A 259.6&amp;#x03BC;W nonlinear HRV-EEG chaos processor with body channel communication interface for mental health monitoring

Roh, Taehwan; Hong, Sunjoo; Cho, Hyunwoo; Yoo, Hoi‐Jun

doi:10.1109/isscc.2012.6177020

Cited by 12 publications

(11 citation statements)

References 7 publications

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“…It is known that improper delay degrades the accuracy, and improper embedding dimension degrades both of the accuracy and the computational cost. However, previous works did not compensate for these parameters, but merely fix them because of additional computational cost [2]. In the proposed processor, the ReOpt works to find the optimal delay and the optimal dimension.…”

Section: Accuracy-improving Reoptmentioning

confidence: 99%

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A 95% accurate EEG-connectome Processor for a Mental Health Monitoring System

Kim¹,

Song²,

Roh³

et al. 2016

JSTS:Journal of Semiconductor Technology and Science

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Section: Accuracy-improving Reoptmentioning

confidence: 99%

“…In this context, several EEG processors have been proposed for continuous mental health monitoring systems [2,3]. However, most of them used only power spectral analysis or nonlinear analysis.…”

Section: Introductionmentioning

confidence: 99%

A 95% accurate EEG-connectome Processor for a Mental Health Monitoring System

Kim¹,

Song²,

Roh³

et al. 2016

JSTS:Journal of Semiconductor Technology and Science

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“…1, we present a diagnosis IC which contains 4-channel ExG sensor front-end and an independent component analysis (ICA) processor [12] to measure the various signals even if the EA stimulation is simultaneously applied to the human body. Consequently, during the EA treatment, the practitioner can check the patient's status and adaptively controls the stimulation parameters.…”

Section: ) Various Signals Sensing With Stimulationmentioning

confidence: 99%

A dynamic electrode impedance matched acupuncture-type diagnosis system with concurrent feedback of physiological signals

Song

Hong

Roh

et al. 2012

2012 IEEE Asian Solid State Circuits Conference (A-Sscc)

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A dynamic electrode impedance matched acupuncture-type diagnosis system is proposed for an active electro-acupuncture (EA) treatment with the concurrent feedback of physiological signals. The 4-channel ExG sensor front-end and the independent component analysis (ICA) processor, is used for the acquisition of pure ECG, EEG, and EMG signal when the EA stimulation is simultaneously applied to the human body. The EA stimulator front-end adopts both the large time constant (LTC) S/H current matching technique and the offset current regulation for the accurate charge balancing. As a result, it can achieve less than 10 nA DC offset current. There are 25 concentric circular electrodes in the bottom of the proposed system. The dynamic electrode impedance matching circuit monitors the stimulation voltage swing and optimizes the area of the stimulation electrodes and the sensing electrodes for the high CMRR. The proposed diagnosis IC of 5.0 mm × 5.0 mm is fabricated in 0.13 μm RF CMOS technology, and dissipates only 3.6 mW from 1.2 V.

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“…Hence, several wearable devices have been developed to monitor mental stress continuously, e.g. cardio-respiratory sensor [14], GSR sensor with ZigBee [15], HRV-EEG processor [16] and AutoSense [17]. Some of them use multiple aforementioned sensors to improve stress detection rate.…”

Section: Introductionmentioning

confidence: 99%

Activity awareness can improve continuous stress detection in galvanic skin response

Tang

Yeo

Lau

2014

IEEE SENSORS 2014 Proceedings

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Continuous stress monitoring offers a potential to help understand different mental stress patterns and how clinical intervention could best be applied. One economical way to detect stress is to measure galvanic skin response (GSR) as the electrical conductance of skin varies with physiological arousal. In this work, we studied the effects of different activities (sit, stand and walk) on GSR measurements. We implemented a GSR sensor system and an activity recognition system. We showed that using two accelerometers (at thigh and ankle each) achieved an overall accuracy of 94.7% in activity recognition, an improvement of +27.3% from using single sensor node system. We further demonstrated that the activity information could help improve the sensitivity in stress detection at sitting and standing positions.

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A 259.6μW nonlinear HRV-EEG chaos processor with body channel communication interface for mental health monitoring

Cited by 12 publications

References 7 publications

A 95% accurate EEG-connectome Processor for a Mental Health Monitoring System

A 95% accurate EEG-connectome Processor for a Mental Health Monitoring System

A dynamic electrode impedance matched acupuncture-type diagnosis system with concurrent feedback of physiological signals

Activity awareness can improve continuous stress detection in galvanic skin response

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