psychiatry and the behavioral sciences has increased markedly in the last decade. In addition to the significant debates surrounding ideal methods to collect and interpret measures of HRV, standardized reporting of methodology in this field is lacking. Commonly cited recommendations were designed well before recent calls to improve research communication and reproducibility across disciplines. In an effort to standardize reporting, we propose the Guidelines for Reporting Articles on Psychiatry and Heart rate variability (GRAPH), a checklist with four domains: participant selection, interbeat interval collection, data preparation and HRV calculation. This paper provides an overview of these four domains and why their standardized reporting is necessary to suitably evaluate HRV research in psychiatry and related disciplines. Adherence to these communication guidelines will help expedite the translation of HRV research into a potential psychiatric biomarker by improving interpretation, reproducibility and future meta-analyses.
Collapse
Affiliation(s)
- D S Quintana
- Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, University of Oslo, Oslo University Hospital, Oslo, Norway,Division of Mental Health and Addiction, NORMENT, KG Jebsen Centre for Psychosis Research, University of Oslo, Oslo University Hospital, Building 49, Oslo University Hospital, Ullevål, Kirkeveien 166, PO Box 4956, Nydalen, Oslo N-0424, Norway. E-mail:
| | - G A Alvares
- Telethon Kids Institute, University of Western Australia, Perth, WA, Australia,Cooperative Research Centre for Living with Autism (Autism CRC), Brisbane, QLD, Australia
| | - J A J Heathers
- School of Psychology, University of Sydney, Sydney, NSW, Australia,Department of Cardiology and Intensive Therapy, Poznań University of Medical Sciences, Poznań, Poland
| |
Collapse
|
37
|
Hernando A, Lazaro J, Gil E, Arza A, Garzon JM, Lopez-Anton R, de la Camara C, Laguna P, Aguilo J, Bailon R. Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment. IEEE J Biomed Health Inform 2016; 20:1016-25. [PMID: 27093713 DOI: 10.1109/jbhi.2016.2553578] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Respiratory rate and heart rate variability (HRV) are studied as stress markers in a database of young healthy volunteers subjected to acute emotional stress, induced by a modification of the Trier Social Stress Test. First, instantaneous frequency domain HRV parameters are computed using time-frequency analysis in the classical bands. Then, the respiratory rate is estimated and this information is included in HRV analysis in two ways: 1) redefining the high-frequency (HF) band to be centered at respiratory frequency; 2) excluding from the analysis those instants where respiratory frequency falls within the low-frequency (LF) band. Classical frequency domain HRV indices scarcely show statistical differences during stress. However, when including respiratory frequency information in HRV analysis, the normalized LF power as well as the LF/HF ratio significantly increase during stress ( p-value 0.05 according to the Wilcoxon test), revealing higher sympathetic dominance. The LF power increases during stress, only being significantly different in a stress anticipation stage, while the HF power decreases during stress, only being significantly different during the stress task demanding attention. Our results support that joint analysis of respiration and HRV obtains a more reliable characterization of autonomic nervous response to stress. In addition, the respiratory rate is observed to be higher and less stable during stress than during relax ( p-value 0.05 according to the Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 % ).
Collapse
|
38
|
Lázaro J, Nam Y, Gil E, Laguna P, Chon KH. Respiratory rate derived from smartphone-camera-acquired pulse photoplethysmographic signals. Physiol Meas 2015; 36:2317-33. [DOI: 10.1088/0967-3334/36/11/2317] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
|
39
|
Kabir MM, Ghafoori E, Tereshchenko LG. Assessment of Joint Interactions between Respiration and Baroreflex Activity using Joint Symbolic Dynamics in Heart Failure Patients. COMPUTING IN CARDIOLOGY 2015; 42:809-812. [PMID: 26998500 PMCID: PMC4797656 DOI: 10.1109/cic.2015.7411034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
In this paper we employed a novel approach based on joint symbolic dynamics (JSD) to study interaction between respiratory phase and baroreflex activity. Electrocardiogram (ECG) and blood pressure recordings from six participants with history of heart failure were included in this study. First, the ECG R-peaks and systolic blood pressure indices were detected using parabolic fitting. Second, the respiratory signal was derived from Frank orthogonal ECG leads using QRS slopes and R-wave angles. Third, time series of R-R intervals and systolic blood pressure (SBP) were extracted, and respiratory phases were obtained using the Hilbert transform. Subsequently, each series was transformed into binary symbol vectors based on their successive changes and words of length '2' were formed. Baroreflex patterns were studied using word combinations representing baroreflex activity for specific changes in respiratory phases. Baroreflex activity was significantly higher for alternating low-high/high-low heart rate and SBP during inspiration as compared to continuous increase or decrease in heart rate and SBP ([Formula: see text], wRP=11: 39.1±9.3% vs. [Formula: see text], wRP=11: 6.4±3.9%, p<0.0001).
Collapse
Affiliation(s)
- Muammar M Kabir
- Oregon Health and Science University, Knight Cardiovascular Institute, Portland, OR, USA
| | - Elyar Ghafoori
- Oregon Health and Science University, Knight Cardiovascular Institute, Portland, OR, USA
| | - Larisa G Tereshchenko
- Oregon Health and Science University, Knight Cardiovascular Institute, Portland, OR, USA
| |
Collapse
|
40
|
A Novel Method for Screening Children with Isolated Bicuspid Aortic Valve. Cardiovasc Eng Technol 2015; 6:546-56. [PMID: 26577485 DOI: 10.1007/s13239-015-0238-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/23/2015] [Accepted: 07/15/2015] [Indexed: 10/23/2022]
Abstract
This paper presents a novel processing method for heart sound signal: the statistical time growing neural network (STGNN). The STGNN performs a robust classification by merging supervised and unsupervised statistical methods to overcome non-stationary behavior of the signal. By combining available preprocessing and segmentation techniques and the STGNN classifier, we build an automatic tool for screening children with isolated BAV, the congenital heart malformation which can lead to serious cardiovascular lesions. Children with BAV (22 individuals) and healthy condition (28 individuals) are subjected to the study. The performance of the STGNN is compared to that of a time growing neural network (CTGNN) and a conventional support vector (CSVM) machine, using balanced repeated random sub sampling. The average of the accuracy/sensitivity is estimated to be 87.4/86.5 for the STGNN, 81.8/83.4 for the CTGNN, and 72.9/66.8 for the CSVM. Results show that the STGNN offers better performance and provides more immunity to the background noise as compared to the CTGNN and CSVM. The method is implementable in a computer system to be employed in primary healthcare centers to improve the screening accuracy.
Collapse
|
41
|
Fekr AR, Radecka K, Zilic Z. Design and Evaluation of an Intelligent Remote Tidal Volume Variability Monitoring System in E-Health Applications. IEEE J Biomed Health Inform 2015; 19:1532-48. [PMID: 26087508 DOI: 10.1109/jbhi.2015.2445783] [Citation(s) in RCA: 38] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
A reliable long-term monitoring and diagnosis of breath disorders at an early stage provides an improvement of medical act, life expectancy, and quality of life while decreasing the costs of treatment and medical services. Therefore, a real-time unobtrusive monitoring of respiration patterns, as well as breath parameters, is a critical need in medical applications. In this paper, we propose an intelligent system for patient home care, capable of measuring respiration rate and tidal volume variability via a wearable sensing technology. The proposed system is designed particularly for the goal of diagnosis and treatment in patients with pathological breathing, e.g., respiratory complications after surgery or sleep disorders. The complete system was comprised of wearable calibrated accelerometer sensor, Bluetooth low energy, and cloud database. The experiments are conducted with eight subjects and the overall error in respiration rate calculation is obtained 0.29%±0.33% considering SPR-BTA spirometer as the reference. We also introduce a method for tidal volume variability estimation while validated using Pearson correlation. Furthermore, since it is essential to detect the critical events resulted from sudden rise or fall in per breath tidal volume of the patients, we provide a technique to automatically find the accurate threshold values based on each individual breath characteristics. Therefore, the system is able to detect the major changes, precisely by more than 98%, and provide immediate feedback such as sound alarm for round-the-clock respiration monitoring.
Collapse
|
42
|
Waks JW, Soliman EZ, Henrikson CA, Sotoodehnia N, Han L, Agarwal SK, Arking DE, Siscovick DS, Solomon SD, Post WS, Josephson ME, Coresh J, Tereshchenko LG. Beat-to-beat spatiotemporal variability in the T vector is associated with sudden cardiac death in participants without left ventricular hypertrophy: the Atherosclerosis Risk in Communities (ARIC) Study. J Am Heart Assoc 2015; 4:e001357. [PMID: 25600143 PMCID: PMC4330061 DOI: 10.1161/jaha.114.001357] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023]
Abstract
Background Despite advances in prevention and treatment of cardiovascular disease, sudden cardiac death (SCD) remains a clinical challenge. Risk stratification in the general population is needed. Methods and Results Beat‐to‐beat spatiotemporal variability in the T vector was measured as the mean angle between consecutive T‐wave vectors (mean TT′ angle) on standard 12‐lead ECGs in 14 024 participants in the Atherosclerosis Risk in Communities (ARIC) study. Subjects with left ventricular hypertrophy, atrial arrhythmias, frequent ectopy, ventricular pacing, or QRS duration ≥120 ms were excluded. The mean spatial TT′ angle was 5.21±3.55°. During a median of 14 years of follow‐up, 235 SCDs occurred (1.24 per 1000 person‐years). After adjustment for demographics, coronary heart disease risk factors, and known ECG markers for SCD, mean TT′ angle was independently associated with SCD (hazard ratio 1.089; 95% CI 1.044 to 1.137; P<0.0001). A mean TT′ angle >90th percentile (>9.57°) was associated with a 2‐fold increase in the hazard for SCD (hazard ratio 2.01; 95% CI 1.28 to 3.16; P=0.002). In a subgroup of patients with T‐vector amplitude ≥0.2 mV, the association with SCD was almost twice as strong (hazard ratio 3.92; 95% CI 1.91 to 8.05; P<0.0001). A significant interaction between mean TT′ angle and age was found: TT′ angle was associated with SCD in participants aged <55 years (hazard ratio 1.096; 95% CI 0.043 to 1.152; P<0.0001) but not in participants aged ≥55 years (Pinteraction=0.009). Conclusions In a large, prospective, community‐based cohort of left ventricular hypertrophy–free participants, increased beat‐to‐beat spatiotemporal variability in the T vector, as assessed by increasing TT′ angle, was associated with SCD.
Collapse
Affiliation(s)
- Jonathan W Waks
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.W.W., M.E.J.)
| | - Elsayed Z Soliman
- Epidemiological Cardiology Research Center (EPICARE), Division of Public Health Sciences and Department of Medicine, Cardiology Section, Wake Forest School of Medicine, Winston Salem, NC (E.Z.S.)
| | - Charles A Henrikson
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR (C.A.H., L.G.T.)
| | | | - Lichy Han
- Whitening School of Engineering, Johns Hopkins University, Baltimore, MD (L.H.)
| | - Sunil K Agarwal
- Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins School of Public Health, Baltimore, MD (S.K.A., J.C.)
| | - Dan E Arking
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (D.E.A.)
| | - David S Siscovick
- University of Washington, Seattle, WA (N.S., D.S.S.) The New York Academy of Medicine, New York, NY (D.S.S.)
| | - Scott D Solomon
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA (S.D.S.)
| | - Wendy S Post
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (W.S.P., L.G.T.)
| | - Mark E Josephson
- Division of Cardiovascular Medicine, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA (J.W.W., M.E.J.)
| | - Josef Coresh
- Department of Epidemiology and Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins School of Public Health, Baltimore, MD (S.K.A., J.C.)
| | - Larisa G Tereshchenko
- Knight Cardiovascular Institute, Oregon Health & Science University, Portland, OR (C.A.H., L.G.T.) Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD (W.S.P., L.G.T.)
| |
Collapse
|