Key result
A proposed cepstrum coefficient method applying dynamic time warping successfully extracted feature vectors and classified Normal and PACED BEAT ECG signals from the MIT/BIH database.
Why the study?
A method to extract feature vectors from long-term ECG signals is needed to identify characteristics and diagnose abnormalities.
A proposed cepstrum coefficient method using dynamic time warping successfully extracts features and classifies normal and paced beats from long-term ECG signals.
This paper proposes a cepstrum coefficient method applying the dynamic time warping technique to extract the feature vectors from long-term ECG signals. Utilizing this method, one can identify the characteristics hidden in an ECG signal; and then classify the signal as well as diagnose the abnormalities. To evaluate this method, the Normal and PACED BEAT data from the MIT/BIH database are used. The results show that the proposed method successfully extracts the corresponding feature vectors, distinguishes the difference and classifies both signals.
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Jen et al. (2007) studied ECG abnormalities. Cepstrum coefficient method applying dynamic time warping was evaluated on Feature vector extraction and signal classification. A proposed cepstrum coefficient method applying dynamic time warping successfully extracted feature vectors and classified Normal and PACED BEAT ECG signals from the MIT/BIH database.
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