A 0.83-&lt;formula formulatype="inline"&gt;&lt;tex Notation="TeX"&gt;$\mu {\rm W}$&lt;/tex&gt;&lt;/formula&gt; QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35-&lt;formula formulatype="inline"&gt; &lt;tex Notation="TeX"&gt;$\mu{\rm m}$&lt;/tex&gt;&lt;/formula&gt; CMOS

Ieong, Chio-In; Lam, Chi-Pang; Dong, Cheng; Vai, Mang-I; Mak, Peng Un; Pun, Sio Hang; Wan, Feng; Martins, Rui P.

doi:10.1109/tbcas.2012.2188798

Cited by 116 publications

(15 citation statements)

References 21 publications

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“…53 Filter Banks99.5999.56Hamilton and Tompkins 54 BPF/Search-back99.6999.77Zhang and Lian 55 Multiscale Morphology99.8199.80Ieong et al . 56 Quadratic Spline wavelet99.3199.70Nallathambi and Principe 7 Pulse Train99.5899.55Martnez et al . 57 Wavelet Delineation99.6699.56Method IAdaptive Predictor99.6499.81Method IICompressive Sampling Matching PursuitN/RN/RMethod IIIDecimating By A Factor B / K 99.7899.92

SE stands for sensitivity, while +P stands for positive predictivity.

…”

Section: Resultsmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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Current medical screening and diagnostic procedures have shifted toward recording longer electrocardiogram (ECG) signals, which have traditionally been processed on personal computers (PCs) with high-speed multi-core processors and efficient memory processing. Battery-driven devices are now more commonly used for the same purpose and thus exploring highly efficient, low-power alternatives for local ECG signal collection and processing is essential for efficient and convenient clinical use. Several ECG compression methods have been reported in the current literature with limited discussion on the performance of the compressed and the reconstructed ECG signals in terms of the QRS complex detection accuracy. This paper proposes and evaluates different compression methods based not only on the compression ratio (CR) and percentage root-mean-square difference (PRD), but also based on the accuracy of QRS detection. In this paper, we have developed a lossy method (Methods III) and compared them to the most current lossless and lossy ECG compression methods (Method I and Method II, respectively). The proposed lossy compression method (Method III) achieves CR of 4.5×, PRD of 0.53, as well as an overall sensitivity of 99.78% and positive predictivity of 99.92% are achieved (when coupled with an existing QRS detection algorithm) on the MIT-BIH Arrhythmia database and an overall sensitivity of 99.90% and positive predictivity of 99.84% on the QT database.

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SE stands for sensitivity, while +P stands for positive predictivity.

…”

Section: Resultsmentioning

confidence: 99%

Efficient ECG Compression and QRS Detection for E-Health Applications

Elgendi

Mohamed

Ward

2017

Sci Rep

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“…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].…”

Section: Experiments Resultsmentioning

confidence: 72%

“…The scale-3 coefficients of QSWT [3] is selected instead of the four scales to further lower the hardware cost. Based on this method, we propose a more subtle threshold adjusting mechanism to improve the detector's noise-tolerance.…”

Section: Noise-tolerant Qrs Detectormentioning

confidence: 99%

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

Luo

Chen

Xiang

et al. 2017

IEICE Electron. Express

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This work presents a low power dual-mode electrocardiogram (ECG) processor with QRS detection and ECG signal lossless compression. An adaptive difference-insensitive filter is proposed to eliminate redundant information in signal for QRS detection, which helps minimize calculation power. It is also adopted as a noise estimator and used to improve the noise tolerance of the detector. Furthermore, in compression mode, linear predictor with a novel adaptive length encoder is applied to the ECG data lossless compression, achieving a compression ratio (CR) of 2.42 with 1.06 K gate count. Implemented in 40 nm CMOS technology, the processor has a total core area of 6806 µm 2 , with 36 nW power consumption in detection mode and 7.3 nW in compression mode under a supply voltage of 0.5 V.

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“…First, most ADCs that are designed for heart-rate monitoring use faster sampling rates than the absolute minimum from this example. For example, [27][28][29][30] use sampling rates from 300 Hz to 600 kHz, and using these larger sampling rates would significantly increase the average power consumption in an equivalent ADC. Second, the purpose of many heart-monitoring systems is to determine the features of the QRS complex-while our asynchronous ADC has minimal power savings strictly at the conversion stage for this application, our asynchronous ADC is performing many of the operations that would be reserved for a subsequent digital processing system when using a traditional ADC, which would also have a substantial power consumption.…”

Section: Electrocardiogrammentioning

confidence: 99%

Reconfigurable Analog Preprocessing for Efficient Asynchronous Analog-to-Digital Conversion

Kelly

DiLello

Graham

2019

JLPEA

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Wearable medical devices, wireless sensor networks, and other energy-constrained sensing devices are often concerned with finding specific data within more-complex signals while maintaining low power consumption. Traditional analog-to-digital converters (ADCs) can capture the sensor information at a high resolution to enable a subsequent digital system to process for the desired data. However, traditional ADCs can be inefficient for applications that only require specific points of data. This work offers an alternative path to lower the energy expenditure in the quantization stage-asynchronous content-dependent sampling. This asynchronous sampling scheme is achieved by pairing a flexible analog front-end with an asynchronous successive-approximation ADC and a time-to-digital converter. The versatility and reprogrammability of this system allows a multitude of event-driven, asynchronous, or even purely data-driven quantization methods to be implemented for a variety of different applications. The system, fabricated in standard 0.5 µm and 0.35 µm processes, is demonstrated along with example applications with voice, EMG, and ECG signals.

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A 0.83-<formula formulatype="inline"><tex Notation="TeX">$\mu {\rm W}$</tex></formula> QRS Detection Processor Using Quadratic Spline Wavelet Transform for Wireless ECG Acquisition in 0.35-<formula formulatype="inline"> <tex Notation="TeX">$\mu{\rm m}$</tex></formula> CMOS

Cited by 116 publications

References 21 publications

Efficient ECG Compression and QRS Detection for E-Health Applications

Efficient ECG Compression and QRS Detection for E-Health Applications

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

Reconfigurable Analog Preprocessing for Efficient Asynchronous Analog-to-Digital Conversion

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