Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform
The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormo...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-04-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/8/1781 |
| _version_ | 1797604463325741056 |
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| author | GiriBabu Sinnapolu Shadi Alawneh Simon R. Dixon |
| author_facet | GiriBabu Sinnapolu Shadi Alawneh Simon R. Dixon |
| author_sort | GiriBabu Sinnapolu |
| collection | DOAJ |
| description | The work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU. |
| first_indexed | 2024-03-11T04:46:50Z |
| format | Article |
| id | doaj.art-8235c0fe8ed84adca4db6899f6cb3f6f |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T04:46:50Z |
| publishDate | 2023-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-8235c0fe8ed84adca4db6899f6cb3f6f2023-11-17T20:16:19ZengMDPI AGMathematics2227-73902023-04-01118178110.3390/math11081781Prediction and Analysis of Heart Diseases Using Heterogeneous Computing PlatformGiriBabu Sinnapolu0Shadi Alawneh1Simon R. Dixon2Electrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USAElectrical and Computer Engineering Department, Oakland University, Rochester, MI 48309, USADepartment of Cardiovascular Medicine, Beaumont Hospitals, Royal Oak, MI 48073, USAThe work in this paper helps study cardiac rhythms and the electrical activity of the heart for two of the most critical cardiac arrhythmias. Various consumer devices exist, but implementation of an appropriate device at a certain position on the body at a certain pressure point containing an enormous number of blood vessels and developing filtering techniques for the most accurate signal extraction from the heart is a challenging task. In this paper, we provide evidence of prediction and analysis of Atrial Fibrillation (AF) and Ventricular Fibrillation (VF). Long-term monitoring of diseases such as AF and VF occurrences is very important, as these will lead to occurrence of ischemic stroke, cardiac arrest and complete heart failure. The AF and VF signal classification accuracy are much higher when processed on a Graphics Processor Unit (GPU) than Central Processing Unit (CPU) or traditional Holter machines. The classifier COMMA-Z filter is applied to the highly-sensitive industry certified Bio PPG sensor placed at the earlobe and computed on GPU.https://www.mdpi.com/2227-7390/11/8/1781Graphics Processing Unit (GPU)proximity sensorsheart diseasesAtrial FibrillationVentricular Fibrillation |
| spellingShingle | GiriBabu Sinnapolu Shadi Alawneh Simon R. Dixon Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform Mathematics Graphics Processing Unit (GPU) proximity sensors heart diseases Atrial Fibrillation Ventricular Fibrillation |
| title | Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform |
| title_full | Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform |
| title_fullStr | Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform |
| title_full_unstemmed | Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform |
| title_short | Prediction and Analysis of Heart Diseases Using Heterogeneous Computing Platform |
| title_sort | prediction and analysis of heart diseases using heterogeneous computing platform |
| topic | Graphics Processing Unit (GPU) proximity sensors heart diseases Atrial Fibrillation Ventricular Fibrillation |
| url | https://www.mdpi.com/2227-7390/11/8/1781 |
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