Heart Sound Signals Segmentation and Multiclass Classification

Bourouhou, Abdennaser; Jilbab, Abdelilah; Nacir, Chafik; Hammouch, Ahmed

doi:10.3991/ijoe.v16i15.16817

Cited by 6 publications

(7 citation statements)

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“…Moreover, the expectation maximisation algorithm developed in [71] searched for sojourn time distribution parameters of an HSMM for each subject. Many studies [6], [31], [40], [41], [43], [72]- [79] have employed logistic regression-based HSMM (LR-HSMM) proposed in [80] for heart sound segmentation. LR was incorporated to predict the probability of P [s j |o t ], and B is then computed with the Bayes rule.…”

Section: B Segmentationmentioning

confidence: 99%

“…, where P (X|y) is the likelihood probability distribution. The Naïve Bayes Classifier [41], [91], [102], [103] was widely used for heart sound classification due to its advantage of being easy-touse. Gaussian Mixture Models (GMMs) [53], [95] were used to estimate the data distribution by optimising the weights of Gaussian mixture components and mean and variance in each component.…”

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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Section: B Segmentationmentioning

confidence: 99%

Section: Dominant Frequency Valuementioning

confidence: 99%

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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“…In the early years, many researchers emphasized signal segmentation to divide heart sounds into S1 (the frst heart sound), systolic, S2 (the second heart sound), and diastolic segments [6][7][8][9][10][11]. However, accurate segmentation relies on the ECG signal to locate the boundaries of heart sounds.…”

Section: Introductionmentioning

confidence: 99%

A Computer-Aided Heart Valve Disease Diagnosis System Based on Machine Learning

Ding

Kan

et al. 2023

Journal of Healthcare Engineering

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Cardiac auscultation is a noninvasive, convenient, and low-cost diagnostic method for heart valvular disease, and it can diagnose the abnormality of the heart valve at an early stage. However, the accuracy of auscultation relies on the professionalism of cardiologists. Doctors in remote areas may lack the experience to diagnose correctly. Therefore, it is necessary to design a system to assist with the diagnosis. This study proposed a computer-aided heart valve disease diagnosis system, including a heart sound acquisition module, a trained model for diagnosis, and software, which can diagnose four kinds of heart valve diseases. In this study, a training dataset containing five categories of heart sounds was collected, including normal, mitral stenosis, mitral regurgitation, and aortic stenosis heart sound. A convolutional neural network GoogLeNet and weighted KNN are used to train the models separately. For the model trained by the convolutional neural network, time series heart sound signals are converted into time-frequency scalograms based on continuous wavelet transform to adapt to the architecture of GoogLeNet. For the model trained by weighted KNN, features from the time domain and time-frequency domain are extracted manually. Then feature selection based on the chi-square test is performed to get a better group of features. Moreover, we designed software that lets doctors upload heart sounds, visualize the heart sound waveform, and use the model to get the diagnosis. Model assessments using accuracy, sensitivity, specificity, and F1 score indicators are done on two trained models. The results showed that the model trained by modified GoogLeNet outperformed others, with an overall accuracy of 97.5%. The average accuracy, sensitivity, specificity, and F1 score for diagnosing four kinds of heart valve diseases are 98.75%, 96.88%, 99.22%, and 97.99%, respectively. The computer-aided diagnosis system, with a heart sound acquisition module, a diagnostic model, and software, can visualize the heart sound waveform and show the reference diagnostic results. This can assist in the diagnosis of heart valve diseases, especially in remote areas, which lack skilled doctors.

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“…This paper provides researchers with the analytical ability to calculate the effect of intentional and unintentional interfering in the CSS demand channel on the processing efficiency of the demand signals of the systems in question. There are many works focused on handling many signal treatments especially in heart sound _signals as in [16], IoT as in the line of Fiber-Optic [17], also in sensors in IoT [18,19]. The rest of this article is organized as follows: Section 2 presents a brief description of the signal and interference flows in the CSS.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the Interference’s Impact of Cooperative Surveillance Systems Signals Processing for Healthcare

et al. 2022

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Patient signals produced from a physical device, such as electrocardiography (ECG), which records the electrical activity of the heart, are vulnerable to keep noise due to various physical constraints of acquisition devices. It is critical for the detection and diagnosis of a variety of diseases. An ECG signal should be displayed as clean and clear as feasible due to its relevance in assisting physicians and doctors in making appropriate judgments. ECG is vulnerable to many types of noise because it is an electrical signal. To improve the quality of assistance for consumers Info a surveillance system by cooperative surveillance systems, high-quality data processing by the observed surveillance systems is required, which predetermines the requirement for high noise immunity of the latter. At the same time, the basics of building cooperative surveillance systems as a network of two-channel asynchronous information communication systems which include numeral of transmitting and receiving process using diverse frequency limits for reception and transmission, failure-prone open single-channel queuing systems, and request signals do not allow to provide the required the noise resistance of the systems under consideration. This paper first gives a characterization of request and interference signal flows in cooperative surveillance systems and briefly examines the features of request signals and their effect on the immunity of cooperative surveillance systems. Then, it calculates the noise resistance of the demand signals of the cooperative surveillance systems for the probabilities of errors: signal skipping; First- and second-degree false alarms in the case of unrelated events, unintended and intra-system impulse interferences in the request channel

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Heart Sound Signals Segmentation and Multiclass Classification

Cited by 6 publications

References 0 publications

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

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

A Computer-Aided Heart Valve Disease Diagnosis System Based on Machine Learning

Evaluation of the Interference’s Impact of Cooperative Surveillance Systems Signals Processing for Healthcare

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