An approach for automatic sleep stage scoring and apnea-hypopnea detection
In this article we present an application of data mining to the medical domain sleep research, an approach for automatic sleep stage scoring and apnea-hypopnea detection. By several combined techniques (Fourier and wavelet transform, derivative dynamic time warping, and waveform recognition), our approach extracts meaningful features (frequencies and special patterns like k-complexes and sleep spindles) from physiological recordings containing EEG, ECG, EOG and EMG data. Based on these pieces of information, an ensemble of decision trees is constructed using the principle of bagging, which classifies sleep epochs in their sleep stages according to the rules by Rechtschaffen and Kales and annotates occurrences of apnea-hypopnea (total or partial cessation of respiration). After that, case-based reasoning is applied in order to improve quality. We tested and evaluated our approach on several large public databases from PhysioBank, which showed an overall accuracy of 95.2% for sleep stage scoring and 94.5% for classifying minutes as apneic or non-apneic.
sleep time series data processing signal processing feature extraction pattern classification biomedical signal processing