스마트 환자 모니터링 : 다수 환자를 위한 AI 기반 실시간 시스템

Abstract

Cardiovascular disease (CVD) is a leading cause of death worldwide, necessitating electrocardiogram (ECG) monitoring for its early detection and appropriate treatment. Particularly for hospitalized patients, continuous real-time ECG monitoring can enhance treatment efficacy. With advancements in technology, IoT devices such as wearable sen- sors are being integrated for Real-Time ECG monitoring system. Centralized systems enable medical staff to monitor multiple patients simultaneously, thereby improving the efficiency of decision support. We developed a system in which a self-scheduling-based parallel processing and adaptive batching method is employed to process real-time ECG data from multiple patients in a single-system. In this system, independently operating algorithm modules dynamically retrieve patient data for analysis through self-scheduling. Different patient data are processed, and the batch size is flexibly adjusted based on the volume of the data, enabling real-time data processing. The proposed system was evaluated in two aspects: real-time processing performance and algorithm performance. The proposed system could enable real-time processing for up to 240 patients, achieving an average completion time of 2.788 s, which was shorter than the input interval of 3-s. The performance of the algorithm was evaluated in a cohort of 80 hospitalized patients who underwent radiofrequency catheter ablation, demonstrating arrhythmia detection performance with a sensitivity of 99.429% and a positive predictive value of 91.238%. This system can efficiently and rapidly process data within a single-system, even when utilizing multiple wearable sensors, allowing for the low-cost monitoring of a large number of patients