Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network

Table 6 Studies for HS feature extraction and classification using machine learning

Year	Author	Dataset	Feature extraction methods	Classifier	Results
Year	Author	Dataset	Feature extraction methods	Classifier	Sens (%)	Spec (%)	Acc (%)
2015	Zheng et al. [23]	88 normal heart sounds, 64 abnormal heart sounds	MF-DFA, MESE, EMD	HMM	82.95	79.68	81.58
				BP-ANN	85.23	82.81	84.21
				LS-SVM	96.59	93.75	95.39
2016	Thomae et al. [29]	PhysioNet	1D CNN	Bidirectional GRU	96	83	–
2016	Potes et al. [43]	PhysioNet	LR-HMMS, MFCC	AdaBoost	70	88	–
2016	Potes et al. [43]	PhysioNet	Frequency bands decomposition	CNN	79	86	–
2019	Li et al. [25]	2532 recordings from healthy subjects, 664 recordings from patients	DAE	1D CNN	–	–	97.85
2019	Li et al. [25]		MFCC	1D CNN	–	–	91.02
2020	Li et al. [24]	PhysioNet	Eight domains	CNN	87	86.6	86.8
2020	Gao et al. [27]	1286 normal recordings form PhysioNet, 108 abnormal heart sounds from patients	–	SVM	–	–	87.62
				FCN	–	–	94.65
				LSTM	–	–	96.29
				GRU	–	–	98.82
2020	Deng et al. [28]	PhysioNet	MFCC	CRNN	98.66	98.01	98.34
2020	Deng et al. [28]	PhysioNet	MFCC	PRCNN	97.33	97.33	97.34

MF-DFA multifractal detrended fluctuation analysis, MESE maximum entropy spectra estimation, EMD empirical mode decomposition, CNN convolutional neural network, 1D CNN one-dimensional convolutional neural network, DAE denoising autoencoder, MFCC Mel-frequency cepstrum coefficient, HMM hidden Markov model, LR-HSMM logistic regression-based hidden semi-Markov model, BP-ANN back-propagation artificial neural network, LS-SVM least square support vector machine, GRU gated recurrent unit, FCN Fully Convolutional Network, LSTM long-short term memory network, CRNN convolutional recurrent neural networks, PRCNN paralleling recurrent convolutional neural network

ISSN: 1475-925X