A micropower biomedical signal processor for mobile healthcare applications

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Kawaguchi

et al. 2012

This report describes a robust method of Instantaneous Heart Rate (IHR) detection from noisy electrocardiogram (ECG) signals. Generally, the IHR is calculated from the interval of R-waves. Then, the R-waves are extracted from the ECG using a threshold. However, in wearable biosignal monitoring systems, various noises (e.g. muscle artifacts from myoelectric signals, electrode motion artifacts) increase incidences of misdetection and false detection because the power consumption and electrode distance of the wearable sensor are limited to reduce its size and weight. To prevent incorrect detection, we use a short-time autocorrelation technique. The proposed method uses similarity of the waveform of the QRS complex. Therefore, it has no threshold calculation Process and it is robust for noisy environment. Simulation results show that the proposed method improves the success rate of IHR detection by up to 37%.

Section: Conventional Methodsmentioning

confidence: 99%

“…A wearable and wireless ECG telemetry system [4,5] and single-chip ECG monitoring system LSIs [6,7,8] have been developed. However, the wearable ECG monitor is sensitive to noise because its electrodes are close together.…”

Section: Introductionmentioning

confidence: 99%

Instantaneous Heart Rate detection using short-time autocorrelation for wearable healthcare systems

Konishi

2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

Kawaguchi

et al. 2012

“…We implemented a quadratic spline wavelet (QSW) filter and a two-stage IHR extractor. The QSW is commonly used as the low-power noise reduction method for ECG [3]. The hum-noise and baseline wander are well suppressed using QSW.…”

Section: Noise Tolerant Heart Rate Extractormentioning

confidence: 99%

A 6.14µA normally-off ECG-SoC with noise tolerant heart rate extractor for wearable healthcare systems

2014 IEEE Biomedical Circuits and Systems Conference (BioCAS) Proceedings

Yamashita

Nakagawa

et al. 2014

This paper describes an electrocardiograph (ECG) monitoring SoC using a non-volatile MCU (NVMCU) and a noise tolerant instantaneous heart rate (IHR) monitor. The novelty of this work is the combination of the non-volatile MCU for normally-off computing and a noise-tolerant-QRS (heart beat) detection algorithm to achieve both low-power and noise tolerance. To minimize the stand-by current of MCU, a nonvolatile flip-flop and a 6T-4C NVRAM are employed. Proposed plate-line charge-share and bit-line non-precharge techniques also contribute to mitigate the active power overhead of 6T-4C NVRAM. The proposed accurate heart beat detector employs a coarse-fine autocorrelation and a template matching technique. Accurate heart beat detection also contributes system level power reduction because the active ratio of ADC and digital block can be reduced using a heart beat prediction. Then, at least 25% active time can be reduced. Measurement results show the fully integrated ECG-SoC consumes 6.14A including 1.28-A nonvolatile MCU and 0.7-A heart rate extractor. 

“…The threshold is calculated using the root mean square value of the wavelet transform. This algorithm has been used in robust ECG monitoring LSIs [7], [17], [18]. The QSW requires few calculations and low hardware costs because it can be implemented using only adders and shift operators.…”

Section: Heart Rate Extraction Techniquesmentioning

confidence: 99%

“…The Discrete Wavelet Transform (DWT) [7], [15], [16] uses a wavelet transform with quadratic spline wavelet (QSW). The threshold is calculated using the root mean square value of the wavelet transform.…”

Section: Heart Rate Extraction Techniquesmentioning

confidence: 99%

Noise Tolerant Heart Rate Extraction Algorithm Using Short-Term Autocorrelation for Wearable Healthcare Systems

IEICE Trans. Inf. & Syst.

Yamashita

Nakai

et al. 2015

SUMMARYThis report describes a robust method of instantaneous heart rate (IHR) extraction from noisy electrocardiogram (ECG) signals. Generally, R-waves are extracted from ECG using a threshold to calculate the IHR from the interval of R-waves. However, noise increases the incidence of misdetection and false detection in wearable healthcare systems because the power consumption and electrode distance are limited to reduce the size and weight. To prevent incorrect detection, we propose a short-time autocorrelation (STAC) technique. The proposed method extracts the IHR by determining the search window shift length which maximizes the correlation coefficient between the template window and the search window. It uses the similarity of the QRS complex waveform beat-by-beat. Therefore, it has no threshold calculation process. Furthermore, it is robust against noisy environments. The proposed method was evaluated using MIT-BIH arrhythmia and noise stress test databases. Simulation results show that the proposed method achieves a state-of-the-art success rate of IHR extraction in a noise stress test using a muscle artifact and a motion artifact.