Key result
Scheme VIII for complex multiplication in FFT architecture achieved the lowest dynamic power consumption of 116 mW for 1024-point FFT calculations in wearable ECG gadgets.
Why the study?
Wearable ECG gadgets for real-time analysis face computational and power challenges when processing multi-lead signals and Fast Fourier Transform on large datasets in battery-operated devices.
Implementing pre-computation techniques for FFT algorithms in wearable ECG devices can optimize dynamic power consumption, addressing a key computational challenge in real-time biomedical monitoring.
May extend wearable ECG monitoring duration; leaves open prospective clinical validation.
The focus on wearable devices for biomedical applications is gaining a lot of research and market interest. Heart diseases remain by far the main cause of death and a challenging problem for biomedical engineers to monitor and analyze. Electrocardiography (ECG) is an essential practice in heart medicine. However, wearable ECG gadgets for real time analysis still faces computational challenges, especially when multiple lead signals are to be analyzed in parallel, in real time, and under increasing sampling frequencies and battery operated gadgets. Another challenge is the computation of Fast Fourier Transform (FFT) on huge amounts of data that may grow to days of recordings. In this research we present the comparative study of FFT calculation best suitable for power optimized applications in the biomedical field and exclusive performance enhancement by reducing dynamic power consumption. ECG application specific to FFT calculation to the final hardware-software( HW/SW) architecture is the focus of this paper.
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Pankaj Joshi (2020) studied Cardiovascular diseases (ECG monitoring). Pre-computation schemes for complex multiplication in FFT vs. Standard complex multiplication was evaluated on Dynamic power consumption. Scheme VIII for complex multiplication in FFT architecture achieved the lowest dynamic power consumption of 116 mW for 1024-point FFT calculations in wearable ECG gadgets.
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