Approximate Pruned and Truncated Haar Discrete Wavelet Transform VLSI Hardware for Energy-Efficient ECG Signal Processing

• Published in: IEEE transactions on circuits and systems. I, Regular papers Vol. 68; no. 5; pp. 1814 - 1826
• Main Authors: Seidel, Henrique Bestani, da Rosa, Morgana Macedo Azevedo, Paim, Guilherme, da Costa, Eduardo Antonio Cesar, Almeida, Sergio J. M., Bampi, Sergio
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Approximate computing; Computer architecture; Discrete Wavelet Transform; Discrete wavelet transforms; ECG processing; Electrocardiography; Embedded systems; Energy efficiency; Haar discrete wavelet transform; Hardware; Integrated circuits; Multimedia; Pruning; Signal processing; Signal processing algorithms; Signal to noise ratio; Transforms; VLSI design; Wavelet transforms; Wearable technology
• ISSN: 1549-8328; 1558-0806
• DOI: 10.1109/TCSI.2021.3057584

The approximate computing paradigm emerged as a key alternative for trading off accuracy and energy efficiency. Error-tolerant applications, such as multimedia and signal processing, can process the information with lower-than-standard accuracy at the circuit level while still fulfilling a good and acceptable service quality at the application level. The automatic detection of R-peaks in an electrocardiogram (ECG) signal is the essential step preceding ECG processing and analysis. The Haar discrete wavelet transform (HDWT) is a low-complexity pre-processing filter suitable to detect ECG R-peaks in embedded systems like wearable devices, which are incredibly energy-constrained. This work presents an approximate HDWT hardware architecture for ECG processing at very high energy efficiency. Our best-proposal employing pruning within the approximate HDWT hardware architecture requires just seven additions. The use of a truncation technique to improve energy efficiency is also investigated herein by observing the evolution of the signal-to-noise ratio and the ultimate impact in the ECG peak-detection application. This research finds that our HDWT approximate hardware architecture proposal accepts higher truncation levels than the original HDWT. In summary: Our results show about 9 times energy reduction when combining our HDWT matrix approximation proposal with the pruning and the highest acceptable level of truncation while still maintaining the R-peak detection performance accuracy of 99.68% on average.