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A low-complexity ECG feature extraction algorithm for mobile healthcare applications

A low-complexity ECG feature extraction algorithm for mobile healthcare applications
A low-complexity ECG feature extraction algorithm for mobile healthcare applications
This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.
algorithm design and analysis, discrete wavelet transforms, electrocardiography, feature extraction, monitoring, noise, signal processing algorithms, discrete wavelet transform (dwt), electrocardiogram (ecg) feature extraction, low complexity algorithm, mobile healthcare
2168-2194
459-469
Mazomenos, E.B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Biswas, D.
76983b74-d729-4aae-94c3-94d05e9b2ed4
Acharyya, A.
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Chen, T.
62b1db38-757b-4250-8b48-de4e47f09d9e
Maharatna, K.
93bef0a2-e011-4622-8c56-5447da4cd5dd
Rosengarten, J.
86af59ba-a758-4fe0-9292-531a9ec1d39c
Morgan, J.
7891d404-a419-4f8e-94db-09d7925209e5
Curzen, N.
70f3ea49-51b1-418f-8e56-8210aef1abf4
Mazomenos, E.B.
23983827-c7e7-4ee1-bfc8-986aa3594279
Biswas, D.
76983b74-d729-4aae-94c3-94d05e9b2ed4
Acharyya, A.
f7c95a87-04ac-4d13-a74c-0c4d89b1c79c
Chen, T.
62b1db38-757b-4250-8b48-de4e47f09d9e
Maharatna, K.
93bef0a2-e011-4622-8c56-5447da4cd5dd
Rosengarten, J.
86af59ba-a758-4fe0-9292-531a9ec1d39c
Morgan, J.
7891d404-a419-4f8e-94db-09d7925209e5
Curzen, N.
70f3ea49-51b1-418f-8e56-8210aef1abf4

Mazomenos, E.B., Biswas, D., Acharyya, A., Chen, T., Maharatna, K., Rosengarten, J., Morgan, J. and Curzen, N. (2013) A low-complexity ECG feature extraction algorithm for mobile healthcare applications. IEEE Journal of Biomedical and Health Informatics, 17 (2), 459-469. (doi:10.1109/TITB.2012.2231312). (PMID:23362250)

Record type: Article

Abstract

This paper introduces a low-complexity algorithm for the extraction of the fiducial points from the Electrocardiogram (ECG). The application area we consider is that of remote cardiovascular monitoring, where continuous sensing and processing takes place in low-power, computationally constrained devices, thus the power consumption and complexity of the processing algorithms should remain at a minimum level. Under this context, we choose to employ the Discrete Wavelet Transform (DWT) with the Haar function being the mother wavelet, as our principal analysis method. From the modulus-maxima analysis on the DWT coefficients, an approximation of the ECG fiducial points is extracted. These initial findings are complimented with a refinement stage, based on the time-domain morphological properties of the ECG, which alleviates the decreased temporal resolution of the DWT. The resulting algorithm is a hybrid scheme of time and frequency domain signal processing. Feature extraction results from 27 ECG signals from QTDB, were tested against manual annotations and used to compare our approach against the state-of-the art ECG delineators. In addition, 450 signals from the 15-lead PTBDB are used to evaluate the obtained performance against the CSE tolerance limits. Our findings indicate that all but one CSE limits are satisfied. This level of performance combined with a complexity analysis, where the upper bound of the proposed algorithm, in terms of arithmetic operations, is calculated as 2:423N + 214 additions and 1:093N + 12 multiplications for N 861 or 2:553N + 102 additions and 1:093N +10 multiplications for N > 861 (N being the number of input samples), reveals that the proposed method achieves an ideal trade-off between computational complexity and performance, a key requirement in remote CVD monitoring systems.

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More information

e-pub ahead of print date: 7 March 2013
Published date: March 2013
Keywords: algorithm design and analysis, discrete wavelet transforms, electrocardiography, feature extraction, monitoring, noise, signal processing algorithms, discrete wavelet transform (dwt), electrocardiogram (ecg) feature extraction, low complexity algorithm, mobile healthcare
Organisations: Electronic & Software Systems, Human Development & Health

Identifiers

Local EPrints ID: 349831
URI: https://eprints.soton.ac.uk/id/eprint/349831
ISSN: 2168-2194
PURE UUID: 0a07c02f-6105-4271-93d8-6602890b3c67

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Date deposited: 12 Mar 2013 13:07
Last modified: 09 Sep 2019 18:49

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Contributors

Author: E.B. Mazomenos
Author: D. Biswas
Author: A. Acharyya
Author: T. Chen
Author: K. Maharatna
Author: J. Rosengarten
Author: J. Morgan
Author: N. Curzen

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