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

被引：28

作者：

Seidel, Henrique Bestani ^{[1
]}

Azevedo da Rosa, Morgana Macedo ^{[1
]}

Paim, Guilherme ^{[2
]}

Cesar da Costa, Eduardo Antonio ^{[1
]}

Almeida, Sergio J. M. ^{[1
]}

Bampi, Sergio ^{[2
]}

机构：

[1] Univ Catolica Pelotas UCPel, Grad Program Elect Engn & Comp, BR-96015560 Pelotas, RS, Brazil

[2] Fed Univ Rio Grande do Sul UFRGS, Informat Inst, Grad Program Microelect PGMICRO, BR-90040060 Porto Alegre, RS, Brazil

来源：

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS | 2021年 / 68卷 / 05期

关键词：

Electrocardiography; Transforms; Hardware; Discrete wavelet transforms; Computer architecture; Signal processing algorithms; Signal processing; Approximate computing; Haar discrete wavelet transform; VLSI design; energy efficiency; ECG processing; QRS-DETECTION ALGORITHM; DESIGN; IMAGE; CLASSIFICATION; ARCHITECTURE; ACQUISITION; DETECTOR; DCT;

D O I：

10.1109/TCSI.2021.3057584

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

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.

引用

页码：1814 / 1826

页数：13