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Hyperdimensional computing (HD) is an emerging brain-inspired paradigm used for machine learning classification tasks. It manipulates ultra-long vectors-hypervectors- using simple operations, which allows for fast learning, energy efficiency, noise tolerance, and a highly parallel distributed framework. HD computing has shown a significant promise in the area of biological signal classification. This paper addresses group-specific premature ventricular contraction (PVC) beat detection with HD computing using the data from the MIT-BIH arrhythmia database. Temporal, heart rate variability (HRV), and spectral features are extracted, and minimal redundancy maximum relevance (mRMR) is used to rank and select features for classification. Three encoding approaches are explored for mapping the features into the HD space. The HD computing classifiers can achieve a PVC beat detection accuracy of 97.7 % accuracy, compared to 99.4% achieved by more computationally complex methods such as convolutional neural networks (CNNs).
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Enhancing Beat-to-Beat Analysis of Heart Signals With Respiration Harmonics Reduction Through Demodulation and Template Matching
Heart rate variability (HRV) analysis using Doppler radar (DR) is a promising method for noninvasive health and stress assessment. However, the respiration signal harmonic content typically limits HRV parameter estimation accuracy. While several harmonic reduction techniques have been used to improve the average HR estimation accuracy, achieving high beat-to-beat interval (BBI) accuracy is still challenging. This article demonstrates that arctangent demodulation (AD) with wavelet-based signal processing enhanced with template matching is effective to estimate HRV parameters with accuracy on the order of 10 ms with 2.4 GHz DR. Moreover, it was theoretically and experimentally demonstrated that the cases where AD provides limited improvement due to phase delay between thorax and abdomen motion are easily identifiable, and can be alternatively processed using a single-channel data.
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- PAR ID:
- 10547788
- Publisher / Repository:
- IEEE
- Date Published:
- Journal Name:
- IEEE Transactions on Microwave Theory and Techniques
- Volume:
- 72
- Issue:
- 1
- ISSN:
- 0018-9480
- Page Range / eLocation ID:
- 750 to 758
- Subject(s) / Keyword(s):
- Doppler radar heart rate monitoring heart rate variability (HRV) analysis multiresolution analysis noncontact monitoring respiration harmonics template matching filtering
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1109/TMTT.2023.3324444
