Table 2.

Segmentation methods of PCG signals.

Year	Author	Segmentation method	Dataset	Result
2019	Giordano and Knaflitz [16]	Envelope-based technique	Sample population of 24 healthy subjects over 10-min-long simultaneous phonocardiography recordings	F1 of 99.2%
2019	Oliveira et al. [17]	HSMM-GMM	PhysioNet [18], PASCAL [19] and a pediatric dataset composed of 29 heart sounds	F-score of 92%
2019	Kamson et al. [20]	HSMM	Training-set-a of 2016 PhysioNet/computing in cardiology challenge	Sensitivity	P+	F1
				98.28	98.45	98.36
2019	Renna et al. [21]	HSMM-CNN	PhysioNet	Sensitivity: 93.9%
2018	Liu et al. [22]	Time-domain analysis, frequency-domain analysis and time-frequency-domain analysis	Heart sound & Murmur library of UMich	Sensitivity: 98.63%
2018	Belmecheri et al. [23]	Correlation coefficients matrix	A database of 21 clean heart sounds	Sensitivity: 76%
2018	Alexander et al. [24]	HMM	3240 PCG recordings from PhysioNet and PASCAL	Sensitivity	Specificity
				90.3%	89.9%
2017	Babu et al. [25]	VMD	Database:	Sensitivity	P+	Accuracy
			PhysioNet	98.90	96.07	95.14
			PASCAL	99	100	99
			Michigan [26]	100	100	100
			eGeneralMedical [27]	100	100	100
			Real-time PCG signals	100	97.08	97.08
2017	Varghees et al. [28]	EWT	PhysioNet, PASCAL, Michigan, eGeneralMedical and real-time PCG signals	Sensitivity	Pp	OA
				94.38%	97.25%	91.92%
2017	Liu et al. [29]	HSMM	More than 120 000 s of heart sounds recorded from eight independent heart sound databases	F1 of 98.5%
2016	Thomas et al. [30]	Fractal decomposition (FD)	Michigan (23 different heart sounds and 6 patients' recordings done in a real clinical environment)	Sensitivity	+P	DER
				96.97	99.58	3.55
2016	Springer et al. [31]	HSMM	405 synchronous 30–40 s PCG and ECG recordings from 123 deidentified adult patients	F1 of 95.63 ± 0.85%
2015	Salman et al. [32]	Peak intervals pattern	1089 cycles from 62 set of normal and abnormal signals	Correct cycle detected rate of 83.38%