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Sun S, Song W, Tong Y, Li X, Zhao M, Deng Q, Liu G, Liu Z, Liu C. A novel methodology for evaluation of S 2 wide split via estimated parameters. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2023; 242:107777. [PMID: 37714021 DOI: 10.1016/j.cmpb.2023.107777] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/11/2021] [Revised: 07/25/2023] [Accepted: 08/22/2023] [Indexed: 09/17/2023]
Abstract
BACKGROUND AND OBJECTIVE Aimed at the shortcomings of using time interval ( [Formula: see text] ) between the sounds produced by the aortic valve closure (A2) and the pulmonary valve closure (P2) to detect the wide splitting of the second heart sound (S2), which are the [Formula: see text] easily influenced by the heartbeat and not easily distinguished from the fixed splitting of S2 without considering the entire respiratory phase, and from the third heart sound (S3), this study proposes a novel methodology to detect the wide splitting of S2 using an estimated split coefficient of S2 ( [Formula: see text] ) combined with an adaptive number (NAda) of S2. METHODOLOGY The methodology is orderly summarized as follows: Stage 1 describes the segmentation-based S2 automatic location and extraction. A Gaussian mixture model (GMM)-based regression model for S2 is proposed to estimate the positions of A2 and P2, then an overlapping rate (OLR)-based [Formula: see text] and the [Formula: see text] are estimated, and finally, a NAda-S2 is automatically determined to calculate the statistics of [Formula: see text] and [Formula: see text] . In stage 3, based on the combination of estimated features, the detection of wide splitting of S2 is determined. RESULTS The performance is evaluated using a total of 3350-period heart sounds from 72 patients, with an overall accuracy of 100%, F1=1 and a Cohen's kappa value (κ) of 1. DISCUSSION The significant contributions are highlighted: A novel GMM-based efficient methodology is proposed for estimating the characteristics of A2 and P2. A novel OLR-based [Formula: see text] is defined to replace the current state-of-the-art criterion for evaluating the split degree of S2. Considering respiration phases combined with CR are proposed for the high-precision diagnosis of S2 wide split.
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Affiliation(s)
- Shuping Sun
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China.
| | - Wei Song
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Yaonan Tong
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China.
| | - Xu Li
- School of Economic, Bohai University, Liaoning Jinzhou 121013, China
| | - Man Zhao
- School of Economic, Bohai University, Liaoning Jinzhou 121013, China
| | - Qi Deng
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Guangyu Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Zhi Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
| | - Chao Liu
- School of Information Science and Engineering, Hunan Institute of Science and Technology, Yueyang 414006, Hunan, China
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Stagg A, Giglia TM, Gardner MM, Shustak RJ, Natarajan SS, Hehir DA, Szwast AL, Rome JJ, Ravishankar C, Preminger TJ. Feasibility of Digital Stethoscopes in Telecardiology Visits for Interstage Monitoring in Infants with Palliated Congenital Heart Disease. Pediatr Cardiol 2023; 44:1702-1709. [PMID: 37285041 PMCID: PMC10246546 DOI: 10.1007/s00246-023-03198-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/18/2023] [Accepted: 05/25/2023] [Indexed: 06/08/2023]
Abstract
Infants with staged surgical palliation for congenital heart disease are at high-risk for interstage morbidity and mortality. Interstage telecardiology visits (TCV) have been effective in identifying clinical concerns and preventing unnecessary emergency department visits in this high-risk population. We aimed to assess the feasibility of implementing auscultation with digital stethoscopes (DSs) during TCV and the potential impact on interstage care in our Infant Single Ventricle Monitoring & Management Program. In addition to standard home-monitoring practice for TCV, caregivers received training on use of a DS (Eko CORE attachment assembled with Classic II Infant Littman stethoscope). Sound quality of the DS and comparability to in-person auscultation were evaluated based on two providers' subjective assessment. We also evaluated provider and caregiver acceptability of the DS. From 7/2021 to 6/2022, the DS was used during 52 TCVs in 16 patients (median TCVs/patient: 3; range: 1-8), including 7 with hypoplastic left heart syndrome. Quality of heart sounds and murmur auscultation were subjectively equivalent to in-person findings with excellent inter-rater agreement (98%). All providers and caregivers reported ease of use and confidence in evaluation with the DS. In 12% (6/52) of TCVs, the DS provided additional significant information compared to a routine TCV; this expedited life-saving care in two patients. There were no missed events or deaths. Use of a DS during TCV was feasible in this fragile cohort and effective in identifying clinical concerns with no missed events. Longer term use of this technology will further establish its role in telecardiology.
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Affiliation(s)
- Alyson Stagg
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Therese M Giglia
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Monique M Gardner
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Rachel J Shustak
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Shobha S Natarajan
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - David A Hehir
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Anita L Szwast
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jonathan J Rome
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chitra Ravishankar
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tamar J Preminger
- Children's Hospital of Philadelphia, 3401 Civic Center Blvd, Philadelphia, PA, 19401, USA.
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
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Huang DM, Huang J, Qiao K, Zhong NS, Lu HZ, Wang WJ. Deep learning-based lung sound analysis for intelligent stethoscope. Mil Med Res 2023; 10:44. [PMID: 37749643 PMCID: PMC10521503 DOI: 10.1186/s40779-023-00479-3] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 09/05/2023] [Indexed: 09/27/2023] Open
Abstract
Auscultation is crucial for the diagnosis of respiratory system diseases. However, traditional stethoscopes have inherent limitations, such as inter-listener variability and subjectivity, and they cannot record respiratory sounds for offline/retrospective diagnosis or remote prescriptions in telemedicine. The emergence of digital stethoscopes has overcome these limitations by allowing physicians to store and share respiratory sounds for consultation and education. On this basis, machine learning, particularly deep learning, enables the fully-automatic analysis of lung sounds that may pave the way for intelligent stethoscopes. This review thus aims to provide a comprehensive overview of deep learning algorithms used for lung sound analysis to emphasize the significance of artificial intelligence (AI) in this field. We focus on each component of deep learning-based lung sound analysis systems, including the task categories, public datasets, denoising methods, and, most importantly, existing deep learning methods, i.e., the state-of-the-art approaches to convert lung sounds into two-dimensional (2D) spectrograms and use convolutional neural networks for the end-to-end recognition of respiratory diseases or abnormal lung sounds. Additionally, this review highlights current challenges in this field, including the variety of devices, noise sensitivity, and poor interpretability of deep models. To address the poor reproducibility and variety of deep learning in this field, this review also provides a scalable and flexible open-source framework that aims to standardize the algorithmic workflow and provide a solid basis for replication and future extension: https://github.com/contactless-healthcare/Deep-Learning-for-Lung-Sound-Analysis .
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Affiliation(s)
- Dong-Min Huang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China
| | - Jia Huang
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Kun Qiao
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China
| | - Nan-Shan Zhong
- Guangzhou Institute of Respiratory Health, China State Key Laboratory of Respiratory Disease, National Clinical Research Center for Respiratory Disease, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, 510120, China.
| | - Hong-Zhou Lu
- The Third People's Hospital of Shenzhen, Shenzhen, 518112, Guangdong, China.
| | - Wen-Jin Wang
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, 518055, Guangdong, China.
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Ge B, Yang H, Ma P, Guo T, Pan J, Wang W. Detection of pulmonary hypertension associated with congenital heart disease based on time-frequency domain and deep learning features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104316] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Ge B, Yang H, Ma P, Guo T, Pan J, Wang W. Detection of pulmonary arterial hypertension associated with congenital heart disease based on time–frequency domain and deep learning features. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104451] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022]
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Wang M, Hu Y, Guo B, Tang H. Simulation of Acute Pulmonary Hypertension in Beagle Dogs. Int Heart J 2022; 63:612-622. [PMID: 35650161 DOI: 10.1536/ihj.21-676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Acoustic cardiography (AC) combined with heart sound (HS) recording and electrocardiogram (ECG) provides a noninvasive and inexpensive way to understand the electrical mechanical activity of the heart. Pulmonary artery stenosis can cause hemodynamic abnormalities that might lead to pulmonary hypertension (PH). In this paper, we examined the relationships between the acoustic characteristics of the AC and hemodynamic changes in a beagle dog model of PH.Four healthy beagle dogs were injected with the prostaglandin endoperoxide receptor agonist U-44069 to induce acute PH states. AC was employed to analyze the process of pre-PH, intra-PH, and post-PH. Right ventricular blood pressure (RVBP) was measured via right cardiac catheterization, an invasive method performed in parallel for comparative hemodynamic evaluation. As RVBP increased or decreased, the HS features changed accordingly during acute PH occurrence and development. Right ventricular systolic blood pressure (RVSBP) significantly correlated with the minimum of the first HS (S1) amplitude (correlation coefficient (CC) = -0.82), energy of the S1 (CC = 0.86), energy of the second HS (S2) (CC = 0.67), entropy of the S1 (CC = -0.94), and ratio of electromechanical systolic time (EMST) to the cardiac cycle time (CC = 0.81). The two techniques (AC [HSs and ECG] versus right cardiac catheterization [RVBP]) were significantly correlated. Especially, the diastolic filling time (DFT) had a significant relationship with the right ventricular diastolic time (RVDT) (CC = 0.97), perfusion time (PT) (CC = 0.96), and cardiac cycle time (RR) (CC = 0.96). The CCs between the RVDT and the max dp/dt to min dp/dt, the EMST and the Q to min dp/dt, and the electromechanical activation time and the Q to max dp/dt were 0.95, 0.99, and 0.86, respectively. Furthermore, the logistic regression model with different combinations was used to identify the effective features for monitoring hemodynamic and pathophysiologic conditions.AC provided significant insight into mechanical dysfunction in a rapid and noninvasive way that could be used for early screening of PH.
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Affiliation(s)
- Miao Wang
- School of Biomedical Engineering, Dalian University of Technology
| | - YaTing Hu
- School of Biomedical Engineering, Dalian University of Technology
| | - BinBin Guo
- School of Biomedical Engineering, Dalian University of Technology
| | - Hong Tang
- School of Biomedical Engineering, Dalian University of Technology
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Wang M, Wang J, Hu Y, Guo B, Tang H. Detection of pulmonary hypertension with six training strategies based on deep learning technology. Comput Intell 2022. [DOI: 10.1111/coin.12527] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Affiliation(s)
- Miao Wang
- School of Biomedical Engineering Dalian University of Technology Dalian China
| | - JiWen Wang
- Cardiovascular Department The Second Hospital of DaLian Medical University Dalian China
| | - YaTing Hu
- School of Biomedical Engineering Dalian University of Technology Dalian China
| | - BinBin Guo
- School of Biomedical Engineering Dalian University of Technology Dalian China
| | - Hong Tang
- School of Biomedical Engineering Dalian University of Technology Dalian China
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Hurley NC, Spatz ES, Krumholz HM, Jafari R, Mortazavi BJ. A Survey of Challenges and Opportunities in Sensing and Analytics for Risk Factors of Cardiovascular Disorders. ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2021; 2:9. [PMID: 34337602 PMCID: PMC8320445 DOI: 10.1145/3417958] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 08/01/2020] [Indexed: 10/22/2022]
Abstract
Cardiovascular disorders cause nearly one in three deaths in the United States. Short- and long-term care for these disorders is often determined in short-term settings. However, these decisions are made with minimal longitudinal and long-term data. To overcome this bias towards data from acute care settings, improved longitudinal monitoring for cardiovascular patients is needed. Longitudinal monitoring provides a more comprehensive picture of patient health, allowing for informed decision making. This work surveys sensing and machine learning in the field of remote health monitoring for cardiovascular disorders. We highlight three needs in the design of new smart health technologies: (1) need for sensing technologies that track longitudinal trends of the cardiovascular disorder despite infrequent, noisy, or missing data measurements; (2) need for new analytic techniques designed in a longitudinal, continual fashion to aid in the development of new risk prediction techniques and in tracking disease progression; and (3) need for personalized and interpretable machine learning techniques, allowing for advancements in clinical decision making. We highlight these needs based upon the current state of the art in smart health technologies and analytics. We then discuss opportunities in addressing these needs for development of smart health technologies for the field of cardiovascular disorders and care.
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Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10165466] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) ± standard deviation (SD) inside record was 1.85 ± 1.82 mmHg. It was 4.37 ± 2.49 mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE ± SD from 12.46 ± 6.56 mmHg dropped to 6.37 ± 4.90 mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.
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Ramanathan A, Zhou L, Marzbanrad F, Roseby R, Tan K, Kevat A, Malhotra A. Digital stethoscopes in paediatric medicine. Acta Paediatr 2019; 108:814-822. [PMID: 30536440 DOI: 10.1111/apa.14686] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 11/29/2018] [Accepted: 12/04/2018] [Indexed: 12/30/2022]
Abstract
AIM To explore, synthesise and discuss currently available digital stethoscopes (DS) and the evidence for their use in paediatric medicine. METHODS Systematic review and narrative synthesis of digital stethoscope use in paediatrics following searches of OVID Medline, Embase, Scopus, PubMed and Google Scholar databases. RESULTS Six digital stethoscope makes were identified to have been used in paediatric focused studies so far. A total of 25 studies of DS use in paediatrics were included. We discuss the use of digital stethoscope technology in current paediatric medicine, comment on the technical properties of the available devices, the effectiveness and limitations of this technology, and potential uses in the fields of paediatrics and neonatology, from telemedicine to computer-aided diagnostics. CONCLUSION Further validation and testing of available DS devices is required. Comparison studies between different types of DS would be useful in identifying strengths and flaws of each DS as well as identifying clinical situations for which each may be most appropriately suited.
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Affiliation(s)
| | - Lindsay Zhou
- Monash Newborn Monash Children's Hospital Melbourne VIC Australia
| | - Faezeh Marzbanrad
- Department of Electrical and Computer Systems Engineering Monash University Melbourne VIC Australia
| | - Robert Roseby
- Department of Paediatrics Monash University Melbourne VIC Australia
- Department of Paediatric Respiratory Medicine Monash Children's Hospital Melbourne VIC Australia
| | - Kenneth Tan
- Department of Paediatrics Monash University Melbourne VIC Australia
- Monash Newborn Monash Children's Hospital Melbourne VIC Australia
- The Ritchie Centre Hudson Institute of Medical Research Melbourne VIC Australia
| | - Ajay Kevat
- Department of Paediatrics Monash University Melbourne VIC Australia
| | - Atul Malhotra
- Department of Paediatrics Monash University Melbourne VIC Australia
- Monash Newborn Monash Children's Hospital Melbourne VIC Australia
- The Ritchie Centre Hudson Institute of Medical Research Melbourne VIC Australia
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11
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Tang H, Jiang Y, Li T, Wang X. Identification of Pulmonary Hypertension Using Entropy Measure Analysis of Heart Sound Signal. ENTROPY 2018; 20:e20050389. [PMID: 33265479 PMCID: PMC7512907 DOI: 10.3390/e20050389] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 05/16/2018] [Accepted: 05/19/2018] [Indexed: 11/16/2022]
Abstract
This study introduced entropy measures to analyze the heart sound signals of people with and without pulmonary hypertension (PH). The lead II Electrocardiography (ECG) signal and heart sound signal were simultaneously collected from 104 subjects aged between 22 and 89. Fifty of them were PH patients and 54 were healthy. Eleven heart sound features were extracted and three entropy measures, namely sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) of the feature sequences were calculated. The Mann–Whitney U test was used to study the feature significance between the patient and health group. To reduce the age confounding factor, nine entropy measures were selected based on correlation analysis. Further, the probability density function (pdf) of a single selected entropy measure of both groups was constructed by kernel density estimation, as well as the joint pdf of any two and multiple selected entropy measures. Therefore, a patient or a healthy subject can be classified using his/her entropy measure probability based on Bayes’ decision rule. The results showed that the best identification performance by a single selected measure had sensitivity of 0.720 and specificity of 0.648. The identification performance was improved to 0.680, 0.796 by the joint pdf of two measures and 0.740, 0.870 by the joint pdf of multiple measures. This study showed that entropy measures could be a powerful tool for early screening of PH patients.
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Affiliation(s)
- Hong Tang
- Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
- Correspondence: ; Tel.: +86-411-8470-6009 (ext. 3013)
| | - Yuanlin Jiang
- Department of Biomedical Engineering, Dalian University of Technology, Dalian 116024, China
| | - Ting Li
- College of Information and Communication Engineering, Dalian Minzu University, Dalian 116024, China
| | - Xinpei Wang
- School of Control Science and Engineering, Shandong University, Jinan 250100, China
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Elgendi M, Bobhate P, Jain S, Guo L, Rutledge J, Coe Y, Zemp R, Schuurmans D, Adatia I. The Voice of the Heart: Vowel-Like Sound in Pulmonary Artery Hypertension. Diseases 2018; 6:E26. [PMID: 29652794 PMCID: PMC6023489 DOI: 10.3390/diseases6020026] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2018] [Revised: 04/05/2018] [Accepted: 04/10/2018] [Indexed: 11/16/2022] Open
Abstract
Increased blood pressure in the pulmonary artery is referred to as pulmonary hypertension and often is linked to loud pulmonic valve closures. For the purpose of this paper, it was hypothesized that pulmonary circulation vibrations will create sounds similar to sounds created by vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) could have unique sound signatures across four auscultatory sites. Using a digital stethoscope, heart sounds were recorded at the cardiac apex, 2nd left intercostal space (2LICS), 2nd right intercostal space (2RICS), and 4th left intercostal space (4LICS) undergoing simultaneous cardiac catheterization. From the collected heart sounds, relative power of the frequency band, energy of the sinusoid formants, and entropy were extracted. PAH subjects were differentiated by applying the linear discriminant analysis with leave-one-out cross-validation. The entropy of the first sinusoid formant decreased significantly in subjects with a mean pulmonary artery pressure (mPAp) ≥ 25 mmHg versus subjects with a mPAp < 25 mmHg with a sensitivity of 84% and specificity of 88.57%, within a 10-s optimized window length for heart sounds recorded at the 2LICS. First sinusoid formant entropy reduction of heart sounds in PAH subjects suggests the existence of a vowel-like pattern. Pattern analysis revealed a unique sound signature, which could be used in non-invasive screening tools.
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Affiliation(s)
- Mohamed Elgendi
- Department of Obstetrics & Gynecology, University of British Columbia and BC Children's & Women's Hospital, Vancouver, BC V6H 3N1, Canada.
- School of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada.
| | - Prashant Bobhate
- Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB T6G 2B7, Canada.
| | - Shreepal Jain
- Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB T6G 2B7, Canada.
| | - Long Guo
- Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB T6G 2B7, Canada.
| | - Jennifer Rutledge
- Division of Cardiology at Alberta Children's Hospital, Calgary, AB T3B 6A8, Canada.
| | - Yashu Coe
- Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB T6G 2B7, Canada.
- Mazankowski Alberta Heart Institute, Edmonton, AB T6G 2B7, Canada.
| | - Roger Zemp
- School of Biomedical Engineering, University of Alberta, Edmonton, AB T6G 2V2, Canada.
| | - Dale Schuurmans
- Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
| | - Ian Adatia
- Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB T6G 2B7, Canada.
- Mazankowski Alberta Heart Institute, Edmonton, AB T6G 2B7, Canada.
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13
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Kaddoura T, Vadlamudi K, Kumar S, Bobhate P, Guo L, Jain S, Elgendi M, Coe JY, Kim D, Taylor D, Tymchak W, Schuurmans D, Zemp RJ, Adatia I. Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians. Sci Rep 2016; 6:33182. [PMID: 27609672 PMCID: PMC5016849 DOI: 10.1038/srep33182] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2016] [Accepted: 08/19/2016] [Indexed: 12/14/2022] Open
Abstract
We hypothesized that an automated speech- recognition-inspired classification algorithm could differentiate between the heart sounds in subjects with and without pulmonary hypertension (PH) and outperform physicians. Heart sounds, electrocardiograms, and mean pulmonary artery pressures (mPAp) were recorded simultaneously. Heart sound recordings were digitized to train and test speech-recognition-inspired classification algorithms. We used mel-frequency cepstral coefficients to extract features from the heart sounds. Gaussian-mixture models classified the features as PH (mPAp ≥ 25 mmHg) or normal (mPAp < 25 mmHg). Physicians blinded to patient data listened to the same heart sound recordings and attempted a diagnosis. We studied 164 subjects: 86 with mPAp ≥ 25 mmHg (mPAp 41 ± 12 mmHg) and 78 with mPAp < 25 mmHg (mPAp 17 ± 5 mmHg) (p < 0.005). The correct diagnostic rate of the automated speech-recognition-inspired algorithm was 74% compared to 56% by physicians (p = 0.005). The false positive rate for the algorithm was 34% versus 50% (p = 0.04) for clinicians. The false negative rate for the algorithm was 23% and 68% (p = 0.0002) for physicians. We developed an automated speech-recognition-inspired classification algorithm for the acoustic diagnosis of PH that outperforms physicians that could be used to screen for PH and encourage earlier specialist referral.
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Affiliation(s)
- Tarek Kaddoura
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Karunakar Vadlamudi
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
| | - Shine Kumar
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
| | - Prashant Bobhate
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
| | - Long Guo
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
| | - Shreepal Jain
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
| | - Mohamed Elgendi
- Department Computing Science, University of Alberta, Edmonton, Canada
| | - James Y Coe
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
| | - Daniel Kim
- Department of Medicine, Division of Cardiology, Cardiac Catheterization Laboratories, University of Alberta Hospital, Mazankowski Alberta Heart Institute, Edmonton, Canada
| | - Dylan Taylor
- Department of Medicine, Division of Cardiology, Cardiac Catheterization Laboratories, University of Alberta Hospital, Mazankowski Alberta Heart Institute, Edmonton, Canada
| | - Wayne Tymchak
- Department of Medicine, Division of Cardiology, Cardiac Catheterization Laboratories, University of Alberta Hospital, Mazankowski Alberta Heart Institute, Edmonton, Canada
| | - Dale Schuurmans
- Department Computing Science, University of Alberta, Edmonton, Canada
| | - Roger J Zemp
- Department of Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Ian Adatia
- Pediatric Pulmonary Hypertension Service, Pediatric Cardiac Critical Care Unit, Stollery Children's Hospital, Mazankowski Alberta Heart Institute, University of Alberta, Edmonton, Canada
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14
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Elgendi M, Bobhate P, Jain S, Guo L, Kumar S, Rutledge J, Coe Y, Zemp R, Schuurmans D, Adatia I. The unique heart sound signature of children with pulmonary artery hypertension. Pulm Circ 2015; 5:631-9. [PMID: 26697170 DOI: 10.1086/683694] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/13/2023] Open
Abstract
We hypothesized that vibrations created by the pulmonary circulation would create sound like the vocal cords during speech and that subjects with pulmonary artery hypertension (PAH) might have a unique sound signature. We recorded heart sounds at the cardiac apex and the second left intercostal space (2LICS), using a digital stethoscope, from 27 subjects (12 males) with a median age of 7 years (range: 3 months-19 years) undergoing simultaneous cardiac catheterization. Thirteen subjects had mean pulmonary artery pressure (mPAp) < 25 mmHg (range: 8-24 mmHg). Fourteen subjects had mPAp ≥ 25 mmHg (range: 25-97 mmHg). We extracted the relative power of the frequency band, the entropy, and the energy of the sinusoid formants from the heart sounds. We applied linear discriminant analysis with leave-one-out cross validation to differentiate children with and without PAH. The significance of the results was determined with a t test and a rank-sum test. The entropy of the first sinusoid formant contained within an optimized window length of 2 seconds of the heart sounds recorded at the 2LICS was significantly lower in subjects with mPAp ≥ 25 mmHg relative to subjects with mPAp < 25 mmHg, with a sensitivity of 93% and specificity of 92%. The reduced entropy of the first sinusoid formant of the heart sounds in children with PAH suggests the existence of an organized pattern. The analysis of this pattern revealed a unique sound signature, which could be applied to a noninvasive method to diagnose PAH.
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Affiliation(s)
- Mohamed Elgendi
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada ; Current address: Electrical and Computer Engineering in Medicine Group, University of British Columbia, and British Columbia Children's Hospital, Vancouver, British Columbia, Canada
| | - Prashant Bobhate
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada
| | - Shreepal Jain
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada
| | - Long Guo
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada
| | - Shine Kumar
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada
| | - Jennifer Rutledge
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada ; Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada
| | - Yashu Coe
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada ; Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada
| | - Roger Zemp
- School of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| | - Dale Schuurmans
- Department of Computing Science, University of Alberta, Edmonton, Alberta, Canada
| | - Ian Adatia
- Department of Pediatrics and Pediatric Pulmonary Hypertension Service, Stollery Children's Hospital, University of Alberta, Edmonton, Alberta, Canada ; Mazankowski Alberta Heart Institute, Edmonton, Alberta, Canada
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15
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Elgendi M, Kumar S, Guo L, Rutledge J, Coe JY, Zemp R, Schuurmans D, Adatia I. Detection of Heart Sounds in Children with and without Pulmonary Arterial Hypertension--Daubechies Wavelets Approach. PLoS One 2015; 10:e0143146. [PMID: 26629704 PMCID: PMC4668061 DOI: 10.1371/journal.pone.0143146] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/25/2015] [Accepted: 10/30/2015] [Indexed: 11/18/2022] Open
Abstract
Background Automatic detection of the 1st (S1) and 2nd (S2) heart sounds is difficult, and existing algorithms are imprecise. We sought to develop a wavelet-based algorithm for the detection of S1 and S2 in children with and without pulmonary arterial hypertension (PAH). Method Heart sounds were recorded at the second left intercostal space and the cardiac apex with a digital stethoscope simultaneously with pulmonary arterial pressure (PAP). We developed a Daubechies wavelet algorithm for the automatic detection of S1 and S2 using the wavelet coefficient ‘D6’ based on power spectral analysis. We compared our algorithm with four other Daubechies wavelet-based algorithms published by Liang, Kumar, Wang, and Zhong. We annotated S1 and S2 from an audiovisual examination of the phonocardiographic tracing by two trained cardiologists and the observation that in all subjects systole was shorter than diastole. Results We studied 22 subjects (9 males and 13 females, median age 6 years, range 0.25–19). Eleven subjects had a mean PAP < 25 mmHg. Eleven subjects had PAH with a mean PAP ≥ 25 mmHg. All subjects had a pulmonary artery wedge pressure ≤ 15 mmHg. The sensitivity (SE) and positive predictivity (+P) of our algorithm were 70% and 68%, respectively. In comparison, the SE and +P of Liang were 59% and 42%, Kumar 19% and 12%, Wang 50% and 45%, and Zhong 43% and 53%, respectively. Our algorithm demonstrated robustness and outperformed the other methods up to a signal-to-noise ratio (SNR) of 10 dB. For all algorithms, detection errors arose from low-amplitude peaks, fast heart rates, low signal-to-noise ratio, and fixed thresholds. Conclusion Our algorithm for the detection of S1 and S2 improves the performance of existing Daubechies-based algorithms and justifies the use of the wavelet coefficient ‘D6’ through power spectral analysis. Also, the robustness despite ambient noise may improve real world clinical performance.
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Affiliation(s)
- Mohamed Elgendi
- Department of Mathematics and Computing Science, University of Alberta, Edmonton, Canada
| | - Shine Kumar
- Pediatric Pulmonary Hypertension Service and Cardiac Critical Care, Stollery children’s Hospital, Mazankowski Heart Institute, University of Alberta, Edmonton, Canada
| | - Long Guo
- Pediatric Pulmonary Hypertension Service and Cardiac Critical Care, Stollery children’s Hospital, Mazankowski Heart Institute, University of Alberta, Edmonton, Canada
| | - Jennifer Rutledge
- Department of Pediatrics, Stollery Children’s Hospital, University of Alberta, Edmonton, Canada
| | - James Y. Coe
- Department of Pediatrics, Stollery Children’s Hospital, University of Alberta, Edmonton, Canada
| | - Roger Zemp
- Department of Biomedical Electrical and Computer Engineering, University of Alberta, Edmonton, Canada
| | - Dale Schuurmans
- Department of Mathematics and Computing Science, University of Alberta, Edmonton, Canada
| | - Ian Adatia
- Pediatric Pulmonary Hypertension Service and Cardiac Critical Care, Stollery children’s Hospital, Mazankowski Heart Institute, University of Alberta, Edmonton, Canada
- Department of Pediatrics, Stollery Children’s Hospital, University of Alberta, Edmonton, Canada
- * E-mail:
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