Cardiovascular Disease Recognition Based on Heartbeat Segmentation and Selection Process
| Autor: | Amal Abdullah AlMansour, Mehrez Boulares, Ahmed Barnawi, Reem Alotaibi |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
heart sounds
Heartbeat Stethoscope Computer science Multifunction cardiogram Health Toxicology and Mutagenesis convolutional neural network Heart Auscultation Convolutional neural network Article law.invention law Artificial Intelligence Heart Rate denoising Humans Medical diagnosis Phonocardiogram business.industry segmentation Public Health Environmental and Occupational Health deep learning Pattern recognition CVD PCG Cardiovascular Diseases Heart sounds Medicine Artificial intelligence Neural Networks Computer business Algorithms |
| Zdroj: | International Journal of Environmental Research and Public Health Volume 18 Issue 20 International Journal of Environmental Research and Public Health, Vol 18, Iss 10952, p 10952 (2021) |
| ISSN: | 1660-4601 |
| Popis: | Assessment of heart sounds which are generated by the beating heart and the resultant blood flow through it provides a valuable tool for cardiovascular disease (CVD) diagnostics. The cardiac auscultation using the classical stethoscope phonological cardiogram is known as the most famous exam method to detect heart anomalies. This exam requires a qualified cardiologist, who relies on the cardiac cycle vibration sound (heart muscle contractions and valves closure) to detect abnormalities in the heart during the pumping action. Phonocardiogram (PCG) signal represents the recording of sounds and murmurs resulting from the heart auscultation, typically with a stethoscope, as a part of medical diagnosis. For the sake of helping physicians in a clinical environment, a range of artificial intelligence methods was proposed to automatically analyze PCG signal to help in the preliminary diagnosis of different heart diseases. The aim of this research paper is providing an accurate CVD recognition model based on unsupervised and supervised machine learning methods relayed on convolutional neural network (CNN). The proposed approach is evaluated on heart sound signals from the well-known, publicly available PASCAL and PhysioNet datasets. Experimental results show that the heart cycle segmentation and segment selection processes have a direct impact on the validation accuracy, sensitivity (TPR), precision (PPV), and specificity (TNR). Based on PASCAL dataset, we obtained encouraging classification results with overall accuracy 0.87, overall precision 0.81, and overall sensitivity 0.83. Concerning Micro classification results, we obtained Micro accuracy 0.91, Micro sensitivity 0.83, Micro precision 0.84, and Micro specificity 0.92. Using PhysioNet dataset, we achieved very good results: 0.97 accuracy, 0.946 sensitivity, 0.944 precision, and 0.946 specificity. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|