A Speed- and Power-Efficient SPIHT Design for Wearable Quality-On-Demand ECG Applications
| Autor: | King-Chu Hung, Jui-Hung Hsieh, Meng-Ju Shih, Yu Ling Lin |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Discrete wavelet transform
Speedup Databases Factual Computer science 0206 medical engineering 02 engineering and technology Electrocardiography Wearable Electronic Devices Set partitioning in hierarchical trees Wavelet Health Information Management 0202 electrical engineering electronic engineering information engineering Humans Electrical and Electronic Engineering Very-large-scale integration business.industry Arrhythmias Cardiac Data Compression 020601 biomedical engineering Computer Science Applications Computer engineering Status register Compression ratio 020201 artificial intelligence & image processing Algorithm design business Algorithms Computer hardware Biotechnology |
| Zdroj: | IEEE Journal of Biomedical and Health Informatics. 22:1456-1465 |
| ISSN: | 2168-2208 2168-2194 |
| DOI: | 10.1109/jbhi.2017.2773097 |
| Popis: | In this paper, a speed and power-efficient set partitioning in hierarchical trees (SPIHT) design is introduced for one-dimensional (1-D) wavelet-based electrocardiography (ECG) compression systems with quality guarantee. To achieve real-time and low-power design objectives toward wearable quality-on-demand (QoD) ECG applications, we first propose a coding-time- and computation-efficient SPIHT algorithm using various types of coding status register files to overcome the disadvantages of low coding speeds and complicated hardware architectures characterizing prior SPIHT algorithms resulting from the necessity of dynamic computation and arrangement in the sorting and refinement processing phase. Second, a highly pipelined and power-efficient very large scale integration (VLSI) architecture is developed to implement a high-performance and low-power SPIHT design based on the proposed algorithm. The final simulation results demonstrate that our proposed algorithm can speed up the average coding time 1.52 to 2.74 times compared to prior work with an identical compression ratio for an 11-level $1024\times 1\,1-{\rm{D}}$ discrete wavelet transform at diverse target percentage root-mean-square differences (PRDT) on various MIT-BIH arrhythmia datasets. Applied to wearable wavelet-based QoD ECG applications, our proposed VLSI architecture attains a working frequency of 740 MHz and consumes an average of $\text{23}\ \mu {\text{W}}$ of power with Taiwan Semiconductor Manufacturing Company 90-nm CMOS technology, which shows the effectiveness of speed and power over the state-of-the-art designs. |
| Databáze: | OpenAIRE |
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