Automatic Detection and Classification of Systolic and Diastolic Profiles of PCG Corrupted Due to Limitations of Electronic Stethoscope Recording

Kambhampati, Ajay Babu; Ramkumar, Barathram

doi:10.1109/jsen.2020.3028373

Cited by 12 publications

(4 citation statements)

References 37 publications

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“…They tested the algorithm's performance using various ML algorithms (hidden semi-Markov model, multilayer perceptron (MLP), SVM, and KNN) on real-time phonocardiogram (PCG) and PCG in a standard database. The results showed that their algorithm outperformed traditional heart sound segmentation algorithms [ 186 ].…”

Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning

confidence: 99%

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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Section: Pathological Voice Recognition For Diagnosis and Evaluationmentioning

confidence: 99%

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Zhang

Qiu

et al. 2023

Computers in Biology and Medicine

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“…In addition, the study that employs wavelet transforms in conjunction with the ML approach for classification has seen considerable appliance [18]. It is now commonly regarded that the continuous wavelet transform (CWT) approach is the best suited for evaluating non-stationary PCG signals (having diverse frequencies and in time) [19][20][21]. The main advantage of this method is that it uses a continuous wavelet transform rather than discrete.…”

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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“…It's against this backdrop that our research ventured into developing a digital stethoscope equipped with the capacity to record phonocardiographic data, subsequently processed by state-of-the-art machine learning algorithms [9]. This innovative approach aims not only to enhance the granularity of heart sound analysis but also to democratize the diagnostic process, rendering it less reliant on individual expertise and more on objective, data-driven analytics [10]. By doing so, the intention is to unearth those elusive early markers of heart disease that, if addressed timely, could drastically alter prognostic outcomes.…”

Section: Introductionmentioning

confidence: 99%

Digital Stethoscope for Early Detection of Heart Disease on Phonocardiography Data

Omarov,

Tuimebayev,

Abdrakhmanov

et al. 2023

IJACSA

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The burgeoning realm of digital healthcare has unveiled a novel diagnostic instrument: a digital stethoscope tailored for the early detection of heart disease as elucidated in this research. By harnessing the nuanced capabilities of phonocardiography, this device captures intricate heart sounds, subsequently processed through advanced machine learning algorithms. Traditional stethoscopes, although indispensable, might miss subtle anomaliesa lacuna this digital counterpart addresses by meticulously analyzing phonocardiographic data for the slightest deviations indicative of cardiac anomalies. As the digital stethoscope delves into this trove of aural cues, the machine learning component discerns patterns and irregularities often imperceptible to human auditors. The confluence of these digital acoustics and computational analytics not only augments the accuracy of early heart disease diagnosis but also facilitates the archival of this data, engendering a continuous, longitudinal assessment of cardiac health. The initial foray into real-world application registered an encouraging precision rate, cementing its potential as an invaluable asset in preemptive cardiac care. With this innovation, we stand on the cusp of a paradigm shift in how heart diseases are diagnosed, making strides towards timely interventions and improved patient outcomes.

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Automatic Detection and Classification of Systolic and Diastolic Profiles of PCG Corrupted Due to Limitations of Electronic Stethoscope Recording

Cited by 12 publications

References 37 publications

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

1D Convolutional Neural Network for Detecting Heart Diseases using Phonocardiograms

Digital Stethoscope for Early Detection of Heart Disease on Phonocardiography Data

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