A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring

Shihui Yin; Minkyu Kim; Deepak Kadetotad; Yang Liu; Chisung Bae; Sang Joon Kim; Yu Cao; Jae Sun Seo

doi:10.23919/VLSIC.2017.8008563

A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring

Shihui Yin, Minkyu Kim, Deepak Kadetotad, Yang Liu, Chisung Bae, Sang Joon Kim, Yu Cao, Jae Sun Seo

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

15 Scopus citations

Abstract

A smart wearable electrocardiographic (ECG) processor is presented for secure ECG-based biometric authentication and cardiac monitoring, including arrhythmia and anomaly detection. Data-driven Lasso regression and low-precision techniques are developed to compress the neural networks by 24.4X. The prototype chip fabricated in 65 nm LP CMOS consumes 1.06 μW at 0.55 V for real-time ECG authentication. Equal error rates of 0.74% and 1.7% are achieved on ECG-ID database and in-house 645-subject database, respectively.

Original language	English (US)
Title of host publication	2017 Symposium on VLSI Circuits, VLSI Circuits 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	C102-C103
ISBN (Electronic)	9784863486065
DOIs	https://doi.org/10.23919/VLSIC.2017.8008563
State	Published - Aug 10 2017
Externally published	Yes
Event	31st Symposium on VLSI Circuits, VLSI Circuits 2017 - Kyoto, Japan Duration: Jun 5 2017 → Jun 8 2017

Publication series

Name	IEEE Symposium on VLSI Circuits, Digest of Technical Papers

Other

Other	31st Symposium on VLSI Circuits, VLSI Circuits 2017
Country/Territory	Japan
City	Kyoto
Period	6/5/17 → 6/8/17

Bibliographical note

Publisher Copyright:
© 2017 JSAP.

Publisher link

10.23919/VLSIC.2017.8008563

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Yin, S., Kim, M., Kadetotad, D., Liu, Y., Bae, C., Kim, S. J., Cao, Y., & Seo, J. S. (2017). A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring. In 2017 Symposium on VLSI Circuits, VLSI Circuits 2017 (pp. C102-C103). Article 8008563 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.23919/VLSIC.2017.8008563

A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring. / Yin, Shihui; Kim, Minkyu; Kadetotad, Deepak et al.
2017 Symposium on VLSI Circuits, VLSI Circuits 2017. Institute of Electrical and Electronics Engineers Inc., 2017. p. C102-C103 8008563 (IEEE Symposium on VLSI Circuits, Digest of Technical Papers).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Yin, S, Kim, M, Kadetotad, D, Liu, Y, Bae, C, Kim, SJ, Cao, Y & Seo, JS 2017, A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring. in 2017 Symposium on VLSI Circuits, VLSI Circuits 2017., 8008563, IEEE Symposium on VLSI Circuits, Digest of Technical Papers, Institute of Electrical and Electronics Engineers Inc., pp. C102-C103, 31st Symposium on VLSI Circuits, VLSI Circuits 2017, Kyoto, Japan, 6/5/17. https://doi.org/10.23919/VLSIC.2017.8008563

Yin S, Kim M, Kadetotad D, Liu Y, Bae C, Kim SJ et al. A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring. In 2017 Symposium on VLSI Circuits, VLSI Circuits 2017. Institute of Electrical and Electronics Engineers Inc. 2017. p. C102-C103. 8008563. (IEEE Symposium on VLSI Circuits, Digest of Technical Papers). doi: 10.23919/VLSIC.2017.8008563

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abstract = "A smart wearable electrocardiographic (ECG) processor is presented for secure ECG-based biometric authentication and cardiac monitoring, including arrhythmia and anomaly detection. Data-driven Lasso regression and low-precision techniques are developed to compress the neural networks by 24.4X. The prototype chip fabricated in 65 nm LP CMOS consumes 1.06 μW at 0.55 V for real-time ECG authentication. Equal error rates of 0.74% and 1.7% are achieved on ECG-ID database and in-house 645-subject database, respectively.",

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A 1.06 μw smart ECG processor in 65 nm CMOS for real-time biometrie authentication and personal cardiac monitoring

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