Anzeige
Erschienen in:
2011 | OriginalPaper | Buchkapitel
Adaptive Abnormality Detection on ECG Signal by Utilizing FLAC Features
verfasst von : Jiaxing Ye, Takumi Kobayashi, Tetsuya Higuchi, Nobuyuki Otsu
Erschienen in: Learning and Intelligent Optimization
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
In this paper we propose a self-adaptive algorithm for noise robust abnormality detection on ECG data. For extracting features from ECG signals, we propose a feature extraction method by characterizing the magnitude, frequency and phase information of ECG signal as well as the temporal dynamics in time and frequency domains. At abnormality detection stage, we employ the subspace method for adaptively modeling the principal pattern subspace of ECG signal in unsupervised manner. Then, we measure the dissimilarity between the test signal and the trained major pattern subspace. The atypical periods can be effectively discerned based on such dissimilarity degree. The experimental results validate the effectiveness of the proposed approach for mining abnormalities of ECG signal including promising performance, high efficiency and robust to noise.