Discrete Wavelet based Features for PCG Signal Classification using Hidden Markov Models

Exploiting Data-Efficient Image Transformer-Based Transfer Learning for Valvular Heart Diseases Detection

“…Database Features Classification technique Domain Accuracy (%) [5] The [6] PhysioNet/CinC Statistical, Frequency XGBoost ML 92.90 [7] PhysioNet/CinC Time, MFCCs and Statistical Neural network (NN) ML 93.33 [11] PhysioNet/CinC MFCC Decision tree ML 86.40 [12] PhysioNet/CinC MFCCs NN ML 92.00 [13] PhysioNet/CinC LPC, Entropy, MFCCs, DWT and PSD NN ML 91.50 [14] Private MFCC DNN DL 91.12 [15] PhysioNet/CinC MFCCs DNN DL 93.00 [16] The HSM database TQWT, EMD and Shannon energy RBF neural networks ML 98.48 [17] PhysioNet/CinC MFSCs SVM ML 92.00 [18] PhysioNet/CinC Gram polynomial and Fourier transform NN ML 94.00 [19] PASCAL DWT Hidden Markov Models ML 92.74 [20] PhysioNet/CinC Wavelet CNN DL 81.20 [21] PhysioNet/CinC Modified frequency slice wavelet transform CNN DL 94.00 [22] PhysioNet/CinC Frequency spectrum, Energy and Entropy SVM ML 88.00 [23] Private EMD and MFCCs SVM ML 91.00 [24] MIT heart sounds Frequency SVM ML 98.00 [25] PhysioNet/CinC Time, MFCC, DWT and Wavelet SVM ML 82.40 [26] The HSM database FMFE + MFCC SVM ML 99.00 [27] PhysioNet/CinC Spectral SVM ML 98.00 [28] The HSM database Time-frequency MCC ML 98.33 [29] PhysioNet/CinC Cochleagram MLP ML 95.00 [30] The HSM database Time-frequency magnitude and phase RF ML 95.12 [31] Private MFCC KNN ML 98.00 [32] Private EMD KNN ML 94.00 [33] PASCAL MFCCs KNN ML 97.00 [34] Private MFCCs KNN ML 92.60 [37] PhysioNet/CinC MFCCs LSTM DL 98.61 [38] PhysioNet/CinC Time, Frequency and Time-frequency DNN DL 92.60 [39] PhysioNet/CinC MFCC+ MFSC 2D-CNN DL 81.50 [40] PhysioNet/CinC Mean, Standard deviation and Power spectrum CNN DL 86.02 [41] PhysioNet/CinC Spectrogram, Mel-spectrogram and MFCCs CNN DL 86.05 [42] The HSM database Normalized signals CNN...…”

“…These studies have explored the use of efficient hand-crafted feature extraction techniques in combination with effective classifiers. Several methods have been proposed for the detection of VHD, including Mel-Frequency Cepstral Coefficients (MFCCs) [11]- [15], Tunable Q-factor Wavelet Transform (TQWT) [16], Mel Frequency Spectral Coefficients (MFSCs) [17], Gram polynomial [18] and Wavelet Transform (WT) [19]- [21]. In ad-dition, the study examined various machine learning classifiers, including the support vector machine (SVM) [22]- [27], multiclass composite classifiers (MCC) [28], Multilayer Perceptron (MLP) [29], Random Forest (RF) [30], and k-Nearest Neighbor (k-NN) [31]- [34].…”

“…TABLE 1. Summary of studies employing ML, DL, and transfer learning for heart sound classification: predominantly focused on five-class prediction using the Heart Sound Murmur (HSM) Database [62] and two-class prediction (Normal/abnormal) using the PhysioNet/CinC Database [56] and PASCAL Classifying Heart Sounds Challenge (PASCAL) [19].…”

Multiclass Classification of Myocardial Infarction Based on Phonocardiogram Signals Using Ensemble Learning

“…A mathematical technique called wavelet analysis is frequently used to decompose a signal into a set of waveforms localized in both the time and frequency domains. This decomposition results in wavelet coefficients [15]. In this study, Discrete Wavelet Transform (DWT) was employed.…”