A Review of Computer-Aided Heart Sound Detection Techniques

Table 4

Literature for heart sound classification using deep learning.

Year	Author	Segmentation method	Dataset	Performance
2019	Wu et al. [56]	CNN	PhysioNet (2575 normal heart sounds and 665 abnormal heart sounds)	Hold out testing
				Sensitivity	Specificity		Accuracy
				86.46%	85.63%		86.0%
				Ten-fold cross validation
				Sensitivity	Specificity		Accuracy
				91.73%	87.91%		89.81%
2019	Abduh et al. [57]	DNN	PhysioNet	Sensitivity	Specificity		Accuracy
				89.30%	97%		95.50%
2018	Gharehbaghi and Lindén [58]	DTGNN	130 recordings of the heart sound signal	Sensitivity	Specificity		CR
				83.9%	86%		85.5%
2018	Chen et al. [59]	DNN	PASCAL	Sensitivity	Specificity		Accuracy	Precision
				98%	88.5%		93%	89.1%
2018	Yaseen et al. [60]	DNN	5 categories of heart sound signal, 200 per class (N, AS, MR, MS, MVP)	Sensitivity	Specificity
				94.5%	98.2%
2018	Han et al. [61]	CNN	2575 normal recordings and 665 abnormal recordings	MAcc	Sensitivity		Specificity
				91.50%	98.33%		84.67%
2018	Ren et al. [62]	CNN	PhysioNet	19.8% higher than the baseline accuracy obtained using traditional audio processing functions and support vector machines.
2018	Morales et al. [63]	CNN	PhysioNet	Accuracy	Sensitivity		Specificity
				97%	93.20%		95.12%
2018	Baris et al. [64]	CNN	UoC-murmur database (innocent murmur versus pathological Murmur) and PhysioNet-2016 database (normal versus pathological)	MAcc	Specificity		Sensitivity
				81.5%	78.5%		84.5%
2018	Messner et al. [65]	DNN	PhysioNet	F1 ≈ 96%
2017	Ghaemmaghami et al. [66]	DNN	128 recordings from male and female subjects with healthy hearts	Accuracy	Sensitivity		Specificity
				95.8%	83.2%		99.2%
2017	Sujadevi et al. [67]	RNN & LSTM&GRU	Dataset A from PhysioNet			Accuracy	Precision
				RNN 4 layer		53.8%	55.8%
				LSTM 4 layer		76.9%	83.3%
				GRU 4 layer		75.3%	78.2%
			Dataset B from PhysioNet			Accuracy	Precision
				RNN 4 layer		65.2%	68.1%
				LSTM 4 layer		74.7%	94.5%
				GRU 4 layer		74.4%	69.7%
2017	Chen et al. [68]	DNN	311 S1 and 313 S2 from 16 people (11 males and 5 females)	Accuracy: 91.12%
2017	Yang and Hsieh [69]	RNN	PhysioNet	MAcc: 84%
2017	Zhang and Han [70]	CNN	Dataset A from PASCAL	Normalized precision: 0.77
			Dataset B from PASCAL	Normalized precision: 0.71
2017	Faturrahman et al. [71]	DBN	MITHSDB [72]	Accuracy: 84.89%
			AADHSDB [73]	Accuracy: 86.15%
2017	Maknickas and Maknickas [74]	CNN	PhysioNet	Train accuracy: 99.7%
				Validation accuracy: 95.2%
2016	Thomae et al. [75]	DNN	PhysioNet	Sensitivity	Specificity		Score
				96%	83%		0.89
2016	Tschannen and Dominik [76]	CNN	PhysioNet	Sensitivity	Specificity		Score
				84.8%	77.6%		0.812
2016	Potes et al. [77]	AdaBoost & CNN	PhysioNet	Sensitivity	Specificity		MAcc
				94.24%	77.81%		86.02%