A Speed- and Power-Efficient SPIHT Design for Wearable Quality-On-Demand ECG Applications

Abstract

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.

Machine Learning Electrocardiogram for Mobile Cardiac Pattern Extraction

“…A global search method was applied to find the threshold τ that corresponds to a transition point in the histogram, which possesses the maximum perpendicular distance to the histogram hypotenuse as defined in (15), where b is a bin in the histogram, h{b} is a function returning the density value for bin b, and Dis{} is a function returning the distance from a point ((b, h(b)) to the hypotenuse that is drawn between the maximum point ((b max , h(b max )) and the rightest point ( b right , h b right . The learned distortion threshold can reflect the natural histogram transition point between high and low-quality raw heartbeat.…”

“…Generally, it is highly desirable to explore convenient and comfortable health management systems to enable long-term continuous and real-time precision medicine [ 1 , 6 , 8 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. We have previously reported a highly wearable ear-worn blood pressure monitoring system, in which the mobile ECG is used to determine the heartbeat occurrence time [ 22 ].…”

“…We name this kind of nonstandard and highly convenient lead configuration “mobile ECG” to distinguish it from traditional limb- or chest-based configurations. Mobile ECG is very suitable for long-term use, compared with many other studies’ configurations, which usually use the interlimb ECG that requires a wire or the touch of a finger, or the chest ECG that may need a chest strap [ 1 , 6 , 8 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 ]. In this study, we take special interest in whether the mobile ECG can be used to track the duration of the QRS complex, which is the central part of an ECG heartbeat.…”

Hardware Implementation Of ECG Signal Compression Using SPIHT

“…Reconfigurability is the distinctive feature for FPGA prototyping. The low power design SPIHT algorithm for ECG compression in wearable devices has been implemented [7].…”

Time–frequency localization using three-tap biorthogonal wavelet filter bank for electrocardiogram compressions

“…Current literature uses two-band WFBs to analyze ECG signals [4][5][6][7][8][9][10]. Poor resolution (∆ω = π/2) of low and highfrequency bands during signal decomposition is the major drawback of two-band WFBs.…”