Abstract
Abnormality detection in ECG time series is very important for cardiologists to detect automatically heart diseases. In this study, we propose a novel algorithm that compare and align efficiently quasi periodic time series. We apply this algorithm to detect exactly in the ECG, where the anomaly is. For this purpose, we use a normal (healthy) ECG segment and we compare it with another ECG segment. Our algorithm is an improvement of the famous dynamic time warping algorithm, called Improved Dynamic Time Warping (I-DTW). Indeed, the alignment of quasi-periodic time series, such as those representing the ECG signal is impossible to achieve with the DTW, especially when the segment of ECGs are of different lengths and composed of different number of periods each. The tests were performed on ECG time series, selected from the public database of the “Massachusetts Institute of Technology - Beth Israel Hospital (MIT-BIH)”. The results show that the proposed method outperforms the famous DTW method in terms of alignment accuracy and that it can be a good method for abnormalities detection in ECGs time series.
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Boulnemour, I., Boucheham, B., Benloucif, S. (2016). Improved Dynamic Time Warping for Abnormality Detection in ECG Time Series. In: Ortuño, F., Rojas, I. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2016. Lecture Notes in Computer Science(), vol 9656. Springer, Cham. https://doi.org/10.1007/978-3-319-31744-1_22
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DOI: https://doi.org/10.1007/978-3-319-31744-1_22
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