Key result
Time series motif-based feature extraction enables accurate ECG abnormality diagnosis across various clinical techniques.
Why the study?
Accurate diagnosis of cardiovascular disease from ECG signals relies on effective feature extraction methods, with time series motif-based techniques being important but requiring comprehensive review.
This review summarizes various time series motif-based feature extraction techniques for automated ECG signal classification and diagnosis.
Surveys motif-based ECG feature methods; leaves open optimal techniques for clinical automated diagnosis.
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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Ramanujam et al. (2019) conducted a review in Cardiovascular disease / ECG abnormalities. Time series motif-based feature extraction techniques was evaluated. This review discusses various time series motif-based feature extraction techniques for the accurate diagnosis of ECG abnormalities.
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