Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals
This paper proposes an intelligent stethoscope system that synchronously displays the electrocardiogram (ECG) and heart sound. The instrument, which accelerates auscultation, can be used for the diagnosis of valvular heart disease (VHD) for clinical physicians. The whole system with ECG patch and st...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10122547/ |
| _version_ | 1797823418679164928 |
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| author | Shuenn-Yuh Lee Po-Han Su Yi-Ting Hsieh Sheng-Hsin Huang I-Pei Lee Ju-Yi Chen |
| author_facet | Shuenn-Yuh Lee Po-Han Su Yi-Ting Hsieh Sheng-Hsin Huang I-Pei Lee Ju-Yi Chen |
| author_sort | Shuenn-Yuh Lee |
| collection | DOAJ |
| description | This paper proposes an intelligent stethoscope system that synchronously displays the electrocardiogram (ECG) and heart sound. The instrument, which accelerates auscultation, can be used for the diagnosis of valvular heart disease (VHD) for clinical physicians. The whole system with ECG patch and stethoscope includes four parts, namely, an analog front-end circuit for bio-signal acquisition, a heart sound-classifying integrated circuit with convolution neural network (CNN), a user-friendly application that synchronously displays the heart sound and ECG signals, and a cloud server with heart murmur detection algorithm for human study. In this system, three algorithms are used in processing both ECG and heart sound signals. The first algorithm is a synchronized algorithm, which can align heart sound and ECG signals simultaneously. The second algorithm is a heart sound-classifying algorithm that can distinguish the first (S1) and the second (S2) heart sound in heart sound signals for identifying the systolic and diastolic phases. The accuracies of the algorithm applied to normal heart sound and heart murmur are 100% and 96.7%, respectively. The third algorithm is heart murmur identification, which can detect systolic murmur and has a macro f1 score of 92.5%. The three algorithms proposed are beneficial for physicians in the diagnosis of VHD. After the establishment of the whole system, a CNN-based classification algorithm is also implemented with a <inline-formula> <tex-math notation="LaTeX">$0.18 \mu \text{m}$ </tex-math></inline-formula> standard CMOS process for the demonstration of the edge computing. The machine learning techniques are implemented on the chip to accelerate the classification process. |
| first_indexed | 2024-03-13T10:23:44Z |
| format | Article |
| id | doaj.art-504a55b67074448fb63c1711d9ada94b |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-13T10:23:44Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-504a55b67074448fb63c1711d9ada94b2023-05-19T23:01:09ZengIEEEIEEE Access2169-35362023-01-0111474204743110.1109/ACCESS.2023.327501510122547Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram SignalsShuenn-Yuh Lee0https://orcid.org/0000-0002-9757-1410Po-Han Su1https://orcid.org/0000-0002-2633-1014Yi-Ting Hsieh2Sheng-Hsin Huang3I-Pei Lee4Ju-Yi Chen5https://orcid.org/0000-0003-2760-9978Department of Electrical Engineering, National Cheng Kung University, Tainan City, TaiwanDepartment of Electrical Engineering, National Cheng Kung University, Tainan City, TaiwanDepartment of Electrical Engineering, National Cheng Kung University, Tainan City, TaiwanDepartment of Electrical Engineering, National Cheng Kung University, Tainan City, TaiwanNational Cheng Kung University Hospital, National Cheng Kung University, Tainan City, TaiwanDepartment of Internal Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan City, TaiwanThis paper proposes an intelligent stethoscope system that synchronously displays the electrocardiogram (ECG) and heart sound. The instrument, which accelerates auscultation, can be used for the diagnosis of valvular heart disease (VHD) for clinical physicians. The whole system with ECG patch and stethoscope includes four parts, namely, an analog front-end circuit for bio-signal acquisition, a heart sound-classifying integrated circuit with convolution neural network (CNN), a user-friendly application that synchronously displays the heart sound and ECG signals, and a cloud server with heart murmur detection algorithm for human study. In this system, three algorithms are used in processing both ECG and heart sound signals. The first algorithm is a synchronized algorithm, which can align heart sound and ECG signals simultaneously. The second algorithm is a heart sound-classifying algorithm that can distinguish the first (S1) and the second (S2) heart sound in heart sound signals for identifying the systolic and diastolic phases. The accuracies of the algorithm applied to normal heart sound and heart murmur are 100% and 96.7%, respectively. The third algorithm is heart murmur identification, which can detect systolic murmur and has a macro f1 score of 92.5%. The three algorithms proposed are beneficial for physicians in the diagnosis of VHD. After the establishment of the whole system, a CNN-based classification algorithm is also implemented with a <inline-formula> <tex-math notation="LaTeX">$0.18 \mu \text{m}$ </tex-math></inline-formula> standard CMOS process for the demonstration of the edge computing. The machine learning techniques are implemented on the chip to accelerate the classification process.https://ieeexplore.ieee.org/document/10122547/Bio-signal acquisitioncardiac auscultationelectrocardiogramheart soundmachine learningapplication software |
| spellingShingle | Shuenn-Yuh Lee Po-Han Su Yi-Ting Hsieh Sheng-Hsin Huang I-Pei Lee Ju-Yi Chen Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals IEEE Access Bio-signal acquisition cardiac auscultation electrocardiogram heart sound machine learning application software |
| title | Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals |
| title_full | Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals |
| title_fullStr | Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals |
| title_full_unstemmed | Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals |
| title_short | Intelligent Stethoscope System and Diagnosis Platform With Synchronized Heart Sound and Electrocardiogram Signals |
| title_sort | intelligent stethoscope system and diagnosis platform with synchronized heart sound and electrocardiogram signals |
| topic | Bio-signal acquisition cardiac auscultation electrocardiogram heart sound machine learning application software |
| url | https://ieeexplore.ieee.org/document/10122547/ |
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