The Usage of Statistical Features in the Approximatıon Components of Wavelet Decomposition for ECG Classification: A Case Study For Standing, Walking and Single Jump Conditions
Yazarlar (2)
Arş. Gör. Makbule Hilal MÜTEVELLİ ÖNCÜL Kastamonu Üniversitesi, Türkiye
Semih Ergin Eskişehir Osmangazi Üniversitesi, Türkiye
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Özgün Makale
(Uluslararası alan indekslerindeki dergilerde yayınlanan tam makale)
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| Dergi Adı | Electronic Journal of Vocational Colleges | ||
| Dergi ISSN | 2146-7684 | ||
| Dergi Tarandığı Indeksler | Open Academic Journals Index, Elektronischen Zeitschriftenbibliothek | ||
| Makale Dili | İngilizce | Basım Tarihi | 12-2018 |
| Cilt / Sayı / Sayfa | 8 / 2 / 178–182 | DOI | – |
| Özet |
| The purpose of this study is to classifyelectrocardiogram (ECG) signals with a high accuracy rate. The ECG signals usedare obtained from the Physiobank archive. These signals are preprocessed toremove noise. Features with distinctiveness in classification are obtained bothin the time domain and the frequency domain. The Discrete Wavelet Transformmethod is used for feature extraction in frequency domain. ECG signals areclassified by the Naive Bayes method after the required features are extracted. |
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