A Real-Time FPGA-Based Accelerator for ECG Analysis and Diagnosis Using Association-Rule Mining

Abstract

Telemedicine provides health care services at a distance using information and communication technologies, which intends to be a solution to the challenges faced by current health care systems with growing numbers of population, increased demands from patients, and shortages in human resources. Recent advances in telemedicine, especially in wearable electrocardiogram (ECG) monitors, call for more intelligent and efficient automatic ECG analysis and diagnostic systems. We present a streaming architecture implemented on Field-Programmable Gate Arrays (FPGAs) to accelerate real-time ECG signal analysis and diagnosis in a pipelining and parallel way. Association-rule mining is employed to generate early diagnostic results by matching features of ECG with generated association rules. To improve performance of the processing, we propose a hardware-oriented data-mining algorithm named Bit_Q_Apriori . The corresponding hardware implementation indicates a good scalability and outperforms other hardware designs in terms of performance, throughput, and hardware cost.