Integrating Deep Learning for Arrhythmia Detection with Automated Drug Delivery: A Comprehensive Approach to Cardiac Health Monitoring and Treatment

TL;DR

This paper presents an integrated system combining deep learning-based arrhythmia detection with automated drug delivery to improve early diagnosis, continuous monitoring, and personalized treatment of cardiac arrhythmias.

Contribution

It introduces a comprehensive approach that combines remote monitoring, real-time data analysis, and synchronized drug delivery for enhanced cardiac care.

Findings

Deep learning models effectively monitor arrhythmias using wearable data.

Automated drug delivery system synchronizes medication with patient needs.

The approach streamlines diagnosis and treatment, potentially improving outcomes.

Abstract

Arrhythmias are irregularities in the hearts electrical system which cause rapid and irregular heartbeats. These heart conditions affect over 33 million people globally and significantly increase the risk of severe complications, including stroke, heart failure, and sudden death. Modern screening and treatment approaches, like 12 lead ECG tests and analyzing patient medical history, use frameworks that dont address early onset of conditions and lack sufficient information to optimize treatment plans post diagnosis. This project aimed to enhance cardiac arrhythmias early diagnosis, monitoring, analysis, and treatment using an optimized 5 step patient pathway. We developed deep learning models using ECG, PPG, and SpO2 data to monitor conditions remotely with smartwatches and document arrhythmic episodes with relevant information, including daily patterns. We synthesized these into patient reports suitable for real world clinical, providing enough information to guide treatment decisions before any new diagnostics. For critical care in a hospitalized setting or personalized home care, we developed a novel drug delivery system synchronized with a phone via Bluetooth and uses prescription based or prediction based to deliver medication intravenously at the optimal time. This approach could reduce the need for additional diagnostic tests, streamline patient management, and optimize medication schedules to better align with the individuals physiological needs, significantly improving patient outcomes.