Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring

Sumukh Prashant Bhanushali; Sudarsan Sadasivuni; Jose Sanchez; Imon Banerjee; Arindam Sanyal

doi:10.1109/BioCAS58349.2023.10388918

Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring

Sumukh Prashant Bhanushali, Sudarsan Sadasivuni, Jose Sanchez, Imon Banerjee, Arindam Sanyal

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

Abstract

This work presents a fully integrated on-chip classifier for on-device detection of atrial fibrillation (AFib) from electrocardiogram (ECG) signal. The ECG signals are digitized using 14-bit analog-to-digital converter followed by time-domain feature extraction. The features are provided as inputs to an in-memory computing analog, 3-layer artificial neural network (ANN) for classification into normal sinus rhythm, AFib and noisy data. On-device AI classification reduces radio-frequency (RF) transmission and extends battery life of the sensing device by performing all the analysis locally and only transmitting in case of AFib detection. Prototype test-chip is fabricated in 65nm and achieves 99.6% accuracy in classification of AFib while consuming 58.3μJ/classification.

Original language	English (US)
Title of host publication	BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9798350300260
DOIs	https://doi.org/10.1109/BioCAS58349.2023.10388918
State	Published - 2023
Externally published	Yes
Event	2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023 - Toronto, Canada Duration: Oct 19 2023 → Oct 21 2023

Publication series

Name	BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings

Conference

Conference	2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023
Country/Territory	Canada
City	Toronto
Period	10/19/23 → 10/21/23

Keywords

atrial fibrillation
electrocardiogram
Machine learning
mixed-signal classifier and in-memory computing

ASJC Scopus subject areas

Signal Processing
Biomedical Engineering
Electrical and Electronic Engineering
Clinical Neurology
Neurology

Access to Document

10.1109/BioCAS58349.2023.10388918

Cite this

Bhanushali, S. P., Sadasivuni, S., Sanchez, J., Banerjee, I., & Sanyal, A. (2023). Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring. In BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings (BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BioCAS58349.2023.10388918

Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring. / Bhanushali, Sumukh Prashant; Sadasivuni, Sudarsan; Sanchez, Jose et al.
BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings).

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

Bhanushali, SP, Sadasivuni, S, Sanchez, J, Banerjee, I & Sanyal, A 2023, Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring. in BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings. BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings, Institute of Electrical and Electronics Engineers Inc., 2023 IEEE Biomedical Circuits and Systems Conference, BioCAS 2023, Toronto, Canada, 10/19/23. https://doi.org/10.1109/BioCAS58349.2023.10388918

Bhanushali SP, Sadasivuni S, Sanchez J, Banerjee I, Sanyal A. Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring. In BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings. Institute of Electrical and Electronics Engineers Inc. 2023. (BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings). doi: 10.1109/BioCAS58349.2023.10388918

Bhanushali, Sumukh Prashant ; Sadasivuni, Sudarsan ; Sanchez, Jose et al. / Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring. BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings. Institute of Electrical and Electronics Engineers Inc., 2023. (BioCAS 2023 - 2023 IEEE Biomedical Circuits and Systems Conference, Conference Proceedings).

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abstract = "This work presents a fully integrated on-chip classifier for on-device detection of atrial fibrillation (AFib) from electrocardiogram (ECG) signal. The ECG signals are digitized using 14-bit analog-to-digital converter followed by time-domain feature extraction. The features are provided as inputs to an in-memory computing analog, 3-layer artificial neural network (ANN) for classification into normal sinus rhythm, AFib and noisy data. On-device AI classification reduces radio-frequency (RF) transmission and extends battery life of the sensing device by performing all the analysis locally and only transmitting in case of AFib detection. Prototype test-chip is fabricated in 65nm and achieves 99.6% accuracy in classification of AFib while consuming 58.3μJ/classification.",

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AU - Sanyal, Arindam

PY - 2023

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N2 - This work presents a fully integrated on-chip classifier for on-device detection of atrial fibrillation (AFib) from electrocardiogram (ECG) signal. The ECG signals are digitized using 14-bit analog-to-digital converter followed by time-domain feature extraction. The features are provided as inputs to an in-memory computing analog, 3-layer artificial neural network (ANN) for classification into normal sinus rhythm, AFib and noisy data. On-device AI classification reduces radio-frequency (RF) transmission and extends battery life of the sensing device by performing all the analysis locally and only transmitting in case of AFib detection. Prototype test-chip is fabricated in 65nm and achieves 99.6% accuracy in classification of AFib while consuming 58.3μJ/classification.

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Fully Integrated Mixed-Signal Classifier for Cardiovascular Health Monitoring

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Keywords

ASJC Scopus subject areas

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