Abstract
Detecting abnormal heartbeats from an electrocardiogram (ECG) signal is an important problem studied extensively and yet is a difficult problem that defies a viable working solution, especially on a mobile platform which requires computationally efficient and yet accurate detection mechanism. In this project, a prototype system has been built to test the feasibility and efficacy of detecting abnormal ECG segments from an ECG data stream targeting a mobile device, where data are arriving continuously and indefinitely and are processed online incrementally and efficiently without being stored in memory. The processing comprises three steps: (i) segmentation using R peak detection, (ii) feature extraction using discrete wavelet transform, and (iii) outlier detection using incremental online microclustering. Experiments conducted using real ambulatory ECG datasets showed satisfactory accuracy. In addition, comparing personalized detection (tuned separately for each patient’s ECG datasets) and non-personalized detection (tuned aggregated over all patients’ datasets) confirms a definite advantage of personalized detection for ECG.
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Lin, Y., Lee, B.S., Lustgarten, D. (2019). Continuous Detection of Abnormal Heartbeats from ECG Using Online Outlier Detection. In: Lossio-Ventura, J., Muñante, D., Alatrista-Salas, H. (eds) Information Management and Big Data. SIMBig 2018. Communications in Computer and Information Science, vol 898. Springer, Cham. https://doi.org/10.1007/978-3-030-11680-4_33
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