Abstract
Recently, with an increase in the number of healthcare devices, studies measuring and diagnosing electrocardiogram (ECG) signals in daily life are emerging. ECG signal analysis is an essential study area that can diagnose fatal heart abnormalities in humans at an early stage. Conventional signal detection uses one reference beat to diagnose ECG signals; thus, the detection rate is different for each person. In this study, we design a system that can learn a reference beat and diagnose ECG signals in real-time using hardware accelerators with the approximated template-based ECG diagnosis algorithm proposed in the previous study. The proposed algorithm can easily perform personalized learning, increasing the detection rate since it has faster learning time and consumes less memory than the existing algorithm. The learning data, which occupies a small memory space, enables real-time and simultaneous diagnosis of several people. We confirmed that the proposed ECG diagnosis algorithm is suitable for hardware acceleration by accelerating the ECG signal diagnosis and measuring the parallelized result using Alveo field-programmable gate array (FPGA). The ECG diagnosis algorithm, implemented at the FPGA in real-time, can flexibly determine reference beats that vary depending on the person and diagnose each person’s signal. The experimental results showed that the time required to diagnose the ECG signals of five people containing 1987 beats takes 5.70 s with software and 0.572 s with hardware accelerators, which is 89.96% shorter than software execution time.
Highlights
Since the average life expectancy has been extended due to the recent development of medical technology, interest in healthcare devices for managing health is increasing
In ECG signal analysis, which diagnoses a large amount of data, the learning process takes up a low percentage of the total execution time because once a reference signal is found, it is no longer executed
We tried reducing the execution time and power consumption by executing the learning process, which occupies a low percentage of the total execution, as an application algorithm, and the diagnosis process, which occupies a high percentage of total execution, as a hardware accelerator
Summary
Since the average life expectancy has been extended due to the recent development of medical technology, interest in healthcare devices for managing health is increasing. Studies have been conducted to create a light-weight wearable system that measures and analyzes vital data using embedded devices [1], [2]. The electrocardiogram (ECG) signal, one of the vital signals that can be measured using a healthcare device, is measured by detecting and amplifying electrical signals generated when the heart beats. It is the best signal for real-time diagnosis of heart abnormalities, which are fatal for humans [3]. ECG signals are sampled at high frequencies above 100 Hz. Abnormal beat varies rarely, so ECG signal must be measured and analyzed for a long time, more than several hours.
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