RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction DOI Creative Commons
Alaa Eleyan, Ebrahim Alboghbaish,

Abdulwahab AlShatti

et al.

Applied System Innovation, Journal Year: 2024, Volume and Issue: 7(5), P. 77 - 77

Published: Aug. 29, 2024

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains major health concern. Traditional risk factors include age, cholesterol, diabetes, blood pressure. Fortunately, artificial intelligence (AI) offers promising solution. We have harnessed the power of AI, specifically deep learning convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for disease detection. Rhythmi leverages extensive medical data from databases MIT-BIH BIDMC. These empower training testing developed model analyze signals accuracy, precision, sensitivity, specificity, F1-score in identifying arrhythmias other conditions, performances reaching 98.52%, 98.55%, 99.26%, respectively. Moreover, we tested real time using single-lead sensor. This user-friendly prototype captures signal, transmits it Rhythmi’s dedicated website, provides instant feedback on patient’s health. The addresses main problems traditional diagnostic devices such as accessibility, cost, mobility, complexity, integration. However, believe that despite results, our system will still need intensive clinical validation future.

Language: Английский

RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction DOI Creative Commons
Alaa Eleyan, Ebrahim Alboghbaish,

Abdulwahab AlShatti

et al.

Applied System Innovation, Journal Year: 2024, Volume and Issue: 7(5), P. 77 - 77

Published: Aug. 29, 2024

Heart disease, a global killer with many variations like arrhythmia and heart failure, remains major health concern. Traditional risk factors include age, cholesterol, diabetes, blood pressure. Fortunately, artificial intelligence (AI) offers promising solution. We have harnessed the power of AI, specifically deep learning convolutional neural networks (CNNs), to develop Rhythmi, an innovative mobile ECG diagnosis device for disease detection. Rhythmi leverages extensive medical data from databases MIT-BIH BIDMC. These empower training testing developed model analyze signals accuracy, precision, sensitivity, specificity, F1-score in identifying arrhythmias other conditions, performances reaching 98.52%, 98.55%, 99.26%, respectively. Moreover, we tested real time using single-lead sensor. This user-friendly prototype captures signal, transmits it Rhythmi’s dedicated website, provides instant feedback on patient’s health. The addresses main problems traditional diagnostic devices such as accessibility, cost, mobility, complexity, integration. However, believe that despite results, our system will still need intensive clinical validation future.

Language: Английский

Citations

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