Enhanced Classification of Phonocardiograms Using Modified Deep Learning
Cardiovascular diseases (CVD) are the foremost cause of death globally, highlighting the importance of effective diagnostic techniques. Phonocardiograms (PCG), known for their affordability and simplicity, are pivotal in assessing heart anomalies and identifying CVDs. Cardiac auscultation, while com...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10770210/ |
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| author | Awais Mahmood Habib Dhahri Mousa Alhajlah Abdulaziz Almaslukh |
| author_facet | Awais Mahmood Habib Dhahri Mousa Alhajlah Abdulaziz Almaslukh |
| author_sort | Awais Mahmood |
| collection | DOAJ |
| description | Cardiovascular diseases (CVD) are the foremost cause of death globally, highlighting the importance of effective diagnostic techniques. Phonocardiograms (PCG), known for their affordability and simplicity, are pivotal in assessing heart anomalies and identifying CVDs. Cardiac auscultation, while commonly employed for cardiac assessment, heavily relies on the clinician’s expertise, leading to a growing need for automated and objective cardiac sound analysis methods. This research focuses on developing an automated PCG classification system. Since the data is imbalanced, first, the data set was balanced using the random oversampling method and then the data audio augmentation method for the publicly accessible PhysioNet/CinC 2016 Challenge dataset. Instead of handicraft features, we converted the speech files into spectrograms and then fed them to the Convolutional neural network (CNN) model as images. The innovative approach involves a modified CNN integrated with dual classifiers: a SoftMax classifier and a Support Vector Machine (SVM), The proposed model demonstrates remarkable proficiency, achieving 97.85% accuracy with the SoftMax classifier and 98.28% accuracy with the SVM, surpassing the former. This model not only outperforms existing methods in PCG signal classification but also enhances computational efficiency and accuracy. |
| format | Article |
| id | doaj-art-95fb7060267c45919825eee438e69df9 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-95fb7060267c45919825eee438e69df92025-08-20T01:54:55ZengIEEEIEEE Access2169-35362024-01-011217890917891610.1109/ACCESS.2024.350792010770210Enhanced Classification of Phonocardiograms Using Modified Deep LearningAwais Mahmood0https://orcid.org/0000-0003-4163-7625Habib Dhahri1https://orcid.org/0000-0003-4668-7840Mousa Alhajlah2https://orcid.org/0000-0003-4799-6004Abdulaziz Almaslukh3https://orcid.org/0000-0002-2147-5772College of Applied Computer Science, King Saud University, Riyadh, Saudi ArabiaCollege of Applied Computer Science, King Saud University, Riyadh, Saudi ArabiaCollege of Applied Computer Science, King Saud University, Riyadh, Saudi ArabiaInformation System Department, King Saud University, Riyadh, Saudi ArabiaCardiovascular diseases (CVD) are the foremost cause of death globally, highlighting the importance of effective diagnostic techniques. Phonocardiograms (PCG), known for their affordability and simplicity, are pivotal in assessing heart anomalies and identifying CVDs. Cardiac auscultation, while commonly employed for cardiac assessment, heavily relies on the clinician’s expertise, leading to a growing need for automated and objective cardiac sound analysis methods. This research focuses on developing an automated PCG classification system. Since the data is imbalanced, first, the data set was balanced using the random oversampling method and then the data audio augmentation method for the publicly accessible PhysioNet/CinC 2016 Challenge dataset. Instead of handicraft features, we converted the speech files into spectrograms and then fed them to the Convolutional neural network (CNN) model as images. The innovative approach involves a modified CNN integrated with dual classifiers: a SoftMax classifier and a Support Vector Machine (SVM), The proposed model demonstrates remarkable proficiency, achieving 97.85% accuracy with the SoftMax classifier and 98.28% accuracy with the SVM, surpassing the former. This model not only outperforms existing methods in PCG signal classification but also enhances computational efficiency and accuracy.https://ieeexplore.ieee.org/document/10770210/Cardiovascular diseasedeep learningheart disease detectionmachine learning |
| spellingShingle | Awais Mahmood Habib Dhahri Mousa Alhajlah Abdulaziz Almaslukh Enhanced Classification of Phonocardiograms Using Modified Deep Learning IEEE Access Cardiovascular disease deep learning heart disease detection machine learning |
| title | Enhanced Classification of Phonocardiograms Using Modified Deep Learning |
| title_full | Enhanced Classification of Phonocardiograms Using Modified Deep Learning |
| title_fullStr | Enhanced Classification of Phonocardiograms Using Modified Deep Learning |
| title_full_unstemmed | Enhanced Classification of Phonocardiograms Using Modified Deep Learning |
| title_short | Enhanced Classification of Phonocardiograms Using Modified Deep Learning |
| title_sort | enhanced classification of phonocardiograms using modified deep learning |
| topic | Cardiovascular disease deep learning heart disease detection machine learning |
| url | https://ieeexplore.ieee.org/document/10770210/ |
| work_keys_str_mv | AT awaismahmood enhancedclassificationofphonocardiogramsusingmodifieddeeplearning AT habibdhahri enhancedclassificationofphonocardiogramsusingmodifieddeeplearning AT mousaalhajlah enhancedclassificationofphonocardiogramsusingmodifieddeeplearning AT abdulazizalmaslukh enhancedclassificationofphonocardiogramsusingmodifieddeeplearning |