Real-time CHF detection from ECG signals using a novel discretization method

Orhan U.

COMPUTERS IN BIOLOGY AND MEDICINE, cilt.43, sa.10, ss.1556-1562, 2013 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 43 Sayı: 10
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.compbiomed.2013.07.015
  • Dergi Adı: COMPUTERS IN BIOLOGY AND MEDICINE
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.1556-1562
  • Anahtar Kelimeler: Congestive heart failure, Electrocardiography, EFiA-EWiT discretization, Time series classification, Real-time detection, SUPPORT VECTOR MACHINES, NEURAL-NETWORK, ARRHYTHMIA CLASSIFICATION, FEATURE-EXTRACTION, NEAREST-NEIGHBORS, RECOGNITION, BEATS, EEG
  • Çukurova Üniversitesi Adresli: Evet

Özet

This study proposes a new method, equal frequency in amplitude and equal width in time (EFiA-EWiT) discretization, to discriminate between congestive heart failure (CHF) and normal sinus rhythm (NSR) patterns in ECG signals. The ECG unit pattern concept was introduced to represent the standard RR interval, and our method extracted certain features from the unit patterns to classify by a primitive classifier. The proposed method was tested on two classification experiments by using ECG records in Physiobank databases and the results were compared to those from several previous studies. In the first experiment, an off-line classification was performed with unit patterns selected from long ECG segments. The method was also used to detect CHF by real-time ECG waveform analysis. In addition to demonstrating the success of the proposed method, the results showed that some unit patterns in a long ECG segment from a heart patient were more suggestive of disease than the others. These results indicate that the proposed approach merits additional research. (C) 2013 Elsevier Ltd. All rights reserved.