Heart Signal Analysis Using Multistage Classification Denoising Model

Table 1

Literature review for the classification of PCG signal.

Sl.	Author name and year	Dataset	Methodology used	Accuracy (%)
1	Yaseen et al. [6] (2018)	Yaseen Khan	MFCC, DWT, and SVM	97.90
2	Ghosh et al. [2] (2019)		Random forest (RF) classifier	98.55
3	Bhagel et al. [21] (2020)		Augmentation, normalization, and filtering	98.6
4	Ranipa et al. [22] (2021)		MFCC and CNN	98.5
5	Shuvo et al. [23] (2021)		CardioXNet	98.2
6	Jain et al. [24] (2023)		1D CNN and DWT	98.6
7	Choudhary et al. [3] (2022)		SVM and TQWT	98.79
8	Meniksa and Maknickas [25] (2017)	Physionet challenge 2016	Deep neural network	84
9	Kay and Agarwal [26] (2017)		Propagation neural networks	90
10	Das et al. [27] (2019)		Cochleagram ANN	95
11	Chen et al. [28] (2019)		Deep convolutional neural networks (DCNNs)	92.47
12	Arora et al. [29] (2019)		Xgboost and RNN	92.9
13	Chowdhury et al. [4] (2020)		MFCC and DNN	97.2
14	Nehary et al. [30] (2021)		FFT mel-spectral coefficients and CNN	97.18
15	Tiwari et al. [31] (2021)		Deep learning and ConvNet	96
16	Devi et al. [32] (2021)		Wavelet scattering transform and alex net	98.11
17	Pauline et al. [33]	PASCAL heart sound [34]	Adaptive filter structure using a sign error LMS algorithm
18	Shuvo et al. [35]	GitHub heart sound	CRNN architecture called noise robust cardio net (NRC-Net)