Recent Advances in PCG Signal Analysis using AI: A Review

Tanmay Sinha Roy; Joyanta Kumar Roy; Nirupama Mandal; Subhas Chandra Mukhopadhyay

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Recent Advances in PCG Signal Analysis using AI: A Review

Tanmay Sinha Roy

Joyanta Kumar Roy

Nirupama Mandal

Subhas Chandra Mukhopadhyay

| 06 mar 2024

International Journal on Smart Sensing and Intelligent Systems

Volumen 17 (2024): Edición 1 (January 2024)

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Article Category: Review

Publicado en línea: 06 mar 2024

Páginas: -

DOI: https://doi.org/10.2478/ijssis-2024-0012

Palabras clave
Auscultation, Feature Extraction, Classification, Shannon Energy, Deep Learning, Xception Network, Valvular Heart Disease, Convolutional Neural Network, Preprocessing, Segmentation

© 2024 Tanmay Sinha Roy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

A typical recorded PCG signal [1]. PCG, phonocardiogram.

Various phases engaged in PCG analysis [2]. PCG, phonocardiogram.

Graphical representation of various steps involved in PCG signal classification [2, 14]. PCG, phonocardiogram.

Characteristics of various kinds of Heart Sounds. AR, aortic regurgitation; AS, aortic stenosis; MR, mitral regurgitation; MS, mitral stenosis; MVP, mitral valve prolapse; PDA, patent ductus arteriosus; PR, pulmonary regurgitation; PS, pulmonary stenosis; TR, tricuspid regurgitation; TS, tricuspid stenosis; VSD, ventricular septal defect.

Schematic diagram of sensors, pre-amplifier, and filters used in PCG signal analysis. PCG, phonocardiogram.

Schematic diagram of PCG signal acquisition system. PCG, phonocardiogram.

Block diagram of ANC. ANC, adaptive noise canceller.

Block diagram of ALE. ALE, adaptive line enhancer.

Block diagram of Heart Sound preprocessing.

Reduction in data size due to feature extraction.

PCG of a normal cardiac Heart Sound. PCG, phonocardiogram.

Shannon energy envelogram of the PCG of a normal Heart Sound. PCG, phonocardiogram.

Schematic diagram of Heart Sound analysis using DWT. ANFIS, adaptive neuro fuzzy inference system; DWT, discrete wavelet transform; DT-CWT, dual tree complex wavelet transform.

PCG analysis of different types of Heart Sounds [41, 39, 49]. PCG, phonocardiogram.

Different coefficients in DWT [121, 122]. DWT, discrete wavelet transform.

Flow chart of DWT [121, 122]. DWT, discrete wavelet transform.

Time-domain analysis of different Heart Sounds using wavelet transform [121, 122]. AR, aortic regurgitation; MS, mitral stenosis.

Time (a) and frequency (b)-domain analysis of different PCG signals using DWT. AS, aortic stenosis; DWT, discrete wavelet transform; MR, mitral regurgitation; MS, mitral stenosis; MVP, mitral valve prolapse; PCG, phonocardiogram.

(a). Amplitude distribution of Normal Heart. (b). Amplitude distribution of Abnormal Heart.

Earlier studies on deep-learning-based methods for cardiac sound classification.

Comparison of machine-learning and deep-learning architectures.

Architecture of CNN model [137] for Heart Sound classification.

PCG signal classification based on deep-learning models [138, 139]. PCG, phonocardiogram.

Application of Heart Sound classification method adopted by a medical practitioner [140, 141].

Study of machine-learning vs. deep-learning methods for cardiac sound classification [148, 149, 150].

Schematic block diagram of Alex Network.

Schematic block diagram of Dense Net 121.

Schematic block diagram of Mobile Net 5.9 Inception Net Model.

Schematic block diagram of Inception Net 5.10 Residual Net Model.

Schematic block diagram of Residual Net.

Study of all CNN-based deep-learning models [142, 143].

Deep neural network based model architecture [144, 145].

Architecture of a machine-learning algorithm like Random Forest [146, 147].

Heart Sound frequencies found in various abnormal Heart Sounds.

Heart Sound	Lowest frequency (Hz)	Highest frequency (Hz)
First Heart Sound	100	200
Second Heart Sound	50	250
AR	60	380
PR	90	150
AS	100	450
PS	150	400
ASD	60	200
VSD	50	180
MR	60	400
TR	90	400
MS	45	90
TS	90	400
MVP	45	90
PDA	90	140
Flow murmur	85	300

Comparison of training and validation performance metrics of CNN-based deep-learning models.

System	Training loss	Training accuracy	Validation loss	Validation accuracy
LENET-5	0.4352	0.7089	0.4757	0.6799
Alex Net	0.3476	0.7379	0.3837	0.7199
VGG16	0.2979	0.8102	0.3134	0.7978
VGG19	0.2692	0.8709	0.2899	0.8469
DENSENET121	0.2476	0.9087	0.2665	0.8876
Squeeze Network	0.2097	0.9265	0.2098	0.8906
Mobile Network	0.1576	0.9435	0.1376	0.9073
Inception Network	0.0472	0.9843	0.0654	0.9863
Residual Network	0.0432	0.9856	0.0533	0.9892
Xception Network	0.0320	0.9951	0.0325	0.9926

A study of PCG signal and their gap analysis reported in the literature

Ref.	Author	Year	Methodological problem and research gap analysis	Features
[15]	Dewangan et al.	2018	Basic features used in the time domain only	Heart Sound analysis using the DWT method
[16]	Thomas Schanze	2017	Biomedical heart signal analysis not using latest machine-learning and deep-learning methods	Singular value decomposition
[17]	Othman and Khaleel	2017	Only time-domain analysis has been made	PCG signal analysis using Shannon Energy Envelop and DWT method
[18]	Martinek et al.	2017	PCG signal analysis applicable to fetal heart only, not real-time human subjects	Adaptive filtering based fetal heart rate monitoring
[19]	Abhijay Rao	2017	It is a survey paper only, not a research paper	Biomedical signal processing
[20]	Sh-Hussain et al.	2016	Frequency domain and statistical domain features need to be analyzed	Heart Sound monitoring system using wavelet transformation
[21]	Prasad and Kumar	2015	Real-time PCG signal analysis was not applied	Analysis of various DWT methods for feature extracted PCG signals
[22]	Pan et al.	2015	Real-time analysis not done	Categorization of PCG signals using multimodal features
[23]	Lubaib and Muneer	2015	More features need to be considered for this analysis	Using pattern recognition techniques
[24]	Roy et al.	2014	No proper experimentation has been done	A survey on classification of PCG signals
[25]	Mishra et al.	2013	It deals with PCG signal noise removal only, not classification	Denoising of Heart Sound signal using DWT
[26]	Zhao et al.	2013	PCG signal/Heart Sound biometric	Marginal Spectrum Analysis
[27]	Singh and Cheema	2013	Limited application of deep-learning algorithms has been used	Classification using Feature Extraction
[28]	Safara et al.	2013	Only time-domain analysis has been made	Multi-level basis selection of wavelet
[29]	Salleh et al.	2012	PCG signal analysis using Kalman filter, not using any standard deep-learning method	Heart Sound analysis: a Kalman filter-based approach
[30]	Misal and Sinha	2012	It deals with PCG signal noise removal only, not classification	Denoising of PCG signal using DWT
[31]	Kasturiwale	2012	Biomedical signal analysis, limited features.	Analysis using component extraction
[32]	McNames and Aboy	2008	It deals with the modeling part of PCG signals, not the classification and analysis	Techniques, statistical modeling of PCG signals
[33]	Ahmad et al.	2009	More feature extraction needs to be done for the PCG signal analysis	Classification of PCG signal using an Adaptive Fuzzy Inference System.
[34]	Debbal and Bereksi-Reguig	2006	Time-domain analysis in time domain only	PCG signal analysis using the CWT
[35]	Gupta et al.	2005	A real-time PCG signal analysis was not done	Segmentation and categorization of Heart Sound for analysis purpose.
[36]	Muthuswamy	2004	It is a survey paper only, not a research paper	Biomedical signal analysis

Comparison of performance metrics in various CNN-based deep-learning models.

System	Accuracy (%)	Precision (%)	Recall (%)	F1 Score (%)	Train time (s)	Test time (s)
LeNet-5	68.97	69.75	67.44	66.68	1086	1135
Alex Net	72.34	70.74	73.88	71.24	1233	1337
VGG16	74.08	75.29	75.08	75.13	1352	1011
VGG19	82.17	83.33	82.19	84.21	1343	947
DenseNet121	92.47	93.55	93.47	94.48	1201	937
Squeeze Network	93.57	92.65	94.79	93.56	1098	1012
Mobile Network	95.09	94.35	95.68	93.46	890	987
Inception Network	96.96	98.17	98.96	97.02	710	974
Residual Network	97.32	98.42	98.32	98.35	783	1056
Xception Network	99.13	98.18	98.43	99.19	750	865
Efficient Network-B3	99.49	98.62	98.72	99.37	786	854

Different sensors used in real-time Heart Sound analysis methods.

Denoising domain	Type of sensors used	Number of sensors	Algorithm adopted
Time	Electret condenser microphone	1	LMS-ANC
Time	Electronic stethoscope	1	Single input ANC
Time	Microphone	1	LMS-ALE, RLS-ALE
Frequency	Electronic stethoscope with an electret microphone	1	DWT, Hilbert transform
Frequency	Microphone	1	DWT, LMS-ALE, RLS-ALE
Frequency	NM	NM	EMD

A summary of CNN and RNN based methods used in PCG signal analysis [108, 109].

S. No.	References	Methods	Features used	Segmentation optimizer		Types of PCG signal
1	Maknickas and Maknickas 2017 [55]	2D-CNN	MFSC	No	RMSprop	N, A
2	Alafif et al. 2020 [56]	2D-CNN + transfer learning	MFCC	No	SGD	N, A
3	Deng et al. 2020 [54]	CNN + RNN	Improved MFCC	No	Adam	N, A
4	Abduh et al. 2019 [57]	2D-DNN	MFSC	No	Adam	N, A
5	Chen et al. 2018 [58]	2D-CNN	Wavelet transform + Hilbert–Huang features	No	Adam	N, M, EXT
6	Rubin et al. 2016 [59]	2D-CNN	MFCC	Yes	Adam	N, A
7	Nilanon et al. 2016 [60]	2D-CNN	Spectrograms	No	SGD	N, A
8	Dominguez-Morales et al. 2018 [61]	2D-CNN	Spectrograms	No	Adam	N, A
9	Bozkurt et al. 2018 [62]	2D-CNN	MFCC + MFSC	Yes	Adam	N, A
10	Chen et al. 2019 [63]	2D-CNN	MFSC	No	Adam	N, A
11	Cheng et al. 2019 [64]	2D-CNN	Spectrograms	No	Adam	N, A
12	Demir et al. 2019 [65]	2D-CNN	Spectrograms	No	Adam	N, M, EXT
13	Ryu et al. 2016 [66]	1D-CNN	1D time-series signals	No	SGD	N, A
14	Xu et al. 2018 [67]	1D-CNN	1D time-series signals	No	SGD	N, A
15	Xiao et al. 2020 [68]	1D-CNN	1D time-series signals	No	SGD	N, A
16	Oh et al. 2020 [69]	1D-CNN WaveNet	1D time-series signals	NO	Adam	N, AS, MS, MR, MVP
17	Khan et al. 2020 [70]	LSTM	MFCC	No	Adam	N, A
18	Yang et al. 2016 [71]	RNN.	1D time-series signals	No	Adam	N, A
19	Raza et al. 2018 [72]	LSTM	1D time-series signals	No	Adam	N, A
20	Tschannen et al. 2016 [73]	2D-CNN + SVM	Deep features	Yes	Adam	N, A

eISSN:: 1178-5608
Idioma:: Inglés

Calendario de la edición:: Volume Open
Temas de la revista:: Engineering, Introductions and Overviews, other

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Recent Advances in PCG Signal Analysis using AI: A Review

Article Category: Review

Publicado en línea: 06 mar 2024

Páginas: -

DOI: https://doi.org/10.2478/ijssis-2024-0012

Palabras claveAuscultation, Feature Extraction, Classification, Shannon Energy, Deep Learning, Xception Network, Valvular Heart Disease, Convolutional Neural Network, Preprocessing, Segmentation

© 2024 Tanmay Sinha Roy et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Heart Sound frequencies found in various abnormal Heart Sounds.

Comparison of training and validation performance metrics of CNN-based deep-learning models.

A study of PCG signal and their gap analysis reported in the literature

Comparison of performance metrics in various CNN-based deep-learning models.

Different sensors used in real-time Heart Sound analysis methods.

A summary of CNN and RNN based methods used in PCG signal analysis [108, 109].

Palabras clave
Auscultation, Feature Extraction, Classification, Shannon Energy, Deep Learning, Xception Network, Valvular Heart Disease, Convolutional Neural Network, Preprocessing, Segmentation