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<p>The most important organ of the human body is the heart as it pumps blood to various regions of the&#xa0;body. Abnormalities in the&#xa0;human heart are regarded as the major cause of death worldwide, as millions of people die prematurely due to heart disorders. The detection of heart disease at early stages is necessary for reducing the mortality and morbidity rate. Thus, automatic heart abnormality detection techniques have been utilized for detecting abnormalities in recent years. Meanwhile, most of these techniques did not correctly predict heart-related ailment cases. In this work, Tangent Artificial Protozoa Optimization- Siamese Convolution Neural Network (TaAPO-SCNN) is proposed for heart abnormality detection based on Electrocardiogram (ECG) and Phonocardiogram (PCG) signals. The TaAPO-SCNN utilizes pre-processing and feature extraction approaches to process the ECG and PCG signals. Here, the ECG signals are converted into ECG images, and then, image features are extracted. Further, the&#xa0;bandpass filter is employed to process the PCG signals and then the signal features are extracted. Following this, heart abnormality is detected using TaAPO-SCNN from the extracted features. Moreover, the TaAPO-SCNN obtained a&#xa0;high True Negative Rate (TNR) of 92.576%, accuracy of 93.333%, and True Positive Rate (TPR) of 94.285%.</p>

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Tangent Artificial Protozoa Optimization and SCNN for Enhanced Heart Abnormality Detection Using Multimodality

  • 期刊论文

  • 发表日期：

    2025年8月5日

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  • Namrata Gawande,
  • Dinesh Goyal,
  • Kriti Sankhla

摘要

The most important organ of the human body is the heart as it pumps blood to various regions of the body. Abnormalities in the human heart are regarded as the major cause of death worldwide, as millions of people die prematurely due to heart disorders. The detection of heart disease at early stages is necessary for reducing the mortality and morbidity rate. Thus, automatic heart abnormality detection techniques have been utilized for detecting abnormalities in recent years. Meanwhile, most of these techniques did not correctly predict heart-related ailment cases. In this work, Tangent Artificial Protozoa Optimization- Siamese Convolution Neural Network (TaAPO-SCNN) is proposed for heart abnormality detection based on Electrocardiogram (ECG) and Phonocardiogram (PCG) signals. The TaAPO-SCNN utilizes pre-processing and feature extraction approaches to process the ECG and PCG signals. Here, the ECG signals are converted into ECG images, and then, image features are extracted. Further, the bandpass filter is employed to process the PCG signals and then the signal features are extracted. Following this, heart abnormality is detected using TaAPO-SCNN from the extracted features. Moreover, the TaAPO-SCNN obtained a high True Negative Rate (TNR) of 92.576%, accuracy of 93.333%, and True Positive Rate (TPR) of 94.285%.