Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform
It is expected that an automatic detection and classification algorithm for the abnormities of first heart sound (S1) can realize computer artificial intelligence diagnosis of some relative cardiovascular disease. Few studies have focused on the detection and classification of the abnormities of S1...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8848375/ |
| _version_ | 1819179158417178624 |
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| author | Haixia Li Yongfeng Ren Guojun Zhang Renxin Wang Jiangong Cui Wendong Zhang |
| author_facet | Haixia Li Yongfeng Ren Guojun Zhang Renxin Wang Jiangong Cui Wendong Zhang |
| author_sort | Haixia Li |
| collection | DOAJ |
| description | It is expected that an automatic detection and classification algorithm for the abnormities of first heart sound (S1) can realize computer artificial intelligence diagnosis of some relative cardiovascular disease. Few studies have focused on the detection and classification of the abnormities of S1 and given out in detail the essential differences between abnormal and normal S1. This work applied Empirical Wavelet Transform (EWT) to decompose S1 and extracted the instantaneous frequency (IF) of mitral component (M1) and tricuspid component (T1) by using Hilbert Transform. Firstly, the heart sound signal is preprocessed following these processes: filtering, resampling, normalization and segmentation. Secondly, S1 is decomposed into several modes based on EWT. First two maximal points with a distance greater than 20Hz in Fourier Spectrum of S1 are selected and the nearest minimal points on both sides of the maximal points are found out as the boundaries for segmentation of the spectrum. S1 is decomposed into 5 modes and every mode's IF are calculated through Hilbert transformation. At last, a k-mean cluster algorithm is applied to cluster the IF of different modes. TD and A<sub>peak_ratio</sub> are calculated for decision tree classifier and S1s are divided into three categories: normal S1, S1 with abnormal split and S1with abnormal amplitude change. When the proposed method is applied to detect normal S1, Se=94.6%, Pp=98.6% and Oa=93.3%; When it is applied to detect S1 with abnormal split, Se=92.6%, Pp=96.9% and Oa=90%; When it is applied to detect S1 with abnormal amplitude change, Se=94.4%, Pp=95.7% and Oa=90.6%; Comparison experiments are carried out between the proposed method and HVD method. The results show Oa of the proposed method is higher than HVD method when detecting the three different S1s. |
| first_indexed | 2024-12-22T21:54:00Z |
| format | Article |
| id | doaj.art-0024271098734988862969eb394507f6 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T21:54:00Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-0024271098734988862969eb394507f62022-12-21T18:11:16ZengIEEEIEEE Access2169-35362019-01-01713964313965210.1109/ACCESS.2019.29437058848375Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet TransformHaixia Li0https://orcid.org/0000-0003-0778-4322Yongfeng Ren1Guojun Zhang2Renxin Wang3https://orcid.org/0000-0002-3441-9286Jiangong Cui4Wendong Zhang5State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, ChinaIt is expected that an automatic detection and classification algorithm for the abnormities of first heart sound (S1) can realize computer artificial intelligence diagnosis of some relative cardiovascular disease. Few studies have focused on the detection and classification of the abnormities of S1 and given out in detail the essential differences between abnormal and normal S1. This work applied Empirical Wavelet Transform (EWT) to decompose S1 and extracted the instantaneous frequency (IF) of mitral component (M1) and tricuspid component (T1) by using Hilbert Transform. Firstly, the heart sound signal is preprocessed following these processes: filtering, resampling, normalization and segmentation. Secondly, S1 is decomposed into several modes based on EWT. First two maximal points with a distance greater than 20Hz in Fourier Spectrum of S1 are selected and the nearest minimal points on both sides of the maximal points are found out as the boundaries for segmentation of the spectrum. S1 is decomposed into 5 modes and every mode's IF are calculated through Hilbert transformation. At last, a k-mean cluster algorithm is applied to cluster the IF of different modes. TD and A<sub>peak_ratio</sub> are calculated for decision tree classifier and S1s are divided into three categories: normal S1, S1 with abnormal split and S1with abnormal amplitude change. When the proposed method is applied to detect normal S1, Se=94.6%, Pp=98.6% and Oa=93.3%; When it is applied to detect S1 with abnormal split, Se=92.6%, Pp=96.9% and Oa=90%; When it is applied to detect S1 with abnormal amplitude change, Se=94.4%, Pp=95.7% and Oa=90.6%; Comparison experiments are carried out between the proposed method and HVD method. The results show Oa of the proposed method is higher than HVD method when detecting the three different S1s.https://ieeexplore.ieee.org/document/8848375/First heart sound (S1)abnormitiesempirical wavelet transform (EWT)mitral component (M1)tricuspid component (T1)Instantaneous frequency (IF) |
| spellingShingle | Haixia Li Yongfeng Ren Guojun Zhang Renxin Wang Jiangong Cui Wendong Zhang Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform IEEE Access First heart sound (S1) abnormities empirical wavelet transform (EWT) mitral component (M1) tricuspid component (T1) Instantaneous frequency (IF) |
| title | Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform |
| title_full | Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform |
| title_fullStr | Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform |
| title_full_unstemmed | Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform |
| title_short | Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform |
| title_sort | detection and classification of abnormities of first heart sound using empirical wavelet transform |
| topic | First heart sound (S1) abnormities empirical wavelet transform (EWT) mitral component (M1) tricuspid component (T1) Instantaneous frequency (IF) |
| url | https://ieeexplore.ieee.org/document/8848375/ |
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