Automatic sleep staging based on SVD, VMD, HHT and morphological features of single-lead ECG signal

Yücelbaş Ş., Yücelbaş C., Tezel G., Özşen S., Yosunkaya Ş.

Expert Systems with Applications, cilt.102, ss.193-206, 2018 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 102
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.eswa.2018.02.034
  • Dergi Adı: Expert Systems with Applications
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.193-206
  • Anahtar Kelimeler: Automatic sleep staging, ECG, HHT, Morphological features, SVD, VMD
  • Hakkari Üniversitesi Adresli: Evet

Özet

Electroencephalogram (EEG) signals, which are among the primary polysomnography (PSG) signals used for sleep staging, are difficult to obtain and interpret. However, it is much easier to obtain and interpret electrocardiogram (ECG) signals. The use of ECG signals for automatic sleep staging systems could bring practicality to these systems. In this study, ECG signals were used to identify the wake (W), non-rapid eye movement (NREM) and rapid eye movement (REM) stages of the sleep data from two different databases with 17,758 epochs of 28 subjects (21 healthy subjects and 7 obstructive sleep apnea (OSA) patients) in total. Four different methods were used to extract features from these signals: Singular Value Decomposition (SVD), Variational Mode Decomposition (VMD), Hilbert Huang Transform (HHT), and Morphological method. As a result of applying the methods separately, four different data sets were obtained. The four different datasets were given to the Wrapper Subset Evaluation system with the best-first search algorithm. After the feature selection procedure, the datasets were separately classified by using the Random Forest classifier. The results were interpreted by using the essential statistical criteria. Among the methods, morphological method was the most successful and it was followed by SVD in terms of success rate for both two databases. For the first database, the mean classification accuracy rate, Kappa coefficient and mean F-measure value of the Morphological method were found as 87.11%, 0.7369, 0.869 for the healthy and 78.08%, 0.5715, 0.782 for the patient, respectively. For the second database, the same statistical measures were determined as 77.02%, 0.4308, 0.755 for the healthy and 76.79%, 0.5227, 0.759 for the patient, respectively. The performance results of the study, which is consistent with real life applications, were compared with the previous studies in this field listed in the literature.