Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Li, Feng; Zhang, Zheng; Wang, Lingling; Liu, Wei

doi:10.3389/fphys.2022.1084420

Cited by 10 publications

(6 citation statements)

References 40 publications

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“…The average sensitivity and specificity on testing datasets trained by the Log-MelSpectrum feature maps are 73.86% and 70.69%, respectively. The result is lower than that of Maknickas [ 19 ] and Li [ 32 ]. This may be due to the following reasons.…”

Section: Discussioncontrasting

confidence: 71%

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Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features

Chen,

Zhou,

Bao

et al. 2023

Bioengineering

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The intelligent classification of heart-sound signals can assist clinicians in the rapid diagnosis of cardiovascular diseases. Mel-frequency cepstral coefficients (MelSpectrums) and log Mel-frequency cepstral coefficients (Log-MelSpectrums) based on a short-time Fourier transform (STFT) can represent the temporal and spectral structures of original heart-sound signals. Recently, various systems based on convolutional neural networks (CNNs) trained on the MelSpectrum and Log-MelSpectrum of segmental heart-sound frames that outperform systems using handcrafted features have been presented and classified heart-sound signals accurately. However, there is no a priori evidence of the best input representation for classifying heart sounds when using CNN models. Therefore, in this study, the MelSpectrum and Log-MelSpectrum features of heart-sound signals combined with a mathematical model of cardiac-sound acquisition were analysed theoretically. Both the experimental results and theoretical analysis demonstrated that the Log-MelSpectrum features can reduce the classification difference between domains and improve the performance of CNNs for heart-sound classification.

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Section: Discussioncontrasting

confidence: 71%

“…The proposed model can classify five different heart sounds. In addition, Li et al [ 32 ] improved Log-MelSpectrum feature maps using dynamic and static MelSpectrum features, and used them as input features for deep residual learning. This method obtained an accuracy of 94.43% for the fusion datasets of three different platforms.…”

Section: Introductionmentioning

confidence: 99%

Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features

Chen,

Zhou,

Bao

et al. 2023

Bioengineering

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“…To develop a cardiac diagnostic system, they proposed a hybrid model that combined the components of a CNN and LSTM. F. Li [4] used the MFCC algorithm to extract features from PCG signals by fusing the PhysioNet/CinC 2016 Challenge dataset, the PASCAL Classifying Heart Sounds Challenge dataset, and the Yassen dataset. The extracted features are classified as Normal, Noise, and Abnormal using a deep residual network.…”

Section: Related Workmentioning

confidence: 99%

“…In addition, the PCG measurement method is noninvasive because it records heart sounds through a sensor and stethoscope, and can be measured in a simple and low-cost manner compared to other biological signal measurements. Because the heart plays essential roles and functions in survival, such as temperature control, nutrient delivery, blood pressure maintenance, and oxygen supply, information on the condition and function of the heart can be obtained through this organ, making it possible to diagnose cardiovascular diseases [4]. It is important to analyze PCG signals for early diagnosis and treatment of cardiovascular diseases.…”

Section: Introductionmentioning

confidence: 99%

Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models

Lee,

Kwak

2023

Applied Sciences

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Analyzing the condition and function of the heart is very important because cardiovascular diseases (CVDs) are responsible for high mortality rates worldwide and can lead to strokes and heart attacks; thus, early diagnosis and treatment are important. Phonocardiogram (PCG) signals can be used to analyze heart rate characteristics to detect heart health and detect heart-related diseases. In this paper, we propose a method for designing using wavelet analysis techniques and an ensemble of deep learning models from phonocardiogram (PCG) for heart sound classification. For this purpose, we use wavelet scattering transform (WST) and continuous wavelet transform (CWT) as the wavelet analysis approaches for 1D-convolutional neural network (CNN) and 2D-CNN modeling, respectively. These features are insensitive to translations of the input on an invariance scale and are continuous with respect to deformations. Furthermore, the ensemble model is combined with 1D-CNN and 2D-CNN. The proposed method consists of four stages: a preprocessing stage for dividing signals at regular intervals, a feature extraction stage through wavelet scattering transform (WST) and continuous wavelet transform (CWT), a design stage of individual 1D-CNN and 2D-CNN, and a classification stage of heart sound by the ensemble model. The datasets used for the experiment were the PhysioNet/CinC 2016 challenge dataset and the PASCAL classifying heart sounds challenge dataset. The performance evaluation is performed by precision, recall, F1-score, sensitivity, and specificity. The experimental results revealed that the proposed method showed good performance on two datasets in comparison to the previous methods. The ensemble method of the proposed deep learning model surpasses the performance of recent studies and is suitable for predicting and diagnosing heart-related diseases by classifying heart sounds through phonocardiogram (PCG) signals.

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“…Mel-frequency spectral coefficients (MFSC) are a commonly used method for extracting time-frequency domain features. For instance, Li et al (2022a) differentiated between normal and pathological heart sounds using MFSC features. Meanwhile, the Homomorphic Envelope (Monteiro et al, 2022), as a morphological feature, reflects changes in the waveform of heart sounds.…”

Section: Fusion Featuresmentioning

confidence: 99%

Assistive diagnostic technology for congenital heart disease based on fusion features and deep learning

Wang,

Yang,

Qian

et al. 2023

Front. Physiol.

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Introduction: Congenital heart disease (CHD) is a cardiovascular disorder caused by structural defects in the heart. Early screening holds significant importance for the effective treatment of this condition. Heart sound analysis is commonly employed to assist in the diagnosis of CHD. However, there is currently a lack of an efficient automated model for heart sound classification, which could potentially replace the manual process of auscultation.Methods: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes.Discussion: The proposed classification model in this study demonstrates a robust capability for identifying congenital heart disease, potentially substituting manual auscultation to facilitate the detection of patients in remote areas.Results: This study introduces an innovative and efficient screening and classification model, combining a locally concatenated fusion approach with a convolutional neural network based on coordinate attention (LCACNN). In this model, Mel-frequency spectral coefficients (MFSC) and envelope features are locally fused and employed as input to the LCACNN network. This model automatically analyzes feature map energy information, eliminating the need for denoising processes. To assess the performance of the classification model, comparative ablation experiments were conducted, achieving classification accuracies of 91.78% and 94.79% on the PhysioNet and HS databases, respectively. These results significantly outperformed alternative classification models.

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Heart sound classification based on improved mel-frequency spectral coefficients and deep residual learning

Cited by 10 publications

References 40 publications

Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features

Classifying Heart-Sound Signals Based on CNN Trained on MelSpectrum and Log-MelSpectrum Features

Heart Sound Classification Using Wavelet Analysis Approaches and Ensemble of Deep Learning Models

Assistive diagnostic technology for congenital heart disease based on fusion features and deep learning

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