A sub-100&amp;#x00B5;W multi-functional cardiac signal processor for mobile healthcare applications

Hsu, S. Y.; Ho, Yen Teng; Tseng, Yuhwai; Lin, Tingyou; Chang, Po-Yao; Lee, Jen-Wei; Hsiao, Ju-Hung; Chuang, Siou-Ming; Yang, Tze-Zheng; Liu, Po-Chun; Yang, T. J.; Chen, Ray‐Jade; Su, Chauchin; Lee, Chen‐Yi

doi:10.1109/vlsic.2012.6243837

Cited by 15 publications

(8 citation statements)

References 7 publications

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“…The threshold is calculated using the root mean square value of the wavelet transform. This algorithm has been used in robust ECG monitoring LSIs [81], [86], [87]. The QSW requires few calculations and low hardware costs because it can be implemented merely by using adders and shift operators.…”

Section: Noise Reduction and Noise Tolerancementioning

confidence: 99%

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

Yoshimoto

Izumi

2019

IEICE Trans. Electron.

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This paper surveys advances in biomedical processor SoC technology for healthcare application and reviews state-of-the-art architecture and circuits used in SoC integration. Particularly, this paper categorizes and describes techniques for improving power efficiency in communication, computation, and sensing. Additionally, it surveys accuracy enhancement techniques for bio-signal measurement and recognition. Finally, we have discussed the potential new directions for development as well as research.

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Section: Noise Reduction and Noise Tolerancementioning

confidence: 99%

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

Yoshimoto

Izumi

2019

IEICE Trans. Electron.

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“…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%

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

Izumi

Yamashita

Nakai

et al. 2015

IEICE Trans. Inf. & Syst.

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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.

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“…The AFE achieves an inputreferred noise of <10 µVrms with a <100 nW power consumption, which demonstrates a very good balance for the noise-power trade-off, compared to state-of-the-art amplifiers. Figure 1 presents a comparison of power consumption versus root mean square (RMS) input-referred noise per square root of bandwidth for the recent state-of-the-art signal acquisition systems [6][7][8][9][10][11][12][13][14][15][16][17][18]. The proposed AFE has the lowest input-referred noise per square root of bandwidth compared to the state-of-the-art AFEs with power consumptions below 100 nW.…”

Section: Introductionmentioning

confidence: 99%

A 0.5 V 68 nW ECG Monitoring Analog Front-End for Arrhythmia Diagnosis

Kosari

Breiholz

Liu

et al. 2018

JLPEA

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This paper presents a power efficient analog front-end (AFE) for electrocardiogram (ECG) signal monitoring and arrhythmia diagnosis. The AFE uses low-noise and low-power circuit design methodologies and aggressive voltage scaling to satisfy both the low power consumption and low input-referred noise requirements of ECG signal acquisition systems. The AFE was realized with a three-stage fully differential AC-coupled amplifier, and it provides bio-signal acquisition with programmable gain and bandwidth. The AFE was implemented in a 130 nm CMOS process, and it has a measured tunable mid-band gain from 31 to 52 dB with tunable low-pass and high-pass corner frequencies. Under only 0.5 V supply voltage, it consumes 68 nW of power with an input-referred noise of 2.8 µVrms and a power efficiency factor (PEF) of 3.9, which makes it very suitable for energy-harvesting applications. The low-noise 68nW AFE was also integrated on a self-powered physiological monitoring System on Chip (SoC) that is used to capture ECG bio-signals. Heart rate extraction (R-R) detection algorithms were implemented and utilized to analyze the ECG data received by the AFE, showing the feasibility of <100 nW AFE for continuous ECG monitoring applications.

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A sub-100µW multi-functional cardiac signal processor for mobile healthcare applications

Cited by 15 publications

References 7 publications

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

Recent Progress of Biomedical Processor SoC for Wearable Healthcare Application: A Review

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

A 0.5 V 68 nW ECG Monitoring Analog Front-End for Arrhythmia Diagnosis

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