TY - GEN
T1 - Design of a 0.5 v 1.68mW nose-on-a-chip for rapid screen of chronic obstructive pulmonary disease
AU - Chou, Ting I.
AU - Chiu, Shih Wen
AU - Chang, Kwuang Han
AU - Chen, Yi Ju
AU - Tang, Chen Ting
AU - Shih, Chung Hung
AU - Hsieh, Chih Cheng
AU - Chang, Meng Fan
AU - Yang, Chia Hsiang
AU - Chiueh, Herming
AU - Tang, Kea Tiong
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016
Y1 - 2016
N2 - Chronic obstructive pulmonary disease (COPD) still lacks a rapid diagnosis strategy. In this paper, we propose a low-power nose-on-a-chip for rapid COPD screening. This chip is designed for implementation in a personal handheld device that detects patient breath for COPD diagnosis. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a two-channel successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated in 90 nm CMOS and consumes 1.68 mW at 0.5 V. In simulation, the system distinguished between undiseased and diseased patients with 90.82% accuracy for a set of diseases including COPD and asthma and exhibited 92.31% accuracy for identifying patients with COPD or asthma. The system classified severity levels of COPD under four labels (normal, mild, moderate, and severe) with 92.00% accuracy. Accordingly, this work provides a promising solution for the unmet medical need of rapid COPD screening.
AB - Chronic obstructive pulmonary disease (COPD) still lacks a rapid diagnosis strategy. In this paper, we propose a low-power nose-on-a-chip for rapid COPD screening. This chip is designed for implementation in a personal handheld device that detects patient breath for COPD diagnosis. The chip has 36 on-chip sensors, a 36-channel adaptive interface with an integrated programmable amplifier, a four-channel frequency readout interface, one on-chip temperature sensor, a two-channel successive approximation analog-to-digital converter, a scalable learning kernel cluster, and a reduced instruction set computing core with low-voltage static random-access memory. This chip is fabricated in 90 nm CMOS and consumes 1.68 mW at 0.5 V. In simulation, the system distinguished between undiseased and diseased patients with 90.82% accuracy for a set of diseases including COPD and asthma and exhibited 92.31% accuracy for identifying patients with COPD or asthma. The system classified severity levels of COPD under four labels (normal, mild, moderate, and severe) with 92.00% accuracy. Accordingly, this work provides a promising solution for the unmet medical need of rapid COPD screening.
KW - COPD
KW - Nose-on-a-chip system
KW - SoC
UR - http://www.scopus.com/inward/record.url?scp=85014168038&partnerID=8YFLogxK
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U2 - 10.1109/BioCAS.2016.7833864
DO - 10.1109/BioCAS.2016.7833864
M3 - Conference contribution
AN - SCOPUS:85014168038
T3 - Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
SP - 592
EP - 595
BT - Proceedings - 2016 IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 12th IEEE Biomedical Circuits and Systems Conference, BioCAS 2016
Y2 - 17 October 2016 through 19 October 2016
ER -