Abnormal ECG signal detection based on compressed sampling in Wearable ECG sensor
Abstract
Nowadays to diagnose cardiac arrhythmias Holter device is used to record 1 or 2 ECG leads during 24 or 48h. Power consumption limitations determine that the amount of data needs to be diminished without damaging the quality of information. To get a solution, we introduce a novel method based on Compressed Sensing (CS) technique to the Wearable ECG sensor (WES). The main principle underlying this framework is to sample analog signals at sub-Nyquist rate at the analog-digital converters (ADCs) and to classify directly compressed measurement into normal and abnormal state. Those compressed measurements which imply a risk of cardiac anomaly will be stored in a multimedia flash memory card or be transferred to the terminal of the network for a cardiologist to make an off-line diagnosis of cardiac arrhythmias using the reconstructed signals from the compressed measurements. In this paper we propose a scheme to directly classify compressed ECG samples into normal or abnormal states, thus avoiding reconstruction of the entire signal to perform this task. Our algorithm takes advantage of estimating parameters directly from the compressed measurements; thereby eliminating the reconstruct stage and reducing the computational complexity in WES. Direct cardiac arrhythmia detection based on CS reduces 34% energy consumption and 90% storage in WES for the reconstructed performance of 41dB.
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Several data mining techniques are applied for these tasks: wavelet analysis for artifact reduction and data compression, rule-based methods for data summarizing and transmitting, and Gaussian process for secure authentication.
“In order to conduct these problems, several data mining techniques are applied such as wavelet analysis for artifact reduction [53] and data compression [54], rule-based methods for data summarizing and transmitting [55,56], and Gaussian process for secure authentication [57].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [54]
Usually single sensor data have been used for specific analysis on individual physiological data such as ECG signal analysis or blood glucose monitoring.
“Usually single sensor data have been used for specific analysis on individual physiological data such as ECG signal analysis [32,54,92] or blood glucose monitoring [23,28].”
From Data Mining for Wearable Sensors in Health Monitoring Systems: A Review of Recent Trends and Challenges (2013) · cites this paper as [54]
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Banaee, H., Ahmed, M., & Loutfi, A. (2013) · Sensors · doi:10.3390/s131217472 · Cited by 452 · see citing works
- Authors
- Ding, H., Sun, H., & Hou, K.-M.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- Wuhan University (CN)
- Venue
- 2011 International Conference on Wireless Communications and Signal Processing (WCSP)
- Publisher
- IEEE
- Published
- 2011-11
- Pages
- 1-5
- OpenAlex
- W2066245142
- Cited by
- 16
- References
- 13
- Citations by year
- 2024: 1 · 2022: 1 · 2021: 1 · 2020: 2 · 2018: 3 · 2016: 1 · 2015: 1 · 2014: 4 · 2013: 2
- FWCI
- 6.22
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Wireless Communications and Signal Processing (WCSP) (publishedVersion)
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Computational Mechanics › Sparse and Compressive Sensing Techniques
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- compressed sensing, wearable ECG sensors, arrhythmia detection, sub-Nyquist sampling, energy consumption reduction, storage reduction
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