# RHYTHMI: A Deep Learning-Based Mobile ECG Device for Heart Disease Prediction
## 6 Conclusions  
This study aimed to introduce Rhythmi, a mobile ECG diagnosis device powered by deep learning and convolutional neural networks (CNNs) that was developed to provide accessible and accurate heart disease detection, particularly for conditions such as arrhythmia and heart failure. The device was trained using extensive medical data from reputable databases, achieving high accuracy in identifying various heart conditions. Moreover, the confusion matrix analysis demonstrated Rhythmi's effectiveness and ability to distinguish between heart conditions with minimal errors, highlighting its robustness and reliability in real-world applications. The few misclassifications observed between NSR and ARR suggest areas for further refinement, but overall, the results indicate that the device can accurately diagnose heart diseases in real time. The impact of Rhythmi extends beyond the immediate clinical setting. By integrating AI into a mobile platform, the device addresses the significant limitations of traditional ECG diagnostic methods, including issues related to accessibility, cost, mobility, and data integration. This innovation represents a significant advancement in biomedical engineering, offering a portable, user-friendly solution that can be easily used by both healthcare professionals and patients. But, despite its strong performance and user-friendliness, the system has limitations that limit its general adoption. The use of only two databases restricts its ability to recognize diverse arrhythmias, including critical conditions such as Long QT syndrome. Additionally, the generic medical report lacks patient-specific information and is not personalized.  
In the broader context of the healthcare system, Rhythmi has the potential to revolutionize the early detection and management of heart diseases, enabling timely intervention and reducing the burden on healthcare facilities. By making advanced diagnostic tools more accessible, Rhythmi contributes to the democratization of healthcare, ensuring that high-quality cardiac care is available to a wider population, particularly in remote or underserved areas. Our plans include expanding the ECG dataset to encompass diverse heart conditions, enhancing medical reports with AI-generated insights and blood pressure data, and improving application accessibility through an AI-powered chatbot.  
Conceptualization, A.E. and E.A.; Data curation, E.A., A.A. ([PERSON]), A.A. ([PERSON]) and D.A.; Formal analysis, A.E. and E.A.; Funding acquisition, A.E.; Investigation, A.A. ([PERSON]) and A.A. ([PERSON]); Methodology, A.E.; Project administration, A.E.; Resources, A.A. ([PERSON]) and D.A.; Software, E.A. and A.A. ([PERSON]); Supervision, A.E.; Validation, E.A., A.A. ([PERSON]) and D.A.; Visualization, E.A.; Writing--original draft, E.A., A.A. ([PERSON]), A.A. ([PERSON]) and D.A.; Writing--review and editing, A.E. and E.A. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The databases used in this research are available at [[https://physionet.org/about/database/](https://physionet.org/about/database/)]([https://physionet.org/about/database/](https://physionet.org/about/database/)) (accessed on 1 March 2024).  
The authors are the inventors of patent No. 20 2024 100 997. IPC: G16H 50/20.