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Table 4 Wavelet domain

From: Localization and classification of heart beats in phonocardiography signals —a comprehensive review

Study

Year

Pre-processing techniques

Localization techniques

Classification techniques

Beat types

Data set

Signals statistics

Evaluation metrics

Results

Kay et al. [73]

2017

Springer’s segmentation algorithm

ANN using beat duration, MFCC, CWT, and complexity features

Normal and abonormal heart sounds

2016 PhysioNet challenge

Accuracy

85.2%

Nabih et al. [77]

2017

Reconstruction based on DWT and IDWT

Db 10 wavelet

ANN

Normal heart sounds

PASCAL CHSC 2011 Dataset A and B

170

SNR, RMSE, and PRD

Jain et al. [69]

2017

Adaptive method for DWT coefficient reduction for denoising PCG

Normal heart sounds

E-General Medical and 10 signal privately recorded

% detection of S1 and S2

55.1–100%

Mostafa et al. [71]

2016

Downsampling, bandpass filtering, and Schmidt spike removal algorithm [104]

Springer’s segmentation algorithm [15]

FDA, ANN, and voting- based classification

Normal and abnormal heart sounds

2016 PhysioNet challenge

4430 signals

Specificity, sensitivity, and over all score

87.2–97%, 81.1–96%, and 84.1–98%

Jain et al. [68]

2016

Adaptive thresholding of DWT coefficient reduction for denoising PCG

Normal heart sounds

Private dataset composed of simulated and real life data

% detection of S1 and S2

86.6–100%

Boussaa et al. [72]

2016

ANN-based classification using MFCC and DWT features

Normal heart sounds and murmurs

MIT BIH [103]

50 signals from MIT BIH dataset

Correct decision rate

100%

Zahhad et al. [67]

2016

DWT-based denoising

Classification based on MFCC, LFCC, WPCC, and NLFCC features

Normal heart sounds and murmurs

BioSec. PCG dataset [105] and PASCAL CHSC 2011 dataset A and B

21 from BioSec. PCG dataset and 206 signals from PASCAL CHSC 2011

Correct recognition rate

Pedrosa et al. [62]

2014

Morlet mother-based continuous wavelet denoising

Time domain, time-frequency domain, perceptual analysis, non-linear, and chaos-based analysis

Time domain, time- frequency domain, perceptual analysis, non-linear, and chaos-based analysis

Normal heart sounds and murmurs

Data recorded at Real Hospital Portugues in Recife, Brazil and PASCAL CHSC 2011 dataset A and B

72 Digiscope signals and 111 signals from PASCAL CHSC 2011

For segmentation: sensitivity and positive predictive, and for murmur: sensitivity and specificity

89.2 and 98.6%; and 52.38–69.67%; and of 46.91–79.40%

Zheng et al. [63]

2013

Normalization, DWT and envelogram

MFCC-based feature extraction

MFCC-based feature extraction

Normal heart sounds and heart sounds with pathological symptoms

E-General Medical

60 signals

Segmentation rate

Marques et al. [64]

2013

Stationary wavelet transform to segment the signal

Hierarchical clustering to distinguish S1 and S2

Normal heart sounds

PASCAL CHSC 2011

30 signals from dataset A and 120 signals from dataset B

Error reduction

Yiqi Deng, Peter J Bentley [65]

2012

Decompose and reconstruct using wavelet decomposition using 4 level Db6 filters and refilter the signal using spectrogram

Time duration between peaks and peak amplitude

Time duration between peaks and peak amplitude

Normal heart sounds, murmurs, extra sounds and artifact

PASCAL CHSC 2011

Dataset A and dataset B

Precision, sensitivity, and specificity

77.67 and 45.83%, 43.75–50.85%, and 44.44–58.2%

Naseri et al. [61]

2012

baseline removal, wavelet base noise removal, and normalization

Time duration, peak value, and time duration between peaks

Time duration, peak value, and time duration between peaks

Normal heart sounds, murmurs, and scouffles

Rajaei Cardi- ovascular, Medical and Research Center, Iran

50 signals of total duration of 52 min

Sensitivity and positive predictive value

99.00 and 98.60%

Song et al. [55]

2012

LMS and Db3 wavelet

Time durations extracted from normalized, Shannon envelop formation

Peak and duration based, fuzzy neural network with structure learning (FNNSL)

Normal and abnormal heart sounds

14 signals composed of private and public subsets

Classification rate

100% for normal and 50% for abnormal

Zhihai Tu et. al. [59]

2010

Db6 wavelet

Heart beat time duration

Hilbert transform-based classification

Normal heart sounds

Shandong University database and real-time device

123 recordings from database and 100 from real-time device

Correct segmentation rate

94.60%

Babaei et al. [56]

2009

Db4 and inverse Db4

Db4 and inverse Db4, ANN and statistical classifier

Normal heart sounds, aortic insufficiency, aortic stenosis, and pulmonary stenosis

Tehran Heart Center, Iran

372 cycles

Accuracy

94.42%

Gupta et al. [60]

2007

Signal decimation, low pass filtering and signal normalization

Homomorphic filtering, time duration and K-means

Wavelet Db2-based features, GAL, and MLP-BP

Normal heart sounds, systolic murmur, and diastolic murmur

Singapore General, Hospital, Singapore

41 signals composed of 340 cycles

Classification rate

90.29%

Gupta et al. [66]

2005

Signal normalization

Peak detection based on homomorphic filtering and K-means

Db2 wavelet-based features and MLP-BP

Normal heart sounds, systolic murmur, and diastolic murmur

Data recorded at Singapore General Hospital, Singapore

340 cycles

Classification and segmentation accuracy

94.5 and 90.45%

Liung et al. [58]

1998

wavelet packet decomposition

Wavelet packet decomposition

Pathological and physiological murmurs

85 signal of 7–12 s

Accuracy

85%

Huiying et al. [57]

1998

Decompose and reconstruct signal using Daubechies wavelet

Shannon energy envelop-based S1 and S2 location

Feature vector based on systole time duration, diastole time duration, and ANN

Normal heart sounds and murmurs

Private dataset

78 recordings

Accuracy

74.4%