Improved abnormality detection from raw ECG signals using feature enhancement

Diptangshu Pandit, Li Zhang, Nauman Aslam, Changyu Liu, Samiran Chattopadhyay

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    4 Citations (Scopus)

    Abstract

    This research presents an abnormal beat detection scheme from lead II Electrocardiogram (ECG) signals along with some improvements on feature extraction. A set of 16 features representing positions, durations, amplitudes and shapes of P, Q, R, S and T waves is proposed in this work for heart beat classification. These features carry important medical information for normal and abnormal beat detection. Diverse classifiers are employed for abnormality detection, including K-Nearest Neighbor, Decision Tree, Artificial Neural Network, Naive Bayesian Classifier, Random Forest, and Support Vector Machine along with some ensemble classifiers such as AdaBoostM1 and Bagging. We have evaluated the proposed system on raw one lead signals extracted from MIT-BIH Arrhythmia, QT and European ST-T databases in the Physionet databank. The experiments using this new set of 16 features achieve better performance for the three test databases than our previous system using a subset of these features.

    Original languageEnglish
    Title of host publication2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016
    EditorsJiayi Du, Chubo Liu, Kenli Li, Lipo Wang, Zhao Tong, Maozhen Li, Ning Xiong
    PublisherInstitute of Electrical and Electronics Engineers Inc.
    Pages1402-1406
    Number of pages5
    ISBN (Electronic)9781509040933
    DOIs
    Publication statusPublished - 24 Oct 2016
    Event12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery - Changsha, China
    Duration: 13 Aug 201615 Aug 2016

    Conference

    Conference12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery
    Abbreviated titleICNC-FSKD 2016
    CountryChina
    CityChangsha
    Period13/08/1615/08/16

    Fingerprint Dive into the research topics of 'Improved abnormality detection from raw ECG signals using feature enhancement'. Together they form a unique fingerprint.

  • Profiles

    No photo of Max Pandit

    Cite this

    Pandit, D., Zhang, L., Aslam, N., Liu, C., & Chattopadhyay, S. (2016). Improved abnormality detection from raw ECG signals using feature enhancement. In J. Du, C. Liu, K. Li, L. Wang, Z. Tong, M. Li, & N. Xiong (Eds.), 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, ICNC-FSKD 2016 (pp. 1402-1406). [7603383] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FSKD.2016.7603383