PCG signal classification using a hybrid multi round transfer learning classifier

Ismail, Shahid; Ismail, Basit

doi:10.1016/j.bbe.2023.01.004

Cited by 6 publications

(2 citation statements)

References 41 publications

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“…Moreover, the latest article introduces a technique that utilizes high-resolution spectrum generation, spectrogram conversion, and multi-round training to address variations in analyzing PCG signals. The experimental results demonstrate that the proposed technique using a Chirplet Z-transformbased spectrogram with multiple rounds of training achieves high accuracy in multiclass classification while maintaining low computational cost [34]. Furthermore, the methodology was validated using multiple datasets with varying signal characteristics.…”

Section: Related Workmentioning

confidence: 93%

Raw Waveform-Based Custom Scalogram CRNN in Cardiac Abnormality Diagnosis

Radha,

Bansal,

Sharma

2024

IEEE Access

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Cardiovascular disease is a significant cause of death worldwide, emphasizing the crucial need for timely detection and diagnosis of heart abnormalities. This study presents a new approach that utilizes deep learning models to diagnose cardiac issues by analyzing raw phonocardiogram (PCG) signals. The proposed method introduces a novel technique called custom scalogram-based convolutional recurrent neural network (CS-CRNN). Diverging from conventional techniques, this model directly handles the raw PCG signals. These signals undergo a transformation into scalogram images within the initial layer of the CRNN architecture, without incorporating any learnable parameters. The results obtained from the CS-CRNN model are compared with traditional feature-based recurrent neural network (RNN) models.The comparison demonstrates comparable performance in both binary classification (normal and abnormal categories) and multiclass classification (5 categories). The CS-CRNN model directly handles raw PCG data and employs data augmentation to enhance performance on small datasets. It achieves an accuracy of 99.6% for binary classification and 98.6% and 99.7% before and after optimization for multiclass classification on the augmented dataset. The results show that the CS-CRNN model offers comparable performance to traditional methods, making it a promising tool for diagnosing cardiac abnormalities.

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Section: Related Workmentioning

confidence: 93%

Raw Waveform-Based Custom Scalogram CRNN in Cardiac Abnormality Diagnosis

Radha,

Bansal,

Sharma

2024

IEEE Access

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“…When doing convolutions, a large number of filters work together in parallel to extract outputs and then express those extracted outputs as activations. When several convolutions are used, the activations become even more expansive, which ultimately results in the formation of a feature map or vector for the associated input [41] The network that had been constructed over the course of this research included an architecture with a total of three convolutions. These layers were interconnected by additional layers in order to boost their efficiency in extracting features.…”

Section: Proposed Modelmentioning

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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