Assessment and comparison of time realignment methods for supervised heart beat classification
(2009) Second International Conference on Bio-inspired Systems and Signal Processing (BIOSIGNALS 2009) — Location: Porto (Portugal) (14.January.2009)
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- Abstract
- A reliable diagnosis of cardiac diseases can sometimes only be obtained by observing the heart of a patient for a long time period where every single heart beat is of importance. Computer-aided classification of heart beats is therefore of great help. The classification of the complete heart beat has many advantages compared to a classification of the QRS complex only or feature extraction methods. Nevertheless, the task is challenging because of the time-varying property of the heart beats. In this work, four time-alignment methods are evaluated and compared in the context of supervised heart beat classification. Among the four methods are three time series resampling methods by linear interpolation, cubic splines interpolation and trace segmentation. The fourth method is a realignment algorithm by dynamic time warping. The multiple sources of artifacts are filtered by discrete wavelet transform. As it only relies on a dissimilarity measure, the k-nearest neighbor classifier is a suitable choice for supervised classification of time series like ECG signals in multiple classes. Two different experiments corresponding to inter-patient and intra-patient classification are conducted on representative dataset built from the standard public MIT-BIH arrhythmia database.
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Citations
de Lannoy, G., Verleysen, M., & Delbeke, J. (2009). Assessment and comparison of time realignment methods for supervised heart beat classification. BIOSIGNALS 2009. Second International Conference on Bio-inspired Systems and Signal Processing, p. 239-244. https://hdl.handle.net/2078.5/253830