Key result
The proposed discrete wavelet transform-based QRS complex detector achieved a sensitivity of 99.57% and positive predictivity of 99.59% using 48 recordings from the MIT-BIH arrhythmia database.
Why the study?
Wireless ECG monitoring remains prohibitively expensive, and lowering cost and power consumption requires reducing algorithm complexity for accurate QRS complex detection.
Answers are grounded in this paper and cite their sources.
A novel DWT-based QRS complex detection algorithm implemented on a VLSI chip provides high accuracy with low power consumption, potentially enabling cost-effective wireless ECG monitoring.
This paper presents a very large–scale integration chip for a novel discrete wavelet transform (DWT) based QRS complex detection algorithm. In many aspects of electrocardiogram (ECG) analyses, QRS complex detection is the first step. For example, heart rate variability (HRV) is evaluated based on the RR interval and thus, an accurate QRS detection algorithm would substantially affect the subsequent steps in HRV analysis. On the other hand, since wireless monitoring currently remains prohibitively expensive and lowering the cost and power consumption of wireless monitoring depends upon a reduction in the complexity of the algorithm, in this study we propose a simple, reliable, low power and cost-effective QRS detection method and its VLSI implementation. Here, the task of the QRS complex detection is performed using the quadratic spline wavelet transform based wavelet packet decomposition. After a four-level DWT decomposition, in order to enhance the QRS complex a novel noise level detection is then performed. The proposed noise level detector would select two of the four wavelet coefficients and then calculate their product. As a result, in addition to DWT, the processing stage includes the product of the two selected wavelet coefficients, adaptive thresholding scheme, and the execution of decision rules. Fabricated on an 0.18- <tex-math notation="LaTeX">μ m </tex-math> complementary metal oxide semiconductor, the 1-KHz processor draws only <tex-math notation="LaTeX">4.2~μ W </tex-math> of power, and the chip area is only <tex-math notation="LaTeX">0.83~mm² </tex-math> . Also, the detection accuracy of the proposed method is further verified using 48 recordings from the MIT-BIH arrhythmia database (Se = 99.57%, <tex-math notation="LaTeX">+P\,\,= 99.59% </tex-math> ), suggesting that the proposed QRS detector may achieve a high accuracy and cost-effective QRS complex detection.
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Chen et al. (2022) studied QRS complex detection (n=48). Discrete wavelet transform (DWT) based QRS complex detection algorithm was evaluated on Detection accuracy (Sensitivity and Positive Predictivity) (+P = 99.59%). The proposed discrete wavelet transform-based QRS complex detector achieved a sensitivity of 99.57% and positive predictivity of 99.59% using 48 recordings from the MIT-BIH arrhythmia database.
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