Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features

Ghosh, Samit Kumar; Tripathy, Rajesh Kumar; Ponnalagu, R N; Pachori, Ram Bilas

doi:10.1109/lsens.2019.2949170

Cited by 71 publications

(38 citation statements)

References 28 publications

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“…These 15 PCG signals were recorded from 15 different subjects (12 males and 3 females with the age group of 27 ± 5 years) using Thinklab digital stethoscope ( https://www.thinklabs.com/ ). The subjects have given written consent before recording the PCG signal in a noninvasive way [ 36 ]. The sampling frequency of each recorded signal is 4 kHz.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…In this study, the number of neurons used in the first and second hidden layers of the proposed DLKSRN is 800 and 600, respectively. Moreover, we have also considered the random forest (RF) [ 36 ] and K -nearest neighbour (KNN) [ 65 ] classifiers for the classification of HVAs from the feature vectors of test PCG recordings. The optimal parameters of the RF classifier [ 71 ] such as the number of trees, number of splits for each decision tree, and depth of each decision tree obtained using the grid-search technique are 20, 20, and 15, respectively.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…The time-scale-based methods such as discrete wavelet transform (DWT) [ 31 , 32 ], empirical mode decomposition (EMD) [ 31 , 32 ], and tunable Q-wavelet transforms (TQWT) [ 33 ] of PCG signals have also been used for the detection of HVAs. Moreover, the time-frequency analysis-based approaches such as the short-time Fourier transform (STFT) [ 34 , 35 ], synchrosqueezing transform (SST) [ 36 ], and other time-frequency decomposition-based approaches [ 37 – 39 ] of PCG signals are used for the categorization of HVAs. The machine learning techniques such as the support vector machines (SVM) [ 40 ], random forest (RF) [ 41 ], convolutional neural network (CNN) [ 42 ], and hidden Markov model (HMM) [ 43 ] have been used for the classification of HVAs.…”

Section: Introductionmentioning

confidence: 99%

“…In [ 45 ], the authors have applied a novel algorithm based on wavelet fractal dimension and a twin support vector machine (TWSVM) for the classification of HVAs using PCG signals. Moreover, Ghosh et al [ 36 ] have extracted the magnitude and phase features from the time-frequency representation of the segmented PCG cycles for the discrimination of HVAs. They have used synchrosqueezing transform (SST) for the evaluation of the time-frequency matrix from the PCG signal.…”

Section: Introductionmentioning

confidence: 99%

“…They have used synchrosqueezing transform (SST) for the evaluation of the time-frequency matrix from the PCG signal. The SST-based method has drawbacks such as it has poor time-frequency resolution for PCG signals as it uses the coefficient reassignment in the time-frequency plane based on the instantaneous frequency of the PCG signal [ 36 , 46 ]. Also, the SST method has shown less performance for the detection of HVAs.…”

Section: Introductionmentioning

confidence: 99%

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Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

Ghosh

Ponnalagu

Tripathy

et al. 2020

BioMed Research International

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The heart valve ailments (HVAs) are due to the defects in the valves of the heart and if untreated may cause heart failure, clots, and even sudden cardiac death. Automated early detection of HVAs is necessary in the hospitals for proper diagnosis of pathological cases, to provide timely treatment, and to reduce the mortality rate. The heart valve abnormalities will alter the heart sound and murmurs which can be faithfully captured by phonocardiogram (PCG) recordings. In this paper, a time-frequency based deep layer kernel sparse representation network (DLKSRN) is proposed for the detection of various HVAs using PCG signals. Spline kernel-based Chirplet transform (SCT) is used to evaluate the time-frequency representation of PCG recording, and the features like L1-norm (LN), sample entropy (SEN), and permutation entropy (PEN) are extracted from the different frequency components of the time-frequency representation of PCG recording. The DLKSRN formulated using the hidden layers of extreme learning machine- (ELM-) autoencoders and kernel sparse representation (KSR) is used for the classification of PCG recordings as normal, and pathology cases such as mitral valve prolapse (MVP), mitral regurgitation (MR), aortic stenosis (AS), and mitral stenosis (MS). The proposed approach has been evaluated using PCG recordings from both public and private databases, and the results demonstrated that an average sensitivity of 100%, 97.51%, 99.00%, 98.72%, and 99.13% are obtained for normal, MVP, MR, AS, and MS cases using the hold-out cross-validation (CV) method. The proposed approach is applicable for the Internet of Things- (IoT-) driven smart healthcare system for the accurate detection of HVAs.

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Section: Proposed Methodsmentioning

confidence: 99%

Section: Proposed Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

Ghosh

Ponnalagu

Tripathy

et al. 2020

BioMed Research International

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Heart Sounds Classification Based on High‐Order Spectrogram and Multi‐Convolutional Neural Network after a New Screening Strategy

Li,

Yi,

Zhong

et al. 2023

Advcd Theory and Sims

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This paper proposes a pre‐processing method for heart sound screening and extracts the high‐order spectral feature of phonocardiogram. Moreover, a multi‐convolutional neural network (mCNN) is constructed to achieve the classification of normal, aortic stenosis, mitral regurgitation, mitral stenosis, and mitral valve prolapse. First, the heart sound recordings are down‐sampled, denoised by wavelet transform, and normalized. Second, a new heart sound screening algorithm is proposed. The waveform of the heart sound recording is segmented and saved as an image which is performed by the gray‐scale processing to calculate the amplitude of the heart sound. The extremely noisy heart sound segments are screened out based on the amplitude information, and the remaining heart sound segments are spliced as pure heart sound recordings. After 50% superposition segmentation of the heart sound recordings, high‐order spectral features are extracted and image data are stored. Finally, a 34‐layer mCNN is specifically designed to boost the performance of heart sound classification through multi‐layer dimensionality reduction. Experimental results show that the proposed method has superior performance compared with the existing one. For the two‐category dataset, the accuracy with and without PCG screening is 97.99% and 99.42%, respectively. For the five‐category dataset, the average accuracy is 99%.

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Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning

et al. 2021

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Automated Detection of Heart Valve Disorders From the PCG Signal Using Time-Frequency Magnitude and Phase Features

Cited by 71 publications

References 28 publications

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

Deep Layer Kernel Sparse Representation Network for the Detection of Heart Valve Ailments from the Time-Frequency Representation of PCG Recordings

Heart Sounds Classification Based on High‐Order Spectrogram and Multi‐Convolutional Neural Network after a New Screening Strategy

Detection of heart valve disorders from PCG signals using TQWT, FA-MVEMD, Shannon energy envelope and deterministic learning

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