RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power o...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-08-01
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| Series: | Applied System Innovation |
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| Online Access: | https://www.mdpi.com/2571-5577/7/5/77 |
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| author | Alaa Eleyan Ebrahim AlBoghbaish Abdulwahab AlShatti Ahmad AlSultan Darbi AlDarbi |
| author_facet | Alaa Eleyan Ebrahim AlBoghbaish Abdulwahab AlShatti Ahmad AlSultan Darbi AlDarbi |
| author_sort | Alaa Eleyan |
| collection | DOAJ |
| description | Heart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future. |
| format | Article |
| id | doaj-art-dc10a6969cfd47579c6df0315e53a2c6 |
| institution | OA Journals |
| issn | 2571-5577 |
| language | English |
| publishDate | 2024-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied System Innovation |
| spelling | doaj-art-dc10a6969cfd47579c6df0315e53a2c62025-08-20T02:11:01ZengMDPI AGApplied System Innovation2571-55772024-08-01757710.3390/asi7050077RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease PredictionAlaa Eleyan0Ebrahim AlBoghbaish1Abdulwahab AlShatti2Ahmad AlSultan3Darbi AlDarbi4College of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitCollege of Engineering and Technology, American University of the Middle East, Egaila 54200, KuwaitHeart disease, a global killer with many variations like arrhythmia and heart failure, remains a major health concern. Traditional risk factors include age, cholesterol, diabetes, and blood pressure. Fortunately, artificial intelligence (AI) offers a promising solution. We have harnessed the power of AI, specifically deep learning and convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for heart disease detection. Rhythmi leverages extensive medical data from databases like MIT-BIH and BIDMC. These data empower the training and testing of the developed deep learning model to analyze ECG signals with accuracy, precision, sensitivity, specificity, and F1-score in identifying arrhythmias and other heart conditions, with performances reaching 98.52%, 98.55%, 98.52%, 99.26%, and 98.52%, respectively. Moreover, we tested Rhythmi in real time using a mobile device with a single-lead ECG sensor. This user-friendly prototype captures the ECG signal, transmits it to Rhythmi’s dedicated website, and provides instant diagnosis and feedback on the patient’s heart health. The developed mobile ECG diagnosis device addresses the main problems of traditional ECG diagnostic devices such as accessibility, cost, mobility, complexity, and data integration. However, we believe that despite the promising results, our system will still need intensive clinical validation in the future.https://www.mdpi.com/2571-5577/7/5/77ECGarrhythmiaconvolutional neural networkartificial intelligencedeep learning |
| spellingShingle | Alaa Eleyan Ebrahim AlBoghbaish Abdulwahab AlShatti Ahmad AlSultan Darbi AlDarbi RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction Applied System Innovation ECG arrhythmia convolutional neural network artificial intelligence deep learning |
| title | RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction |
| title_full | RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction |
| title_fullStr | RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction |
| title_full_unstemmed | RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction |
| title_short | RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction |
| title_sort | rhythmi a deep learning based mobile ecg device for heart disease prediction |
| topic | ECG arrhythmia convolutional neural network artificial intelligence deep learning |
| url | https://www.mdpi.com/2571-5577/7/5/77 |
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