Continuous Detection of Abnormal Heartbeats from ECG Using Online Outlier Detection

  • Yuhang Lin
  • Byung Suk LeeEmail author
  • Daniel Lustgarten
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 898)


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.


ECG Anomaly detection Outlier detection Data stream 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Yuhang Lin
    • 1
    • 3
  • Byung Suk Lee
    • 1
    Email author
  • Daniel Lustgarten
    • 2
  1. 1.Department of Computer ScienceUniversity of VermontBurlingtonUSA
  2. 2.Department of MedicineUniversity of VermontBurlingtonUSA
  3. 3.Department of Computer ScienceNorth Carolina State UniversityRaleightUSA

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