The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.
A 48.6-to-105.2 µW Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications
Abstract
A machine-learning (ML) assisted cardiac sensor SoC (CS-SoC) is designed for mobile healthcare applications. The heterogeneous architecture realizes the cardiac signal acquisition, filtering with versatile feature extractions and classifications, and enables the higher order analysis over traditional DSPs. Besides, the asynchronous architecture with dynamic standby controller further suppresses the system active duty and the leakage power dissipation. The proposed chip is fabricated in a 90-nm standard CMOS technology and operates at 0.5 V-1.0 V (0.7 V-1.0 V for SRAM and I/O interface). Examined with healthcare monitoring applications, the CS-SoC dissipates 48.6/105.2 μW for real-time syndrome detections of ECG-based arrhythmia/VCG-based myocardial infarction with 95.8/99% detection accuracy, respectively.
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“The analysis of the body movements may allow diagnosing Parkinson's disease at an early stage.227”
From Wearables in Medicine (2018) · cites this paper as [227]
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Cites
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Cited by
Yetisen, A. K., Martinez‐Hurtado, J. L., Ünal, B., Khademhosseini, A., & Butt, H. (2018) · Advanced Materials · doi:10.1002/adma.201706910 · Cited by 569
Metadata
- Authors
- Hsu, S.-Y., Ho, Y., Chang, P.-Y., Su, C., & Lee, C.-Y.
- Affiliations
- National Yang Ming Chiao Tung University (TW)
- National Dong Hwa University (TW)
- ORCID
- Lee, Chen-Yi
- Venue
- IEEE Journal of Solid-State Circuits
- Published
- 2014-04
- Volume
- 49
- Issue
- 4
- Pages
- 801-811
- ISSN
- 0018-9200 (ISSN-L)
- 1558-173X
- OpenAlex
- W2080722785
- Cited by
- 82
- References
- 23
- Citations by year
- 2026: 4 · 2025: 6 · 2024: 6 · 2023: 9 · 2022: 10 · 2021: 6 · 2020: 11 · 2019: 6 · 2018: 6 · 2017: 10 · 2016: 3 · 2015: 5
- FWCI
- 4.14
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Journal of Solid-State Circuits (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Analog and Mixed-Signal Circuit Design
- Physical Sciences › Engineering › Biomedical Engineering › Wireless Body Area Networks
- Keywords
- machine learning hardware, low-power SoC, mobile health, ECG arrhythmia detection, feature extraction, asynchronous architecture, leakage power reduction, heterogeneous architectures, 90 nm CMOS
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- CoChat. (2026). Citation check: A 48.6-to-105.2 µW Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications. https://app.cochat.ai/papers/10.1109/jssc.2013.2297406
- The paper
- Hsu, S.-Y., Ho, Y., Chang, P.-Y., Su, C., & Lee, C.-Y. (2014). A 48.6-to-105.2 µW Machine Learning Assisted Cardiac Sensor SoC for Mobile Healthcare Applications. IEEE Journal of Solid-State Circuits. https://doi.org/10.1109/jssc.2013.2297406
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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