Detection of Phonocardiogram Event Patterns in Mitral Valve Prolapse: An Automated Clinically Relevant Explainable Diagnostic Framework

Bs, Rajeshwari; Patra, Madhurima; Sinha, Aman; Sengupta, Arnab; Ghosh, Nirmalya

doi:10.1109/tim.2023.3240995

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…Both of them, on the other hand, relied on a limited open-source database that included information from 47 participants. The one-dimensional convolutional neural network has been used extensively for the classification tasks in the research on the identification of different heart disorders, with the electrocardiogram signal serving as the input [35].…”

Section: Literature Reviewmentioning

confidence: 99%

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Baikuvekov¹,

Tolep²,

Sultan³

et al. 2023

IJACSA

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According to estimations made by World Health Organization, heart disease is the largest cause of mortality throughout the globe, and it is safe to assume that diagnosing heart diseases in their earliest stages is very essential. Diagnosis of cardiovascular disease may be carried out by detection of interference in cardiac signals, one of which is called phonocardiography, and it can be accomplished in a number of various ways. Using phonocardiogram (PCG) inputs and deep learning, the researchers aim to develop a classification system for different types of heart illness. The slicing and normalization of the signal served as the first step in the study's signal preprocessing, which was subsequently followed by a wavelet based transformation method that employs mother wavelet analytic morlet. The results of the decomposition are first shown with the use of a scalogram, afterwards, they are utilized as input for the deep CNN. In this investigation, the analyzed PCG signals were separated into categories, denoting normal and pathological heart sounds. The entire utilized data was divided into two categories as training and test data as 80% to 20%. The developed model demonstrates the degree of clinical diagnosis, sensitivity, specificity and AUC-ROC value.As a result, it has been determined that the proposed method was superior to the mother wavelet as well as other classifier approaches. Consequently, we were able to acquire an electronic stethoscope that has a diagnostic accuracy of more than 90% when it comes to identifying cardiac problems. To be more specific, the proposed deep CNN model has an accuracy of 93.25% in identifying aberrant heart sounds and 93.50% in identifying regular heartbeats. In addition, given the fact that an examination may be completed in only 15 seconds, speed is the primary advantage offered by the suggested stethoscope.

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Section: Literature Reviewmentioning

confidence: 99%

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Baikuvekov¹,

Tolep²,

Sultan³

et al. 2023

IJACSA

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show abstract

“…Among different methods, Grad-CAM exhibits the best performance in terms of localization [22], which is an expected characteristic of heatmaps. A well-localized heatmap can display the borders of the regions encompassing the features that contributed the most toward the classification outcomes [23] and may offer better discrimination [24] and conceivable explanations for the choices crafted by the cognate CNNs [25].…”

mentioning

confidence: 99%

Grad-CAM: Understanding AI Models

Wang,

Zhang

2023

Computers, Materials &Amp; Continua

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Detection of Phonocardiogram Event Patterns in Mitral Valve Prolapse: An Automated Clinically Relevant Explainable Diagnostic Framework

Cited by 2 publications

References 31 publications

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Grad-CAM: Understanding AI Models

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