Design of a high SNR electronic heart sound sensor based on a MEMS bionic hydrophone

Li, Haixia; Ren, Yong; Zhang, Guojun; Wang, Renxin; Zhang, Xiaoyong; Zhang, Ting; Zhang, Lansheng; Cui, Jiangong; Xu, Qingda; Duan, Sicun

doi:10.1063/1.5062619

Cited by 25 publications

(25 citation statements)

References 21 publications

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“…The advanced equipment can collect the signal with high SNR (such as the MEMS electronic stethoscope), thus greatly reducing the difficulty of signal de-noising algorithm. In this paper, the heart sound sensor of the MEMS stethoscope was designed with the aid of the hydrophone which imitated the fish lateral line structure [19]. Because a kind of medical coupling solvent whose density is approximate to the human's blood was used to encapsulate the heart sound sensor, the attenuation of the heart sound signal passing to the sensor's micro-structure via human tissue reduced greatly.…”

Section: A Wavelet Threshold Denoisingmentioning

confidence: 99%

“…Therefore, its sensitivity and SNR has been further improved and its SNR is higher than 3M Littmann 3200 stethoscope 8.2 db. Literature [19] have presented the design and manufacturing process of the MEMS electronic stethoscope in detail. Heart sound data with high SNR can greatly reduce the difficulty of data processing and improve the accuracy of data analysis results.…”

Section: A Wavelet Threshold Denoisingmentioning

confidence: 99%

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Discrimination of the Diastolic Murmurs in Coronary Heart Disease and in Valvular Disease

Ren

et al. 2020

IEEE Access

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Previous studies have shown that the acoustic characteristics of coronary heart disease (CHD) are the diastolic turbulent murmurs. But there is little research on how to distinguish the differences between diastolic turbulent murmurs and diastolic valvular murmurs. In order to improve the diagnosis accuracy of CHD based on the analysis of diastolic murmurs, empirical wavelet transform was applied to distinguish the difference of diastolic murmurs between in CHD and in valvular disease. Firstly, the spectrum of diastolic heart sounds was divided into three modes and the segmentation boundaries were set as [150, 200] Hz. Next, the diastolic modal spectrums were obtained. It is found that the essential difference of diastolic murmurs between in CHD and in mitral stenosis is the third modal spectrum. Finally, the characteristic parameter P3 was defined and used to distinguish the diastolic murmurs in CHD and in valvular disease. The performance of the proposed method is tested and the results show when the proposed method was used to identify the diastolic murmurs of CHD, Se is 94.7%, Pp is 93.4% and Oa is 88.7%; When the proposed method identifying the diastolic murmurs of valvular disease, Se is 93.3%, Pp is 94.6% and Oa is 88.6%. INDEX TERMS Diastolic murmurs, coronary heart disease (CHD), valvular disease, empirical wavelet transform (EWT).

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Section: A Wavelet Threshold Denoisingmentioning

confidence: 99%

Section: A Wavelet Threshold Denoisingmentioning

confidence: 99%

Discrimination of the Diastolic Murmurs in Coronary Heart Disease and in Valvular Disease

Ren

et al. 2020

IEEE Access

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“…Some of the heart sound data used in this paper are collected from website and others are collected by the MEMS electronic stethoscope made in our laboratory and 3M littmann 3200 electronic stethoscope [1]. The proposed detection method of abnormities of S1 includes several steps: preprocessing, decomposition of S1 based on EWT, discrimination M1 and T1 from IF and classification of S1 into three classes.…”

Section: Systtem Overviewmentioning

confidence: 99%

“…Due to the different amplification coefficients adopted by different stethoscope, the amplitude difference of heart sound data collected from a same patient is very large. In order to improve the robustness of the algorithm, pure heart sound data consistency needs to be normalized by using (1). The envelopes of several continuous cycles of pure heart sounds are obtained through Shannon Envelope Extraction.…”

Section: Systtem Overviewmentioning

confidence: 99%

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Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform

Ren

Zhang

et al. 2019

IEEE Access

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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 peak_ratio 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. INDEX TERMSFirst heart sound (S1), abnormities, empirical wavelet transform (EWT), mitral component (M1), tricuspid component (T1), Instantaneous frequency (IF).

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Optimum Design and Test of a Novel Bionic Electronic Stethoscope based on the Cruciform Microcantilever with Leaf Microelectromechanical Systems Structure

Zang

Zhou

Xiang

et al. 2022

Adv Materials Technologies

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This paper proposes a heart sound sensing structure and system by combining the traditional auscultation structure and human‐like ear eardrum gas–solid coupling sound conduction process. This system is based on the lever pickup mechanism of eardrum vibration and the traditional stethoscope pickup amplification, microelectromechanical system (MEMS) technology, and the detection principle of piezoresistive and Wheatstone bridge. The optimal size and process parameters of the biomimetic sensor structure are given through theoretical calculation and numerical simulation on Comsol and SRIM. Computer numerical control, 3D printing, MEMS technology, and 3D heterogeneous integration technology are used to complete the precise processing and encapsulation of the proposed structure. After the performance tests, results show that the optimal structure can collect directional signals, with a bandwidth from 10 Hz to 1 kHz, which can effectively cover the range of the heart frequency. The signal‐to‐noise ratio is improved by 2.3 dB compared with the 3M stethoscope. An electrocardiogram and phonocardiogram synchronization detection system is developed with this structure. The structure and system can be effectively applied to the high‐quality collection and intelligent recognition of heart sound data sets. The recognition accuracy can reach 98.5%. It is highly effective for early screening and intelligent diagnosis of cardiovascular diseases.

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Design of a high SNR electronic heart sound sensor based on a MEMS bionic hydrophone

Cited by 25 publications

References 21 publications

Discrimination of the Diastolic Murmurs in Coronary Heart Disease and in Valvular Disease

Discrimination of the Diastolic Murmurs in Coronary Heart Disease and in Valvular Disease

Detection and Classification of Abnormities of First Heart Sound Using Empirical Wavelet Transform

Optimum Design and Test of a Novel Bionic Electronic Stethoscope based on the Cruciform Microcantilever with Leaf Microelectromechanical Systems Structure

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