Loading...

ECG beat classification based on a cross-distance analysis

Shahram, M ; Sharif University of Technology | 2001

55 Viewed
  1. Type of Document: Article
  2. DOI: 10.1109/ISSPA.2001.949820
  3. Publisher: IEEE Computer Society , 2001
  4. Abstract:
  5. This paper presents a multi-stage algorithm for QRS complex classification into normal and abnormal categories using an unsupervised sequential beat clustering and a cross-distance analysis algorithm. After the sequential beat clustering, a search algorithm based on relative similarity of created classes is used to detect the main normal class. Then other classes are labeled based on a distance measurement from the main normal class. Evaluated results on the MIT-BIH ECG database exhibits an error rate less than 1% for normal and abnormal discrimination and 0.2% for clustering of 15 types of arrhythmia existing on the MIT-BIH database. © 2001 IEEE
  6. Keywords:
  7. Electrocardiography ; Filtering ; Sampling methods ; Labeling ; Hidden Markov models ; Spatial databases ; Filters ; Patient monitoring ; Algorithm design and analysis ; Clustering algorithms
  8. Source: 6th International Symposium on Signal Processing and Its Applications, ISSPA 2001, Kuala Lumpur, 13 August 2001 through 16 August 2001 ; Volume 1 , 2001 , Pages 234-237 ; 0780367030 (ISBN); 9780780367036 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/949820