A Low-Complexity ECG Feature Extraction Algorithm for Mobile Healthcare Applications

Mazomenos, E B and Biswas, D and Acharyya, Amit and Chen, T and Maharatna, K and Rosengarten, J and 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). pp. 459-469. ISSN 2168-2194

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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 tradeoff between computational complexity and performance, a key requirement in remote cardiovascular disease monitoring systems.

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