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Velraeds A, Strik M, van der Zande J, Fontagne L, Haissaguerre M, Ploux S, Wang Y, Bordachar P. Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity. SENSORS (BASEL, SWITZERLAND) 2023; 23:9283. [PMID: 38005669 PMCID: PMC10674836 DOI: 10.3390/s23229283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/26/2023]
Abstract
Smartwatches equipped with automatic atrial fibrillation (AF) detection through electrocardiogram (ECG) recording are increasingly prevalent. We have recently reported the limitations of the Apple Watch (AW) in correctly diagnosing AF. In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. We included 723 patients (579 patients for algorithm development and 144 patients for validation) who underwent ECG recording with an AW and a 12-lead ECG (21% had AF and 24% had no ECG abnormalities). Similar to the existing algorithm, we first screened for AF by detecting irregularities in ventricular intervals. However, as opposed to the existing algorithm, we included all ECGs (not applying quality or heart rate exclusion criteria) but we excluded ECGs in which we identified regular patterns within the irregular rhythms by screening for interval clusters. This "irregularly irregular" approach resulted in a significant improvement in accuracy compared to the existing AW algorithm (sensitivity of 90% versus 83%, specificity of 92% versus 79%, p < 0.01). Identifying regularity within irregular rhythms is an accurate yet inclusive method to detect AF using a smartwatch ECG.
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Affiliation(s)
- Anouk Velraeds
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
- Biomedical Signals and Systems, TechMed Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Marc Strik
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Joske van der Zande
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
- Biomedical Signals and Systems, TechMed Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Leslie Fontagne
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Michel Haissaguerre
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Sylvain Ploux
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
| | - Ying Wang
- Biomedical Signals and Systems, TechMed Centre, University of Twente, 7522 NH Enschede, The Netherlands
| | - Pierre Bordachar
- Cardio-Thoracic Unit, Bordeaux University Hospital (CHU), F-33600 Bordeaux, France; (A.V.); (J.v.d.Z.)
- IHU Liryc, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, F-33600 Bordeaux, France
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Lee MA, Song M, Bessette H, Roberts Davis M, Tyner TE, Reid A. Use of wearables for monitoring cardiometabolic health: A systematic review. Int J Med Inform 2023; 179:105218. [PMID: 37806179 DOI: 10.1016/j.ijmedinf.2023.105218] [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: 05/09/2023] [Revised: 08/28/2023] [Accepted: 09/15/2023] [Indexed: 10/10/2023]
Abstract
INTRODUCTION Cardiometabolic disorders (CMD) such as hyperglycemia, obesity, hypertension, and dyslipidemia are the leading causes of mortality and significant public health concerns worldwide. With the advances in wireless technology, wearables have become popular for health promotion, but its impact on cardiometabolic health is not well understood. PURPOSE A systematic literature review aimed to describe the features of wearables used for monitoring cardiometabolic health and identify the impact of using wearables on those cardiometabolic health indicators. METHODS A systematic search of PubMed, CINAHL, Academic Search Complete, and Science and Technology Collection databases was performed using keywords related to CMD risk indicators and wearables. The wearables were limited to sensors for blood pressure (BP), heart rate (HR), electrocardiogram (ECG), glucose, and cholesterol. INCLUDED STUDIES 1) were published from 2016 to March 2021 in English, 2) focused on wearables external to the body, and 3) examined wearable use by individuals in daily life (not by health care providers). Protocol, technical, and non-empirical studies were excluded. RESULTS Out of 53 studies, the types of wearables used were smartwatches (45.3%), patches (34.0%), chest straps (22.6%), wristbands (13.2%), and others (9.4%). HR (58.5%), glucose (28.3%), and ECG (26.4%) were the predominant indicators. No studies tracked BP or cholesterol. Additional features of wearables included physical activity, respiration, sleep, diet, and symptom monitoring. Twenty-two studies primarily focused on the use of wearables and reported direct impacts on cardiometabolic indicators; seven studies used wearables as part of a multi-modality approach and presented outcomes affected by a primary intervention but measured through CMD-sensor wearables; and 24 validated the precision and usability of CMD-sensor wearables. CONCLUSION The impact of wearables on cardiometabolic indicators varied across the studies, indicating the need for further research. However, this body of literature highlights the potential of wearables to promote cardiometabolic health.
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Affiliation(s)
- Mikyoung A Lee
- Texas Woman's University, College of Nursing, Dallas, TX, United States.
| | - MinKyoung Song
- Oregon Health & Science University, School of Nursing, Portland, OR, United States.
| | - Hannah Bessette
- Oregon Health & Science University, School of Nursing, Portland, OR, United States
| | - Mary Roberts Davis
- Oregon Health & Science University, School of Nursing, Portland, OR, United States
| | - Tracy E Tyner
- Texas Woman's University, College of Nursing, Dallas, TX, United States
| | - Amy Reid
- Texas Woman's University, College of Nursing, Dallas, TX, United States
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Rao SKS, Martis RJ. RR Interval-based Atrial Fibrillation Detection using Traditional and Ensemble Machine Learning Algorithms. JOURNAL OF MEDICAL SIGNALS & SENSORS 2023; 13:224-232. [PMID: 37622040 PMCID: PMC10445672 DOI: 10.4103/jmss.jmss_4_22] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/23/2022] [Accepted: 05/16/2022] [Indexed: 08/26/2023]
Abstract
Atrial fibrillation (AF) is a life threatening disease and can cause stroke, heart failure, and sometimes death. To reduce the rate of mortality and morbidity due to increased prevalence of AF, early detection of the same becomes a prior concern. Traditional machine learning (TML) algorithms and ensemble machine learning (EML) algorithms are proposed to detect AF in this article. The performances of both these methods are compared in this study. Methodology involves computation of RR interval features extracted from electrocardiogram and its classification into: normal, AF, and other rhythms. TML techniques such as Classification and Regression Tree, K Nearest Neighbor, C4.5, Iterative Dichotomiser 3, Support Vector Machine and EML classifier such as Random Forest (RF), and Rotation Forest are used for classification. The proposed method is evaluated using PhysioNet challenge 2017. During the tenfold cross validation, it is observed that RF classifier provided good classification accuracy of 99.10% with area under the curve of 0.998. Apart from contributing a new methodology, the proposed study also experimentally proves higher performance with ensemble learning method, RF. The methodology has many applications in health care management systems including defibrillators, cardiac pacemakers, etc.
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Affiliation(s)
- S. K. Shrikanth Rao
- Department of Electronics and Communication Engineering, Vivekananda College of Engineering and Technology, Puttur, Karnataka, India
| | - Roshan Joy Martis
- Department of Computer Science Engineering, Global Academy of Technology, Bengaluru, Karnataka, India
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Neri L, Oberdier MT, van Abeelen KCJ, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick RT, Borghi C, Halperin HR. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. SENSORS (BASEL, SWITZERLAND) 2023; 23:4805. [PMID: 37430719 PMCID: PMC10223364 DOI: 10.3390/s23104805] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Revised: 05/09/2023] [Accepted: 05/12/2023] [Indexed: 07/12/2023]
Abstract
Worldwide, population aging and unhealthy lifestyles have increased the incidence of high-risk health conditions such as cardiovascular diseases, sleep apnea, and other conditions. Recently, to facilitate early identification and diagnosis, efforts have been made in the research and development of new wearable devices to make them smaller, more comfortable, more accurate, and increasingly compatible with artificial intelligence technologies. These efforts can pave the way to the longer and continuous health monitoring of different biosignals, including the real-time detection of diseases, thus providing more timely and accurate predictions of health events that can drastically improve the healthcare management of patients. Most recent reviews focus on a specific category of disease, the use of artificial intelligence in 12-lead electrocardiograms, or on wearable technology. However, we present recent advances in the use of electrocardiogram signals acquired with wearable devices or from publicly available databases and the analysis of such signals with artificial intelligence methods to detect and predict diseases. As expected, most of the available research focuses on heart diseases, sleep apnea, and other emerging areas, such as mental stress. From a methodological point of view, although traditional statistical methods and machine learning are still widely used, we observe an increasing use of more advanced deep learning methods, specifically architectures that can handle the complexity of biosignal data. These deep learning methods typically include convolutional and recurrent neural networks. Moreover, when proposing new artificial intelligence methods, we observe that the prevalent choice is to use publicly available databases rather than collecting new data.
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Affiliation(s)
- Luca Neri
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Matt T. Oberdier
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Kirsten C. J. van Abeelen
- Department of Informatics, Systems, and Communication, University of Milano-Bicocca, 20126 Milan, Italy
- Department of Internal Medicine, Radboud University Medical Center, 6525 AJ Nijmegen, The Netherlands
| | - Luca Menghini
- Department of Psychology and Cognitive Science, University of Trento, 38068 Rovereto, Italy
| | - Ethan Tumarkin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Hemantkumar Tripathi
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Sujai Jaipalli
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Alessandro Orro
- Institute of Biomedical Technologies, National Research Council, 20054 Segrate, Italy
| | - Nazareno Paolocci
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Ilaria Gallelli
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Massimo Dall’Olio
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Amir Beker
- AccYouRate Group S.p.A., 67100 L’Aquila, Italy
| | - Richard T. Carrick
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
| | - Claudio Borghi
- Department of Medical and Surgical Sciences, University of Bologna, 40138 Bologna, Italy
| | - Henry R. Halperin
- Department of Medicine, Division of Cardiology, Johns Hopkins University, Baltimore, MD 21218, USA; (L.N.)
- Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA
- Department of Radiology, Johns Hopkins University, Baltimore, MD 21205, USA
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Ciccarelli M, Giallauria F, Carrizzo A, Visco V, Silverio A, Cesaro A, Calabrò P, De Luca N, Mancusi C, Masarone D, Pacileo G, Tourkmani N, Vigorito C, Vecchione C. Artificial intelligence in cardiovascular prevention: new ways will open new doors. J Cardiovasc Med (Hagerstown) 2023; 24:e106-e115. [PMID: 37186561 DOI: 10.2459/jcm.0000000000001431] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Prevention and effective treatment of cardiovascular disease are progressive issues that grow in tandem with the average age of the world population. Over recent decades, the potential role of artificial intelligence in cardiovascular medicine has been increasingly recognized because of the incredible amount of real-world data (RWD) regarding patient health status and healthcare delivery that can be collated from a variety of sources wherein patient information is routinely collected, including patient registries, clinical case reports, reimbursement claims and billing reports, medical devices, and electronic health records. Like any other (health) data, RWD can be analysed in accordance with high-quality research methods, and its analysis can deliver valuable patient-centric insights complementing the information obtained from conventional clinical trials. Artificial intelligence application on RWD has the potential to detect a patient's health trajectory leading to personalized medicine and tailored treatment. This article reviews the benefits of artificial intelligence in cardiovascular prevention and management, focusing on diagnostic and therapeutic improvements without neglecting the limitations of this new scientific approach.
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Affiliation(s)
- Michele Ciccarelli
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Albino Carrizzo
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
| | - Valeria Visco
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Angelo Silverio
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
| | - Arturo Cesaro
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Paolo Calabrò
- Department of Translational Medical Sciences, University of Campania 'Luigi Vanvitelli', Naples, Italy
| | - Nicola De Luca
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Costantino Mancusi
- Department of Advanced Biomedical Sciences, Federico II University, Naples, Italy
| | - Daniele Masarone
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Giuseppe Pacileo
- Heart Failure Unit, Department of Cardiology, AORN dei Colli-Monaldi Hospital Naples, Naples, Italy
| | - Nidal Tourkmani
- Cardiology and Cardiac Rehabilitation Unit, 'Mons. Giosuè Calaciura Clinic', Catania, Italy
- ABL, Guangzhou, China
| | - Carlo Vigorito
- Department of Translational Medical Sciences, Federico II University, Naples, Italy
| | - Carmine Vecchione
- Department of Medicine, Surgery and Dentistry, University of Salerno, Baronissi, Italy
- Vascular Physiopathology Unit, IRCCS Neuromed, Pozzilli
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Patel S, Wang M, Guo J, Smith G, Chen C. A Study of R-R Interval Transition Matrix Features for Machine Learning Algorithms in AFib Detection. SENSORS (BASEL, SWITZERLAND) 2023; 23:3700. [PMID: 37050761 PMCID: PMC10099376 DOI: 10.3390/s23073700] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/16/2023] [Revised: 03/14/2023] [Accepted: 03/28/2023] [Indexed: 06/19/2023]
Abstract
Atrial Fibrillation (AFib) is a heart condition that occurs when electrophysiological malformations within heart tissues cause the atria to lose coordination with the ventricles, resulting in "irregularly irregular" heartbeats. Because symptoms are subtle and unpredictable, AFib diagnosis is often difficult or delayed. One possible solution is to build a system which predicts AFib based on the variability of R-R intervals (the distances between two R-peaks). This research aims to incorporate the transition matrix as a novel measure of R-R variability, while combining three segmentation schemes and two feature importance measures to systematically analyze the significance of individual features. The MIT-BIH dataset was first divided into three segmentation schemes, consisting of 5-s, 10-s, and 25-s subsets. In total, 21 various features, including the transition matrix features, were extracted from these subsets and used for the training of 11 machine learning classifiers. Next, permutation importance and tree-based feature importance calculations determined the most predictive features for each model. In summary, with Leave-One-Person-Out Cross Validation, classifiers under the 25-s segmentation scheme produced the best accuracies; specifically, Gradient Boosting (96.08%), Light Gradient Boosting (96.11%), and Extreme Gradient Boosting (96.30%). Among eleven classifiers, the three gradient boosting models and Random Forest exhibited the highest overall performance across all segmentation schemes. Moreover, the permutation and tree-based importance results demonstrated that the transition matrix features were most significant with longer subset lengths.
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Affiliation(s)
- Sahil Patel
- John T. Hoggard High School, Wilmington, NC 28403, USA
- Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
| | - Maximilian Wang
- Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
- Isaac M. Bear Early College High School, Wilmington, NC 28403, USA
| | - Justin Guo
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Georgia Smith
- Department of Biostatistics, University of Colorado’s Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Cuixian Chen
- Department of Mathematics and Statistics, University of North Carolina Wilmington, Wilmington, NC 28403, USA
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Stojancic RS, Subramaniam A, Vuong C, Utkarsh K, Golbasi N, Fernandez O, Shah N. Predicting Pain in People With Sickle Cell Disease in the Day Hospital Using the Commercial Wearable Apple Watch: Feasibility Study. JMIR Form Res 2023; 7:e45355. [PMID: 36917171 PMCID: PMC10131899 DOI: 10.2196/45355] [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: 12/26/2022] [Revised: 01/30/2023] [Accepted: 01/30/2023] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Sickle cell disease (SCD) is a genetic red blood cell disorder associated with severe complications including chronic anemia, stroke, and vaso-occlusive crises (VOCs). VOCs are unpredictable, difficult to treat, and the leading cause of hospitalization. Recent efforts have focused on the use of mobile health technology to develop algorithms to predict pain in people with sickle cell disease. Combining the data collection abilities of a consumer wearable, such as the Apple Watch, and machine learning techniques may help us better understand the pain experience and find trends to predict pain from VOCs. OBJECTIVE The aim of this study is to (1) determine the feasibility of using the Apple Watch to predict the pain scores in people with sickle cell disease admitted to the Duke University SCD Day Hospital, referred to as the Day Hospital, and (2) build and evaluate machine learning algorithms to predict the pain scores of VOCs with the Apple Watch. METHODS Following approval of the institutional review board, patients with sickle cell disease, older than 18 years, and admitted to Day Hospital for a VOC between July 2021 and September 2021 were approached to participate in the study. Participants were provided with an Apple Watch Series 3, which is to be worn for the duration of their visit. Data collected from the Apple Watch included heart rate, heart rate variability (calculated), and calories. Pain scores and vital signs were collected from the electronic medical record. Data were analyzed using 3 different machine learning models: multinomial logistic regression, gradient boosting, and random forest, and 2 null models, to assess the accuracy of pain scores. The evaluation metrics considered were accuracy (F1-score), area under the receiving operating characteristic curve, and root-mean-square error (RMSE). RESULTS We enrolled 20 patients with sickle cell disease, all of whom identified as Black or African American and consisted of 12 (60%) females and 8 (40%) males. There were 14 individuals diagnosed with hemoglobin type SS (70%). The median age of the population was 35.5 (IQR 30-41) years. The median time each individual spent wearing the Apple Watch was 2 hours and 17 minutes and a total of 15,683 data points were collected across the population. All models outperformed the null models, and the best-performing model was the random forest model, which was able to predict the pain scores with an accuracy of 84.5%, and a RMSE of 0.84. CONCLUSIONS The strong performance of the model in all metrics validates feasibility and the ability to use data collected from a noninvasive device, the Apple Watch, to predict the pain scores during VOCs. It is a novel and feasible approach and presents a low-cost method that could benefit clinicians and individuals with sickle cell disease in the treatment of VOCs.
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Affiliation(s)
- Rebecca Sofia Stojancic
- Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Division of Hematology, Duke University Hospital, Durham, NC, United States
| | - Arvind Subramaniam
- Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Division of Hematology, Duke University Hospital, Durham, NC, United States.,Brody School of Medicine, East Carolina University, Greenville, NC, United States
| | - Caroline Vuong
- Department of Pediatric Hematology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
| | - Kumar Utkarsh
- Department of Engineering Sciences and Applied Mathematics, Northwestern University, Evanston, IL, United States
| | - Nuran Golbasi
- Joan & Sanford I Weill Medical College, Cornell University, New York, NY, United States.,University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Olivia Fernandez
- Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Division of Hematology, Duke University Hospital, Durham, NC, United States
| | - Nirmish Shah
- Duke Sickle Cell Comprehensive Care Unit, Department of Medicine, Division of Hematology, Duke University Hospital, Durham, NC, United States
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Zhang H, Liu C, Tang F, Li M, Zhang D, Xia L, Crozier S, Gan H, Zhao N, Xu W, Liu F. Atrial fibrillation classification based on the 2D representation of minimal subset ECG and a non-deep neural network. Front Physiol 2023; 14:1070621. [PMID: 36866172 PMCID: PMC9971936 DOI: 10.3389/fphys.2023.1070621] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2022] [Accepted: 01/30/2023] [Indexed: 02/16/2023] Open
Abstract
Atrial fibrillation (AF) is the most common cardiac arrhythmia, and its early detection is critical for preventing complications and optimizing treatment. In this study, a novel AF prediction method is proposed, which is based on investigating a subset of the 12-lead ECG data using a recurrent plot and ParNet-adv model. The minimal subset of ECG leads (II &V1) is determined via a forward stepwise selection procedure, and the selected 1D ECG data is transformed into 2D recurrence plot (RP) images as an input to train a shallow ParNet-adv Network for AF prediction. In this study, the proposed method achieved F1 score of 0.9763, Precision of 0.9654, Recall of 0.9875, Specificity of 0.9646, and Accuracy of 0.9760, which significantly outperformed solutions based on single leads and complete 12 leads. When studying several ECG datasets, including the CPSC and Georgia ECG databases of the PhysioNet/Computing in Cardiology Challenge 2020, the new method achieved F1 score of 0.9693 and 0.8660, respectively. The results suggested a good generalization of the proposed method. Compared with several state-of-art frameworks, the proposed model with a shallow network of only 12 depths and asymmetric convolutions achieved the highest average F1 score. Extensive experimental studies proved that the proposed method has a high potential for AF prediction in clinical and particularly wearable applications.
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Affiliation(s)
- Hua Zhang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Chengyu Liu
- School of Instrument Science and Engineering, Southeast University, Nanjing, Jiangsu, China
| | - Fangfang Tang
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Mingyan Li
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Dongxia Zhang
- Zhejiang Provincial Centre for Disease Control and Prevention CN, Hangzhou, Zhejiang, China
| | - Ling Xia
- Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Stuart Crozier
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Hongping Gan
- School of Software, Northwestern Polytechnical University, Xi’an, China
| | - Nan Zhao
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia
| | - Wenlong Xu
- Department of Biomedical Engineering, China Jiliang University, Hangzhou, Zhejiang, China,*Correspondence: Wenlong Xu, ; Feng Liu,
| | - Feng Liu
- School of Information Technology and Electrical Engineering, University of Queensland, Brisbane, QLD, Australia,*Correspondence: Wenlong Xu, ; Feng Liu,
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9
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Silva CAO, Morillo CA, Leite-Castro C, González-Otero R, Bessani M, González R, Castellanos JC, Otero L. Machine learning for atrial fibrillation risk prediction in patients with sleep apnea and coronary artery disease. Front Cardiovasc Med 2022; 9:1050409. [PMID: 36568544 PMCID: PMC9768180 DOI: 10.3389/fcvm.2022.1050409] [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: 09/21/2022] [Accepted: 11/22/2022] [Indexed: 12/12/2022] Open
Abstract
Background Patients with sleep apnea (SA) and coronary artery disease (CAD) are at higher risk of atrial fibrillation (AF) than the general population. Our objectives were: to evaluate the role of CAD and SA in determining AF risk through cluster and survival analysis, and to develop a risk model for predicting AF. Methods Electronic medical record (EMR) database from 22,302 individuals including 10,202 individuals with AF, CAD, and SA, and 12,100 individuals without these diseases were analyzed using K-means clustering technique; k-nearest neighbor (kNN) algorithm and survival analysis. Age, sex, and diseases developed for each individual during 9 years were used for cluster and survival analysis. Results The risk models for AF, CAD, and SA were identified with high accuracy and sensitivity (0.98). Cluster analysis showed that CAD and high blood pressure (HBP) are the most prevalent diseases in the AF group, HBP is the most prevalent disease in CAD; and HBP and CAD are the most prevalent diseases in the SA group. Survival analysis demonstrated that individuals with HBP, CAD, and SA had a 1.5-fold increased risk of developing AF [hazard ratio (HR): 1.49, 95% CI: 1.18-1.87, p = 0.0041; HR: 1.46, 95% CI: 1.09-1.96, p = 0.01; HR: 1.54, 95% CI: 1.22-1.94, p = 0.0039, respectively] and individuals with chronic kidney disease (CKD) developed AF approximately 50% earlier than patients without these comorbidities in a period of 7 years (HR: 3.36, 95% CI: 1.46-7.73, p = 0.0023). Comorbidities that contributed to develop AF earlier in females compared to males in the group of 50-64 years were HBP (HR: 3.75 95% CI: 1.08-13, p = 0.04) CAD and SA in the group of 60-75 years were (HR: 2.4 95% CI: 1.18-4.86, p = 0.02; HR: 2.51, 95% CI: 1.14-5.52, p = 0.02, respectively). Conclusion Machine learning based algorithms demonstrated that CAD, SA, HBP, and CKD are significant risk factors for developing AF in a Latin-American population.
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Affiliation(s)
- Carlos A. O. Silva
- Programa de Pós-graduação em Inovação Tecnológica, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Carlos A. Morillo
- Department of Cardiac Sciences, Cumming School of Medicine, Libin Cardiovascular Institute, University of Calgary, Calgary, AB, Canada
| | - Cristiano Leite-Castro
- Departamento de Engenharia Elétrica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | - Rafael González-Otero
- Departamento de Economía, Facultad de Ciencias Económicas y Administrativas, Pontificia Universidad Javeriana, Bogotá, Colombia
| | - Michel Bessani
- Departamento de Engenharia Elétrica, Escola de Engenharia, Universidade Federal de Minas Gerais, Belo Horizonte, Minas Gerais, Brazil
| | | | - Julio C. Castellanos
- Departamento de Dirección General, Hospital Universitario San Ignacio, Bogotá, Colombia
| | - Liliana Otero
- Centro de Investigaciones Odontológicas, Facultad de Odontología, Pontificia Universidad Javeriana, Bogotá, Colombia,*Correspondence: Liliana Otero,
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10
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Beavers DL, Chung EH. Wearables in Sports Cardiology. Clin Sports Med 2022; 41:405-423. [PMID: 35710269 DOI: 10.1016/j.csm.2022.02.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
The expanding array and adoption of consumer health wearables is creating a new dynamic to the patient-health-care provider relationship. Providers are increasingly tasked with integrating the biometric data collected from their patients into clinical care. Further, a growing body of evidence is supporting the provider-driven utility of wearables in the screening, diagnosis, and monitoring of cardiovascular disease. Here we highlight existing and emerging wearable health technologies and the potential applications for use within sports cardiology. We additionally highlight how wearables can advance the remote cardiovascular care of patients within the context of the COVID-19 pandemic. Finally, despite these promising advances, we acknowledge some of the significant challenges that remain before wearables can be routinely incorporated into clinical care.
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Affiliation(s)
- David L Beavers
- Department of Internal Medicine, Division of Cardiac Electrophysiology, University of Michigan, 1500 East Medical Center Drive, SPC 5853, Ann Arbor, MI 48109-5853, USA.
| | - Eugene H Chung
- Department of Internal Medicine, Division of Cardiac Electrophysiology, University of Michigan, 1500 East Medical Center Drive, SPC 5853, Ann Arbor, MI 48109-5853, USA
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11
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Sivanandarajah P, Wu H, Bajaj N, Khan S, Ng FS. Is machine learning the future for atrial fibrillation screening? CARDIOVASCULAR DIGITAL HEALTH JOURNAL 2022; 3:136-145. [PMID: 35720677 PMCID: PMC9204790 DOI: 10.1016/j.cvdhj.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
Atrial fibrillation (AF) is the most common arrhythmia and causes significant morbidity and mortality. Early identification of AF may lead to early treatment of AF and may thus prevent AF-related strokes and complications. However, there is no current formal, cost-effective strategy for population screening for AF. In this review, we give a brief overview of targeted screening for AF, AF risk score models used for screening and describe the different screening tools. We then go on to extensively discuss the potential applications of machine learning in AF screening.
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Affiliation(s)
- Pavidra Sivanandarajah
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Huiyi Wu
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Nikesh Bajaj
- National Heart and Lung Institute, Imperial College London, London, United Kingdom
| | - Sadia Khan
- Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
| | - Fu Siong Ng
- National Heart and Lung Institute, Imperial College London, London, United Kingdom.,Chelsea and Westminster NHS Foundation Trust, London, United Kingdom
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12
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Wearable Devices for Physical Monitoring of Heart: A Review. BIOSENSORS 2022; 12:bios12050292. [PMID: 35624593 PMCID: PMC9138373 DOI: 10.3390/bios12050292] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 04/27/2022] [Accepted: 04/29/2022] [Indexed: 12/19/2022]
Abstract
Cardiovascular diseases (CVDs) are the leading cause of death globally. An effective strategy to mitigate the burden of CVDs has been to monitor patients’ biomedical variables during daily activities with wearable technology. Nowadays, technological advance has contributed to wearables technology by reducing the size of the devices, improving the accuracy of sensing biomedical variables to be devices with relatively low energy consumption that can manage security and privacy of the patient’s medical information, have adaptability to any data storage system, and have reasonable costs with regard to the traditional scheme where the patient must go to a hospital for an electrocardiogram, thus contributing a serious option in diagnosis and treatment of CVDs. In this work, we review commercial and noncommercial wearable devices used to monitor CVD biomedical variables. Our main findings revealed that commercial wearables usually include smart wristbands, patches, and smartwatches, and they generally monitor variables such as heart rate, blood oxygen saturation, and electrocardiogram data. Noncommercial wearables focus on monitoring electrocardiogram and photoplethysmography data, and they mostly include accelerometers and smartwatches for detecting atrial fibrillation and heart failure. However, using wearable devices without healthy personal habits will cause disappointing results in the patient’s health.
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13
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A Decision-making System with Reject Option for Atrial Fibrillation Prediction without ECG Signals. Ing Rech Biomed 2022. [DOI: 10.1016/j.irbm.2022.04.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
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14
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Shei RJ, Holder IG, Oumsang AS, Paris BA, Paris HL. Wearable activity trackers-advanced technology or advanced marketing? Eur J Appl Physiol 2022; 122:1975-1990. [PMID: 35445837 PMCID: PMC9022022 DOI: 10.1007/s00421-022-04951-1] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Accepted: 04/04/2022] [Indexed: 11/26/2022]
Abstract
Wearable devices represent one of the most popular trends in health and fitness. Rapid advances in wearable technology present a dizzying display of possible functions: from thermometers and barometers, magnetometers and accelerometers, to oximeters and calorimeters. Consumers and practitioners utilize wearable devices to track outcomes, such as energy expenditure, training load, step count, and heart rate. While some rely on these devices in tandem with more established tools, others lean on wearable technology for health-related outcomes, such as heart rhythm analysis, peripheral oxygen saturation, sleep quality, and caloric expenditure. Given the increasing popularity of wearable devices for both recreation and health initiatives, understanding the strengths and limitations of these technologies is increasingly relevant. Need exists for continued evaluation of the efficacy of wearable devices to accurately and reliably measure purported outcomes. The purposes of this review are (1) to assess the current state of wearable devices using recent research on validity and reliability, (2) to describe existing gaps between physiology and technology, and (3) to offer expert interpretation for the lay and professional audience on how best to approach wearable technology and employ it in the pursuit of health and fitness. Current literature demonstrates inconsistent validity and reliability for various metrics, with algorithms not publicly available or lacking high-quality validation studies. Advancements in wearable technology should consider standardizing validation metrics, providing transparency in used algorithms, and improving how technology can be tailored to individuals. Until then, it is prudent to exercise caution when interpreting metrics reported from consumer-wearable devices.
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Affiliation(s)
- Ren-Jay Shei
- Indiana University Alumni Association, Indiana University, 1000 E 17th Street, Bloomington, IN, 47408, USA.
| | - Ian G Holder
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| | - Alicia S Oumsang
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| | - Brittni A Paris
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
| | - Hunter L Paris
- Department of Sports Medicine, Pepperdine University, Malibu, CA, USA
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15
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Martini C, Di Maria B, Reverberi C, Tuttolomondo D, Gaibazzi N. Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation. Diagnostics (Basel) 2022; 12:diagnostics12030712. [PMID: 35328265 PMCID: PMC8947007 DOI: 10.3390/diagnostics12030712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2022] [Revised: 03/05/2022] [Accepted: 03/06/2022] [Indexed: 11/16/2022] Open
Abstract
The usefulness of opportunistic arrhythmia screening strategies, using an electrocardiogram (ECG) or other methods for random “snapshot” assessments is limited by the unexpected and occasional nature of arrhythmias, leading to a high rate of missed diagnosis. We have previously validated a cardiac monitoring system for AF detection pairing simple consumer-grade Bluetooth low-energy (BLE) heart rate (HR) sensors with a smartphone application (RITMIA™, Heart Sentinel srl, Italy). In the current study, we test a significant upgrade to the above-mentioned system, thanks to the technical capability of new HR sensors to run algorithms on the sensor itself and to acquire, and store on-board, single-lead ECG strips. We have reprogrammed an HR monitor intended for sports use (Movensense HR+) to run our proprietary RITMIA algorithm code in real-time, based on RR analysis, so that if any type of arrhythmia is detected, it triggers a brief retrospective recording of a single-lead ECG, providing tracings of the specific arrhythmia for later consultation. We report the initial data on the behavior, feasibility, and high diagnostic accuracy of this ultra-low weight customized device for standalone automatic arrhythmia detection and ECG recording, when several types of arrhythmias were simulated under different baseline conditions. Conclusions: The customized device was capable of detecting all types of simulated arrhythmias and correctly triggered a visually interpretable ECG tracing. Future human studies are needed to address real-life accuracy of this device.
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Affiliation(s)
- Chiara Martini
- Department of Radiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy
- Correspondence: ; Tel.: +39-3457245174
| | | | - Claudio Reverberi
- Poliambulatorio Città di Collecchio, Str. Nazionale Est, 4/A, 43044 Collecchio, Italy;
| | - Domenico Tuttolomondo
- Non-invasive Cardiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy; (D.T.); (N.G.)
| | - Nicola Gaibazzi
- Non-invasive Cardiology, Parma University Hospital, Via Gramsci 14, 43125 Parma, Italy; (D.T.); (N.G.)
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16
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Wang YC, Xu X, Hajra A, Apple S, Kharawala A, Duarte G, Liaqat W, Fu Y, Li W, Chen Y, Faillace RT. Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study. Diagnostics (Basel) 2022; 12:diagnostics12030689. [PMID: 35328243 PMCID: PMC8947563 DOI: 10.3390/diagnostics12030689] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 03/01/2022] [Accepted: 03/06/2022] [Indexed: 02/04/2023] Open
Abstract
Atrial fibrillation (AF) is a common arrhythmia affecting 8–10% of the population older than 80 years old. The importance of early diagnosis of atrial fibrillation has been broadly recognized since arrhythmias significantly increase the risk of stroke, heart failure and tachycardia-induced cardiomyopathy with reduced cardiac function. However, the prevalence of atrial fibrillation is often underestimated due to the high frequency of clinically silent atrial fibrillation as well as paroxysmal atrial fibrillation, both of which are hard to catch by routine physical examination or 12-lead electrocardiogram (ECG). The development of wearable devices has provided a reliable way for healthcare providers to uncover undiagnosed atrial fibrillation in the population, especially those most at risk. Furthermore, with the advancement of artificial intelligence and machine learning, the technology is now able to utilize the database in assisting detection of arrhythmias from the data collected by the devices. In this review study, we compare the different wearable devices available on the market and review the current advancement in artificial intelligence in diagnosing atrial fibrillation. We believe that with the aid of the progressive development of technologies, the diagnosis of atrial fibrillation shall be made more effectively and accurately in the near future.
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Affiliation(s)
- Yu-Chiang Wang
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
- Correspondence:
| | - Xiaobo Xu
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Adrija Hajra
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Samuel Apple
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Amrin Kharawala
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Gustavo Duarte
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Wasla Liaqat
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiwen Fu
- Department of Medicine, Kaiser Permanente Santa Clara Medical Center, Santa Clara, CA 95051, USA;
| | - Weijia Li
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Yiyun Chen
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
| | - Robert T. Faillace
- Department of Medicine, New York City Health + Hospitals/Jacobi, Albert Einstein College of Medicine, The Bronx, New York, NY 10461, USA; (X.X.); (A.H.); (S.A.); (A.K.); (G.D.); (W.L.); (W.L.); (Y.C.); (R.T.F.)
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17
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Naseri Jahfari A, Tax D, Reinders M, van der Bilt I. Machine Learning for Cardiovascular Outcomes From Wearable Data: Systematic Review From a Technology Readiness Level Point of View. JMIR Med Inform 2022; 10:e29434. [PMID: 35044316 PMCID: PMC8811688 DOI: 10.2196/29434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 11/22/2021] [Accepted: 12/04/2021] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Wearable technology has the potential to improve cardiovascular health monitoring by using machine learning. Such technology enables remote health monitoring and allows for the diagnosis and prevention of cardiovascular diseases. In addition to the detection of cardiovascular disease, it can exclude this diagnosis in symptomatic patients, thereby preventing unnecessary hospital visits. In addition, early warning systems can aid cardiologists in timely treatment and prevention. OBJECTIVE This study aims to systematically assess the literature on detecting and predicting outcomes of patients with cardiovascular diseases by using machine learning with data obtained from wearables to gain insights into the current state, challenges, and limitations of this technology. METHODS We searched PubMed, Scopus, and IEEE Xplore on September 26, 2020, with no restrictions on the publication date and by using keywords such as "wearables," "machine learning," and "cardiovascular disease." Methodologies were categorized and analyzed according to machine learning-based technology readiness levels (TRLs), which score studies on their potential to be deployed in an operational setting from 1 to 9 (most ready). RESULTS After the removal of duplicates, application of exclusion criteria, and full-text screening, 55 eligible studies were included in the analysis, covering a variety of cardiovascular diseases. We assessed the quality of the included studies and found that none of the studies were integrated into a health care system (TRL<6), prospective phase 2 and phase 3 trials were absent (TRL<7 and 8), and group cross-validation was rarely used. These issues limited these studies' ability to demonstrate the effectiveness of their methodologies. Furthermore, there seemed to be no agreement on the sample size needed to train these studies' models, the size of the observation window used to make predictions, how long participants should be observed, and the type of machine learning model that is suitable for predicting cardiovascular outcomes. CONCLUSIONS Although current studies show the potential of wearables to monitor cardiovascular events, their deployment as a diagnostic or prognostic cardiovascular clinical tool is hampered by the lack of a realistic data set and proper systematic and prospective evaluation.
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Affiliation(s)
- Arman Naseri Jahfari
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands.,Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
| | - David Tax
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Marcel Reinders
- Pattern Recognition and Bioinformatics, Delft University of Technology, Delft, Netherlands
| | - Ivo van der Bilt
- Department of Cardiology, Haga Teaching Hospital, The Hague, Netherlands
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18
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Anomaly Detection Framework for Wearables Data: A Perspective Review on Data Concepts, Data Analysis Algorithms and Prospects. SENSORS 2022; 22:s22030756. [PMID: 35161502 PMCID: PMC8840097 DOI: 10.3390/s22030756] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 01/11/2022] [Accepted: 01/15/2022] [Indexed: 12/23/2022]
Abstract
Wearable devices use sensors to evaluate physiological parameters, such as the heart rate, pulse rate, number of steps taken, body fat and diet. The continuous monitoring of physiological parameters offers a potential solution to assess personal healthcare. Identifying outliers or anomalies in heart rates and other features can help identify patterns that can play a significant role in understanding the underlying cause of disease states. Since anomalies are present within the vast amount of data generated by wearable device sensors, identifying anomalies requires accurate automated techniques. Given the clinical significance of anomalies and their impact on diagnosis and treatment, a wide range of detection methods have been proposed to detect anomalies. Much of what is reported herein is based on previously published literature. Clinical studies employing wearable devices are also increasing. In this article, we review the nature of the wearables-associated data and the downstream processing methods for detecting anomalies. In addition, we also review supervised and un-supervised techniques as well as semi-supervised methods that overcome the challenges of missing and un-annotated healthcare data.
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19
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Giang KW, Helgadottir S, Dellborg M, Volpe G, Mandalenakis Z. Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register-based medical hospital data and neural networks. EUROPEAN HEART JOURNAL. DIGITAL HEALTH 2021; 2:568-575. [PMID: 36713111 PMCID: PMC9707883 DOI: 10.1093/ehjdh/ztab065] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 06/28/2021] [Accepted: 07/14/2021] [Indexed: 02/01/2023]
Abstract
Aims To improve short-and long-term predictions of mortality and atrial fibrillation (AF) among patients with congenital heart disease (CHD) from a nationwide population using neural networks (NN). Methods and results The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with CHD born from 1970 to 2017. A total of 71 941 CHD patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a NN model was obtained to predict mortality and AF. Logistic regression (LR) based on the same data was used as a baseline comparison. Of 71 941 CHD patients, a total of 5768 died (8.02%) and 995 (1.38%) developed AF over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of NN models in predicting the mortality and AF was higher than the performance of LR regardless of the complexity of the disease, with an average area under the receiver operating characteristic of >0.80 and >0.70, respectively. The largest differences were observed in mortality and complexity of CHD over time. Conclusion We found that NN can be used to predict mortality and AF on a nationwide scale using data that are easily obtainable by clinicians. In addition, NN showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.
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Affiliation(s)
- Kok Wai Giang
- Institute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 11, 416 50 Gothenburg, Sweden,Corresponding author. Tel: +46736488997,
| | - Saga Helgadottir
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Mikael Dellborg
- Institute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 11, 416 50 Gothenburg, Sweden,Adult Congenital Heart Unit, Department of Medicine, Sahlgrenska University Hospital/Östra, Gothenburg, Sweden
| | - Giovanni Volpe
- Department of Physics, University of Gothenburg, Gothenburg, Sweden
| | - Zacharias Mandalenakis
- Institute of Medicine, Department of Molecular and Clinical Medicine, Sahlgrenska Academy, University of Gothenburg, Diagnosvägen 11, 416 50 Gothenburg, Sweden,Adult Congenital Heart Unit, Department of Medicine, Sahlgrenska University Hospital/Östra, Gothenburg, Sweden
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20
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Exploration of physiological sensors, features, and machine learning models for pain intensity estimation. PLoS One 2021; 16:e0254108. [PMID: 34242325 PMCID: PMC8270203 DOI: 10.1371/journal.pone.0254108] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 06/20/2021] [Indexed: 11/19/2022] Open
Abstract
In current clinical settings, typically pain is measured by a patient’s self-reported information. This subjective pain assessment results in suboptimal treatment plans, over-prescription of opioids, and drug-seeking behavior among patients. In the present study, we explored automatic objective pain intensity estimation machine learning models using inputs from physiological sensors. This study uses BioVid Heat Pain Dataset. We extracted features from Electrodermal Activity (EDA), Electrocardiogram (ECG), Electromyogram (EMG) signals collected from study participants subjected to heat pain. We built different machine learning models, including Linear Regression, Support Vector Regression (SVR), Neural Networks and Extreme Gradient Boosting for continuous value pain intensity estimation. Then we identified the physiological sensor, feature set and machine learning model that give the best predictive performance. We found that EDA is the most information-rich sensor for continuous pain intensity prediction. A set of only 3 features from EDA signals using SVR model gave an average performance of 0.93 mean absolute error (MAE) and 1.16 root means square error (RMSE) for the subject-independent model and of 0.92 MAE and 1.13 RMSE for subject-dependent. The MAE achieved with signal-feature-model combination is less than 1 unit on 0 to 4 continues pain scale, which is smaller than the MAE achieved by the methods reported in the literature. These results demonstrate that it is possible to estimate pain intensity of a patient using a computationally inexpensive machine learning model with 3 statistical features from EDA signal which can be collected from a wrist biosensor. This method paves a way to developing a wearable pain measurement device.
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21
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Sánchez J, Luongo G, Nothstein M, Unger LA, Saiz J, Trenor B, Luik A, Dössel O, Loewe A. Using Machine Learning to Characterize Atrial Fibrotic Substrate From Intracardiac Signals With a Hybrid in silico and in vivo Dataset. Front Physiol 2021; 12:699291. [PMID: 34290623 PMCID: PMC8287829 DOI: 10.3389/fphys.2021.699291] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 06/08/2021] [Indexed: 11/15/2022] Open
Abstract
In patients with atrial fibrillation, intracardiac electrogram signal amplitude is known to decrease with increased structural tissue remodeling, referred to as fibrosis. In addition to the isolation of the pulmonary veins, fibrotic sites are considered a suitable target for catheter ablation. However, it remains an open challenge to find fibrotic areas and to differentiate their density and transmurality. This study aims to identify the volume fraction and transmurality of fibrosis in the atrial substrate. Simulated cardiac electrograms, combined with a generalized model of clinical noise, reproduce clinically measured signals. Our hybrid dataset approach combines in silico and clinical electrograms to train a decision tree classifier to characterize the fibrotic atrial substrate. This approach captures different in vivo dynamics of the electrical propagation reflected on healthy electrogram morphology and synergistically combines it with synthetic fibrotic electrograms from in silico experiments. The machine learning algorithm was tested on five patients and compared against clinical voltage maps as a proof of concept, distinguishing non-fibrotic from fibrotic tissue and characterizing the patient's fibrotic tissue in terms of density and transmurality. The proposed approach can be used to overcome a single voltage cut-off value to identify fibrotic tissue and guide ablation targeting fibrotic areas.
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Affiliation(s)
- Jorge Sánchez
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Giorgio Luongo
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Mark Nothstein
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Laura A. Unger
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Javier Saiz
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Beatriz Trenor
- Centro de Investigación e Innovación en Bioingeniería (Ci2B), Universitàt Politècnica de València, Valencia, Spain
| | - Armin Luik
- Medizinische Klinik IV, Städtisches Klinikum Karlsruhe, Karlsruhe, Germany
| | - Olaf Dössel
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
| | - Axel Loewe
- Institute of Biomedical Engineering, Karlsruhe Institute for Technology, Karlsruhe, Germany
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22
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AF episodes recognition using optimized time-frequency features and cost-sensitive SVM. Phys Eng Sci Med 2021; 44:613-624. [PMID: 34142316 DOI: 10.1007/s13246-021-01005-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Accepted: 04/24/2021] [Indexed: 10/21/2022]
Abstract
Although atrial fibrillation (AF) Arrhythmia is highly prevalent within a wide range of populations with major associated risks and due to its episodic occurrence, its recognition remains a challenge for doctors. This paper aims to present and experimentally validate a new efficient approach for the detection and classification of this cardiac anomaly using multiple Electrocardiogram (ECG) signals. This work consists of applying Stockwell transform (ST) with compact support kernel (ST-CSK) for ECG time-frequency analysis. The estimation of the atrial activity (AA) is then achieved after analyzing P-waves of the ECG signals for each heartbeat. ECG signals segmentation allows characterizing the AA by making use of its (t, f) flatness, (t, f) flux, energy concentration and heart rate variability. The features matrix is employed as an input of the support vector machines (SVM) working in binary and asymmetrical mode with an embedded reject option. The proposed algorithm is trained and then tested using different ECG sources namely two databases provided by PhysionNet (MIT-BIH Arrhythmia, MIT-BIH Atrial Fibrillation) and recorded ECG signals using MySignals HW development platform with raspberry Pi 3 model B[Formula: see text]. The used method has achieved [Formula: see text] and [Formula: see text] as sensitivity and specificity, respectively. The obtained results confirm that the proposed approach represents a promising tool for Atrial Fibrillation Episodes (AFE) recognition with significant separability between Normal atrial activity and atrial activity with AF even under real and clinical conditions.
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Rouhi R, Clausel M, Oster J, Lauer F. An Interpretable Hand-Crafted Feature-Based Model for Atrial Fibrillation Detection. Front Physiol 2021; 12:657304. [PMID: 34054575 PMCID: PMC8155476 DOI: 10.3389/fphys.2021.657304] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2021] [Accepted: 04/07/2021] [Indexed: 11/21/2022] Open
Abstract
Atrial Fibrillation (AF) is the most common type of cardiac arrhythmia. Early diagnosis of AF helps to improve therapy and prognosis. Machine Learning (ML) has been successfully applied to improve the effectiveness of Computer-Aided Diagnosis (CADx) systems for AF detection. Presenting an explanation for the decision made by an ML model is considerable from the cardiologists' point of view, which decreases the complexity of the ML model and can provide tangible information in their diagnosis. In this paper, a range of explanation techniques is applied to hand-crafted features based ML models for heart rhythm classification. We validate the impact of the techniques by applying feature selection and classification to the 2017 CinC/PhysioNet challenge dataset. The results show the effectiveness and efficiency of SHapley Additive exPlanations (SHAP) technique along with Random Forest (RF) for the classification of the Electrocardiogram (ECG) signals for AF detection with a mean F-score of 0.746 compared to 0.706 for a technique based on the same features based on a cascaded SVM approach. The study also highlights how this interpretable hand-crafted feature-based model can provide cardiologists with a more compact set of features and tangible information in their diagnosis.
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Affiliation(s)
| | | | - Julien Oster
- IADI U1254, Inserm and Université de Lorraine, Nancy, France
| | - Fabien Lauer
- Université de Lorraine, CNRS, LORIA, Nancy, France
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Olier I, Ortega-Martorell S, Pieroni M, Lip GYH. How machine learning is impacting research in atrial fibrillation: implications for risk prediction and future management. Cardiovasc Res 2021; 117:1700-1717. [PMID: 33982064 PMCID: PMC8477792 DOI: 10.1093/cvr/cvab169] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/18/2021] [Accepted: 05/11/2021] [Indexed: 02/01/2023] Open
Abstract
There has been an exponential growth of artificial intelligence (AI) and machine learning (ML) publications aimed at advancing our understanding of atrial fibrillation (AF), which has been mainly driven by the confluence of two factors: the advances in deep neural networks (DeepNNs) and the availability of large, open access databases. It is observed that most of the attention has centred on applying ML for dvsetecting AF, particularly using electrocardiograms (ECGs) as the main data modality. Nearly a third of them used DeepNNs to minimize or eliminate the need for transforming the ECGs to extract features prior to ML modelling; however, we did not observe a significant advantage in following this approach. We also found a fraction of studies using other data modalities, and others centred in aims, such as risk prediction, AF management, and others. From the clinical perspective, AI/ML can help expand the utility of AF detection and risk prediction, especially for patients with additional comorbidities. The use of AI/ML for detection and risk prediction into applications and smart mobile health (mHealth) technology would enable ‘real time’ dynamic assessments. AI/ML could also adapt to treatment changes over time, as well as incident risk factors. Incorporation of a dynamic AI/ML model into mHealth technology would facilitate ‘real time’ assessment of stroke risk, facilitating mitigation of modifiable risk factors (e.g. blood pressure control). Overall, this would lead to an improvement in clinical care for patients with AF.
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Affiliation(s)
- Ivan Olier
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Sandra Ortega-Martorell
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Mark Pieroni
- School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.,Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK
| | - Gregory Y H Lip
- Liverpool Centre for Cardiovascular Science, Liverpool John Moores University, Liverpool, UK.,Liverpool Heart and Chest Hospital, Liverpool, UK
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25
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Wesselius FJ, van Schie MS, De Groot NMS, Hendriks RC. Digital biomarkers and algorithms for detection of atrial fibrillation using surface electrocardiograms: A systematic review. Comput Biol Med 2021; 133:104404. [PMID: 33951551 DOI: 10.1016/j.compbiomed.2021.104404] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/12/2021] [Accepted: 04/12/2021] [Indexed: 10/21/2022]
Abstract
AIMS Automated detection of atrial fibrillation (AF) in continuous rhythm registrations is essential in order to prevent complications and optimize treatment of AF. Many algorithms have been developed to detect AF in surface electrocardiograms (ECGs) during the past few years. The aim of this systematic review is to gain more insight into these available classification methods by discussing previously used digital biomarkers and algorithms and make recommendations for future research. METHODS On the 14th of September 2020, the PubMed database was searched for articles focusing on algorithms for AF detection in ECGs using the MeSH terms Atrial Fibrillation, Electrocardiography and Algorithms. Articles which solely focused on differentiation of types of rhythm disorders or prediction of AF termination were excluded. RESULTS The search resulted in 451 articles, of which 130 remained after full-text screening. Not only did the amount of research on methods for AF detection increase over the past years, but a trend towards more complex classification methods is observed. Furthermore, three different types of features can be distinguished: atrial features, ventricular features, and signal features. Although AF is an atrial disease, only 22% of the described methods use atrial features. CONCLUSION More and more studies focus on improving accuracy of classification methods for AF in ECGs. As a result, algorithms become increasingly complex and less well interpretable. Only a few studies focus on detecting atrial activity in the ECG. Developing innovative methods focusing on detection of atrial activity might provide accurate classifiers without compromising on transparency.
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Affiliation(s)
- Fons J Wesselius
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Mathijs S van Schie
- Department of Cardiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | | | - Richard C Hendriks
- Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, the Netherlands
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26
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Yang TY, Huang L, Malwade S, Hsu CY, Chen YC. Diagnostic Accuracy of Ambulatory Devices in Detecting Atrial Fibrillation: Systematic Review and Meta-analysis. JMIR Mhealth Uhealth 2021; 9:e26167. [PMID: 33835039 PMCID: PMC8065566 DOI: 10.2196/26167] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2020] [Revised: 02/07/2021] [Accepted: 03/11/2021] [Indexed: 12/14/2022] Open
Abstract
Background Atrial fibrillation (AF) is the most common cardiac arrhythmia worldwide. Early diagnosis of AF is crucial for preventing AF-related morbidity, mortality, and economic burden, yet the detection of the disease remains challenging. The 12-lead electrocardiogram (ECG) is the gold standard for the diagnosis of AF. Because of technological advances, ambulatory devices may serve as convenient screening tools for AF. Objective The objective of this review was to investigate the diagnostic accuracy of 2 relatively new technologies used in ambulatory devices, non-12-lead ECG and photoplethysmography (PPG), in detecting AF. We performed a meta-analysis to evaluate the diagnostic accuracy of non-12-lead ECG and PPG compared to the reference standard, 12-lead ECG. We also conducted a subgroup analysis to assess the impact of study design and participant recruitment on diagnostic accuracy. Methods This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. MEDLINE and EMBASE were systematically searched for articles published from January 1, 2015 to January 23, 2021. A bivariate model was used to pool estimates of sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the summary receiver operating curve (SROC) as the main diagnostic measures. Study quality was evaluated using the quality assessment of diagnostic accuracy studies (QUADAS-2) tool. Results Our search resulted in 16 studies using either non-12-lead ECG or PPG for detecting AF, comprising 3217 participants and 7623 assessments. The pooled estimates of sensitivity, specificity, PLR, NLR, and diagnostic odds ratio for the detection of AF were 89.7% (95% CI 83.2%-93.9%), 95.7% (95% CI 92.0%-97.7%), 20.64 (95% CI 10.10-42.15), 0.11 (95% CI 0.06-0.19), and 224.75 (95% CI 70.10-720.56), respectively, for the automatic interpretation of non-12-lead ECG measurements and 94.7% (95% CI 93.3%-95.8%), 97.6% (95% CI 94.5%-99.0%), 35.51 (95% CI 18.19-69.31), 0.05 (95% CI 0.04-0.07), and 730.79 (95% CI 309.33-1726.49), respectively, for the automatic interpretation of PPG measurements. Conclusions Both non-12-lead ECG and PPG offered high diagnostic accuracies for AF. Detection employing automatic analysis techniques may serve as a useful preliminary screening tool before administering a gold standard test, which generally requires competent physician analyses. Subgroup analysis indicated variations of sensitivity and specificity between studies that recruited low-risk and high-risk populations, warranting future validity tests in the general population. Trial Registration PROSPERO International Prospective Register of Systematic Reviews CRD42020179937; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=179937
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Affiliation(s)
- Tien Yun Yang
- School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan
| | - Li Huang
- Department of Family Medicine, Taipei Medical University Hospital, Taipei City, Taiwan
| | - Shwetambara Malwade
- International Center for Health Information Technology, Taipei Medical University, Taipei, Taiwan
| | - Chien-Yi Hsu
- Division of Cardiology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.,Division of Cardiology and Cardiovascular Research Center, Department of Internal Medicine, Taipei Medical University Hospital, Taipei, Taiwan
| | - Yang Ching Chen
- Department of Family Medicine, Taipei Medical University Hospital, Taipei City, Taiwan.,Department of Family Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei City, Taiwan
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Using machine learning to predict anticoagulation control in atrial fibrillation: A UK Clinical Practice Research Datalink study. INFORMATICS IN MEDICINE UNLOCKED 2021. [DOI: 10.1016/j.imu.2021.100688] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022] Open
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28
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Abstract
The atrial fibrillation (AF) is one of the most well-known cardiac arrhythmias in clinical practice, with a prevalence of 1–2% in the community, which can increase the risk of stroke and myocardial infarction. The detection of AF electrocardiogram (ECG) can improve the early detection of diagnosis. In this paper, we have further developed a framework for processing the ECG signal in order to determine the AF episodes. We have implemented machine learning and deep learning algorithms to detect AF. Moreover, the experimental results show that better performance can be achieved with long short-term memory (LSTM) as compared to other algorithms. The initial experimental results illustrate that the deep learning algorithms, such as LSTM and convolutional neural network (CNN), achieved better performance (10%) as compared to machine learning classifiers, such as support vectors, logistic regression, etc. This preliminary work can help clinicians in AF detection with high accuracy and less probability of errors, which can ultimately result in reduction in fatality rate.
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Huang YH, Alexeenko V, Tse G, Huang CLH, Marr CM, Jeevaratnam K. ECG Restitution Analysis and Machine Learning to Detect Paroxysmal Atrial Fibrillation: Insight from the Equine Athlete as a Model for Human Athletes. FUNCTION (OXFORD, ENGLAND) 2020; 2:zqaa031. [PMID: 35330977 PMCID: PMC8788737 DOI: 10.1093/function/zqaa031] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/11/2020] [Accepted: 11/13/2020] [Indexed: 01/06/2023]
Abstract
Atrial fibrillation is the most frequent arrhythmia in both equine and human athletes. Currently, this condition is diagnosed via electrocardiogram (ECG) monitoring which lacks sensitivity in about half of cases when it presents in paroxysmal form. We investigated whether the arrhythmogenic substrate present between the episodes of paroxysmal atrial fibrillation (PAF) can be detected using restitution analysis of normal sinus-rhythm ECGs. In this work, ECG recordings were obtained during routine clinical work from control and horses with PAF. The extracted QT, TQ, and RR intervals were used for ECG restitution analysis. The restitution data were trained and tested using k-nearest neighbor (k-NN) algorithm with various values of neighbors k to derive a discrimination tool. A combination of QT, RR, and TQ intervals was used to analyze the relationship between these intervals and their effects on PAF. A simple majority vote on individual record (one beat) classifications was used to determine the final classification. The k-NN classifiers using two-interval measures were able to predict the diagnosis of PAF with area under the receiving operating characteristic curve close to 0.8 (RR, TQ with k ≥ 9) and 0.9 (RR, QT with k ≥ 21 or TQ, QT with k ≥ 25). By simultaneously using all three intervals for each beat and a majority vote, mean area under the curves of 0.9 were obtained for all tested k-values (3-41). We concluded that 3D ECG restitution analysis can potentially be used as a metric of an automated method for screening of PAF.
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Affiliation(s)
- Ying H Huang
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
| | - Vadim Alexeenko
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK
| | - Gary Tse
- Tianjin Key Laboratory of Ionic-Molecular Function of Cardiovascular Disease, Department of Cardiology, Tianjin Institute of Cardiology, the Second Hospital of Tianjin Medical University, Tianjin, China
| | - Christopher L-H Huang
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK,Physiological Laboratory, University of Cambridge, Cambridge, CB2 1QW, UK
| | - Celia M Marr
- Rossdales Equine Hospital and Diagnostic Centre, Exning, CB8 7NN, Suffolk, UK
| | - Kamalan Jeevaratnam
- Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7AL, UK,Physiological Laboratory, University of Cambridge, Cambridge, CB2 1QW, UK,Address correspondence to K.J. (e-mail: )
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