Jae-Man Shin , Seongyong Park , Keewon Shin , Woo-Young Seo , Hyun-Seok Kim , Dong-Kyu Kim , Baehun Moon , Seul-Gi Cha , Won-Jung Shin , Sung-Hoon Kim
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A probabilistic representation of the pathological state was first extracted from segmented PCG signals using a TCN-based model. These segment-level representations were subsequently averaged to generate record- or patient-level features. The framework was designed to accommodate recordings of varying durations and different auscultation locations. Furthermore, we addressed domain adaptation challenges in cardiac abnormality detection by incorporating transfer learning techniques.</div></div><div><h3>Results</h3><div>The proposed method was evaluated using two large, independent public PCG datasets, demonstrating robust performance at both record and patient levels. While its initial performance on an unseen external dataset was modest, likely due to demographic characteristics and signal acquisition, transfer learning significantly improved the model's performance, yielding an area under the receiver operating characteristic curve of 0.931±0.027 and an area under the precision-recall curve of 0.867±0.064 in external validation. Combining internal and external datasets further enhanced model generalizability.</div></div><div><h3>Conclusion</h3><div>This proposed framework accommodates multi-channel, variable-length PCG recordings, making it a flexible and accurate solution for detecting pediatric cardiac abnormalities, particularly in low-resource settings. The source code is publicly available on Github (<span><span>https://github.com/baporlab/pcg_pathological_murmur_detection</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":"269 ","pages":"Article 108871"},"PeriodicalIF":4.8000,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Temporal convolutional neural network-based feature extraction and asynchronous channel information fusion method for heart abnormality detection in phonocardiograms\",\"authors\":\"Jae-Man Shin , Seongyong Park , Keewon Shin , Woo-Young Seo , Hyun-Seok Kim , Dong-Kyu Kim , Baehun Moon , Seul-Gi Cha , Won-Jung Shin , Sung-Hoon Kim\",\"doi\":\"10.1016/j.cmpb.2025.108871\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Objective</h3><div>Auscultation-based cardiac abnormality detection is valuable screening approach in pediatric populations, particularly in resource-limited settings. However, its clinical utility is often limited by phonocardiogram (PCG) signal variability and a difficulty in distinguishing between pathological and innocent murmurs.</div></div><div><h3>Methods</h3><div>We proposed a framework that leverages temporal convolutional network (TCN)-based feature extraction and information fusion to integrate asynchronously acquired PCG recordings at the patient level. A probabilistic representation of the pathological state was first extracted from segmented PCG signals using a TCN-based model. These segment-level representations were subsequently averaged to generate record- or patient-level features. The framework was designed to accommodate recordings of varying durations and different auscultation locations. Furthermore, we addressed domain adaptation challenges in cardiac abnormality detection by incorporating transfer learning techniques.</div></div><div><h3>Results</h3><div>The proposed method was evaluated using two large, independent public PCG datasets, demonstrating robust performance at both record and patient levels. While its initial performance on an unseen external dataset was modest, likely due to demographic characteristics and signal acquisition, transfer learning significantly improved the model's performance, yielding an area under the receiver operating characteristic curve of 0.931±0.027 and an area under the precision-recall curve of 0.867±0.064 in external validation. Combining internal and external datasets further enhanced model generalizability.</div></div><div><h3>Conclusion</h3><div>This proposed framework accommodates multi-channel, variable-length PCG recordings, making it a flexible and accurate solution for detecting pediatric cardiac abnormalities, particularly in low-resource settings. 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Temporal convolutional neural network-based feature extraction and asynchronous channel information fusion method for heart abnormality detection in phonocardiograms
Background and Objective
Auscultation-based cardiac abnormality detection is valuable screening approach in pediatric populations, particularly in resource-limited settings. However, its clinical utility is often limited by phonocardiogram (PCG) signal variability and a difficulty in distinguishing between pathological and innocent murmurs.
Methods
We proposed a framework that leverages temporal convolutional network (TCN)-based feature extraction and information fusion to integrate asynchronously acquired PCG recordings at the patient level. A probabilistic representation of the pathological state was first extracted from segmented PCG signals using a TCN-based model. These segment-level representations were subsequently averaged to generate record- or patient-level features. The framework was designed to accommodate recordings of varying durations and different auscultation locations. Furthermore, we addressed domain adaptation challenges in cardiac abnormality detection by incorporating transfer learning techniques.
Results
The proposed method was evaluated using two large, independent public PCG datasets, demonstrating robust performance at both record and patient levels. While its initial performance on an unseen external dataset was modest, likely due to demographic characteristics and signal acquisition, transfer learning significantly improved the model's performance, yielding an area under the receiver operating characteristic curve of 0.931±0.027 and an area under the precision-recall curve of 0.867±0.064 in external validation. Combining internal and external datasets further enhanced model generalizability.
Conclusion
This proposed framework accommodates multi-channel, variable-length PCG recordings, making it a flexible and accurate solution for detecting pediatric cardiac abnormalities, particularly in low-resource settings. The source code is publicly available on Github (https://github.com/baporlab/pcg_pathological_murmur_detection).
期刊介绍:
To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine.
Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.