Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals
Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tail...
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Elsevier
2025-02-01
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844025006371 |
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| author | Ihtisham Ul Haq Ghassan Husnain Yazeed Yasin Ghadi Nisreen Innab Masoud Alajmi Hanan Aljuaid |
| author_facet | Ihtisham Ul Haq Ghassan Husnain Yazeed Yasin Ghadi Nisreen Innab Masoud Alajmi Hanan Aljuaid |
| author_sort | Ihtisham Ul Haq |
| collection | DOAJ |
| description | Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60–650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment. |
| format | Article |
| id | doaj-art-04ba54383ba74f138dbb6c4386c396fe |
| institution | Kabale University |
| issn | 2405-8440 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj-art-04ba54383ba74f138dbb6c4386c396fe2025-01-31T05:12:05ZengElsevierHeliyon2405-84402025-02-01113e42257Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signalsIhtisham Ul Haq0Ghassan Husnain1Yazeed Yasin Ghadi2Nisreen Innab3Masoud Alajmi4Hanan Aljuaid5Department of Computer Science, Modeling, Electronics and Systems (DIMES), University of Calabria, 87036, Rende, CS, ItalyDepartment of Computer Science, CECOS University of IT and Emerging Sciences, Peshawar, 25000, PakistanDepartment of Computer Science, Al Ain University, United Arab EmiratesDepartment of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Diriyah, 13713, Riyadh, Saudi ArabiaDepartment of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi ArabiaComputer Sciences Department, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University (PNU), P.O. Box 84428, Riyadh, 11671, Saudi Arabia; Corresponding author.Congenital heart disease (CHD), impacting around 1 % of infants worldwide, constitutes a significant healthcare challenge. Early detection is crucial, however constrained by the intricacies of conventional diagnostic techniques such as auscultation and echocardiography. This research presents a tailored one-dimensional convolutional neural network (1D-CNN) for the classification of phonocardiogram (PCG) signals into normal or abnormal categories, providing an automated and efficient solution for congenital heart disease (CHD) diagnosis. The model was trained on a composite dataset consisting of local pediatric PCG signals and publicly accessible dataset. Preprocessing methods, such as low- and high-pass filtering (60–650 Hz), resampling, and noise reduction, were utilized to enhance signal quality. Data augmentation techniques, including chunking, padding, and pitch-shifting, were employed to rectify dataset imbalances and improve model efficacy. Experimental results indicate substantial enhancements, attaining an accuracy of 98.56 %, precision of 98.56 %, F1 score of 98.55 %, sensitivity of 0.98, and specificity of 0.99. The comparative analysis demonstrates the proposed approach's superiority over current methods regarding accuracy and reliability. The research highlights the promise of combining modern signal processing with deep learning for efficient CHD screening. The suggested model exhibits outstanding performance yet, issues like dataset variability and noise persist. Future endeavors involve extending to multiclass categorization and assessing performance across a wider range of medical problems. This study represents a significant advancement in accessible, automated CHD diagnoses, enhancing clinical competence to elevate pediatric treatment.http://www.sciencedirect.com/science/article/pii/S2405844025006371PhonocardiogramCongenital heart diseasePhonocardiographyElectrocardiogram |
| spellingShingle | Ihtisham Ul Haq Ghassan Husnain Yazeed Yasin Ghadi Nisreen Innab Masoud Alajmi Hanan Aljuaid Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals Heliyon Phonocardiogram Congenital heart disease Phonocardiography Electrocardiogram |
| title | Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals |
| title_full | Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals |
| title_fullStr | Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals |
| title_full_unstemmed | Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals |
| title_short | Enhancing pediatric congenital heart disease detection using customized 1D CNN algorithm and phonocardiogram signals |
| title_sort | enhancing pediatric congenital heart disease detection using customized 1d cnn algorithm and phonocardiogram signals |
| topic | Phonocardiogram Congenital heart disease Phonocardiography Electrocardiogram |
| url | http://www.sciencedirect.com/science/article/pii/S2405844025006371 |
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