An Energy-Efficient Design for ECG Recording and R-Peak Detection Based on Wavelet Transform

A dual-mode ECG processor with difference-insensitive QRS detection and lossless compression

“…Evaluated by MITDB, the detection performance (represented in Se and Pr) of this work is comparable to that of [3,8,9], and higher than [2]. And the time accuracy of AE2:96 ms is better than both [2] and [8]. In data compression mode, the proposed method achieves an average CR of 2.42, which is higher than that of [9].…”

“…In data compression mode, the proposed method achieves an average CR of 2.42, which is higher than that of [9]. Though [8] shows much higher CR, the compression is lossy with unguaranteed signal quality and it requires repeated memory access and calculation, which requires high processing frequency. As for power efficiency, with low complexity design and near-threshold voltage supply in advanced technology, the proposed processor has an ultra-low power consumption of 36 nW in detection mode and 7.3 nW in compression mode, which is much lower than the others.…”

“…On the other hand, some abnormal beats might not be recognized from QRS alone, and the raw signal should be transmitted for further analysis. [8] proposed a design with QRS detection and ECG lossy compression for this purpose. Yet the compression requires large memory and high processing frequency for repeated write/read.…”

A novel electrode‐area‐weighted method of implementing wavelet transform processor with surface acoustic wave device

“…It is now widely used in many fields [1][2][3][4][5][6][7][8][9], for example, in signal analysis, image processing, quantum mechanics, theoretical physics, computer classification and identification, music and language synthetic, medical imaging and diagnosis, seismic data processing, and fault diagnosis. It is now widely used in many fields [1][2][3][4][5][6][7][8][9], for example, in signal analysis, image processing, quantum mechanics, theoretical physics, computer classification and identification, music and language synthetic, medical imaging and diagnosis, seismic data processing, and fault diagnosis.…”

“…Wavelet transform is one of the most brilliant scientific achievements of the 20th century. It is now widely used in many fields [1][2][3][4][5][6][7][8][9], for example, in signal analysis, image processing, quantum mechanics, theoretical physics, computer classification and identification, music and language synthetic, medical imaging and diagnosis, seismic data processing, and fault diagnosis. However, wavelet transform algorithm needs a larger number of mathematical operations and complicated program, so it is very complicated and difficult to implement in engineering fields.…”