An Approach for Automatic Sleep Stage Scoring and Apnea-Hypopnea Detection

This paper presents an application of data mining to the medical domain sleep research, i.e. an approach for automatic sleep stage scoring and apnea-hypopnea detection. By several combined techniques (Fourier and wavelet transform, DDTW and waveform recognition), our approach extracts meaningful features (frequencies and special patterns) from EEG, ECG, EOG and EMG data, on which a decision trees classifier is built for classifying epochs into their sleep stages (according to the rules by Rechtschaffen and Kales) and annotating occurrences of apnea-hypopnea (total or partial cessation of respiration). After that, case-based reasoning is applied to improve quality. We evaluated our approach on 3 large public databases from PhysioBank, which showed an overall accuracy of 95.2% for sleep stage scoring and 94.5% for classifying apneic/non-apneic minutes.