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A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques

A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques

Ramanujam Elangovan, Padmavathi S.
Copyright: © 2019 |Volume: 9 |Issue: 2 |Pages: 18
EISBN13: 9781522566519|ISSN: 2642-1577|EISSN: 2642-1585|DOI: 10.4018/IJAIML.2019070103
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MLA

Elangovan, Ramanujam, and Padmavathi S. "A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques." IJAIML vol.9, no.2 2019: pp.39-56. http://doi.org/10.4018/IJAIML.2019070103

APA

Elangovan, R. & Padmavathi S. (2019). A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques. International Journal of Artificial Intelligence and Machine Learning (IJAIML), 9(2), 39-56. http://doi.org/10.4018/IJAIML.2019070103

Chicago

Elangovan, Ramanujam, and Padmavathi S. "A Review on Time Series Motif Discovery Techniques an Application to ECG Signal Classification: ECG Signal Classification Using Time Series Motif Discovery Techniques," International Journal of Artificial Intelligence and Machine Learning (IJAIML) 9, no.2: 39-56. http://doi.org/10.4018/IJAIML.2019070103

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Abstract

Cardiovascular disease diagnosis from an ECG signal plays an important and significant role in the health care system. Recently, numerous researchers have developed an automatic time series-based multi-step diagnosis system for the fast and accurate diagnosis of ECG abnormalities. The multi-step procedure involves ECG signal acquisition, signal pre-processing, feature extraction, and classification. Among which, the feature extraction plays a vital role in the field of accurate diagnosis. The features may be different types such as statistical, morphological, wavelet or any other signal-based approach. This article discusses various time series motif-based feature extraction techniques with respect to a different dimension of ECG signal.

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