A Comparative Study of Heart Sound Signal Classification Based on Temporal, Spectral and Geometric Features

Ibrahim, Nabilah; Jamal, Norezmi; Sha'abani, Mohd Nurul Al-Hafiz; Mahadi, Lina Farhana

doi:10.1109/iecbes48179.2021.9398810

Cited by 4 publications

(9 citation statements)

References 22 publications

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“…A range of Butterworth bandpass filters with various orders have been applied with different cutoff frequency settings. For instance, a 4-th order Butterworth filter was set with a cutoff frequency of 25-400 Hz in [44], and a 5-th order Butterworth filter was designed to have a cutoff frequency of 25-500 Hz in [45] and 25-250 Hz in [46]. A 6-th order Butterworth filter was designed with a cutoff frequency of 50-950 Hz [47], [48] and 30-900 Hz in [49].…”

Section: A Denoisingmentioning

confidence: 99%

“…Envelope-based segmentation. Apart from energy, heart sound segmentation can be achieved based on envelopes [46], [64]. For instance, heart sound segmentation was implemented based on Shannon energy envelope and zero crossings of heart sounds in [9].…”

Section: B Segmentationmentioning

confidence: 99%

“…Generative models aim to generate the joint probability distribution P (X, y), given the features X and the labels y. The posterior probability P (y|X) is computed via the Spectral amplitude Fourier transform [7], [32], [33], [38], [46], [86]- [89]…”

Section: Classic Machine Learning For Classificationmentioning

confidence: 99%

“…Frequency which leads to the maximum spectrum [87], [88] Dominant frequency ratio Ratio of the maximun energy to the total energy [87], [88] Energy Spectral energy [38] Spectral roll-off Frequency below a specific percentage of the total spectral energy [32] Spectral centroid Average of magnitude spectrogram at each frame [32], [89] Specrtal flux Changing speed of the power spectrum [32] Power spectral density (PSD) Distribution of power in spectral components [46], [85], [89] Spectral entropy Shannon entropy of PSD [54], [86] Discrete cosine transform of Mel-scaled spectrogram [38], [75], [79], [86], [87], [93]- [95] Fractional Fourier transform-based Mel-frequency Mel-frequency from the fractional Fourier transform [43] Wavelet Wavelet transform Frequency analysis of a signal at various scales [59], [85], [89] Wavelet scattering transform "Wavelet convolution with nonlinear modulus and averaging scaling function" a (translation invariance and elastic deformation stability [96]) [96], [97] Wavelet synchrosqueezing transform Reassignment of wavelet coefficients [35] Tunable quality wavelet transform "Wavelet multiresolution analysis with a user-specified Q-factor, which is the ratio of the centre frequency to the bandwidth of the filters" b…”

Section: Dominant Frequency Valuementioning

confidence: 99%

“…4 presents a statistic of recent works from 2017 to 2022 that employ classic machine learning models for heart sound classification. SVMs have been very widely used for heart sound classification by learning a supporting hyperplane between classes [6], [33], [40], [41], [43], [46]- [48], [55], [60], [64], [75], [79], [85], [91]- [93], [95]- [97], [99], [100], [103]. Apart from linear projection between data samples and labels, SVMs can learn separating hyperplanes on non-linear data via non-linear kernels, such as radial basis function.…”

Section: Dominant Frequency Valuementioning

confidence: 99%

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A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Zhao¹,

Chen²,

Nguyên³

et al. 2023

Preprint

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Heart sound auscultation has been demonstrated to be beneficial in clinical usage for early screening of cardiovascular diseases. Due to the high requirement of well-trained professionals for auscultation, automatic auscultation benefiting from signal processing and machine learning can help auxiliary diagnosis and reduce the burdens of training professional clinicians. Nevertheless, classic machine learning is limited to performance improvement in the era of big data. Deep learning has achieved better performance than classic machine learning in many research fields, as it employs more complex model architectures with stronger capability of extracting effective representations. Deep learning has been successfully applied to heart sound analysis in the past years. As most review works about heart sound analysis were given before 2017, the present survey is the first to work on a comprehensive overview to summarise papers on heart sound analysis with deep learning in the past six years 2017-2022. We introduce both classic machine learning and deep learning for comparison, and further offer insights about the advances and future research directions in deep learning for heart sound analysis.

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