Abstract
Sleep Apnea is an anomaly in sleeping characterized by short pause in breathing. Failure to treat sleep apnea leads to fatal complications in both psychological and physiological being of human. Electroencephalogram (EEG) performs an important task in probing for sleep apnea through identifying and recording the brain’s activities while sleeping. In this study, computer aided detection of sleep apnea from EEG signals is developed to optimize and increase the prompt recognition and diagnosis of sleep apnea in patients. The time domain, wavelets, and frequency domain of the EEG signals were computed, and features were extracted from these domains. These features are inputted into two machine learning algorithms: Support Vector Machine and K-Nearest Neighbors of different kernel functions and orders. Evaluation metrics such as specificity, accuracy, and sensitivity are computed and analyzed for the classifiers. The KNN classifier outperforms the SVM in classifying apnea from non-apnea events in patients. The KNN order 3 shows the highest performance sensitivity of 85.92%, specificity of 80% and accuracy of 82.69%.
Highlights
Sleep is a state of rest and part of a daily rhythm essential for human life which covers about thirty- three percent (33%) of human existence[1].Sleep is an important part of human existence that helps in regulating mental and physical activities [2]
True Positive (TP) is the number of cases correctly identified as Sleep Apnea, False Positive (FP) is the number of normal cases incorrectly identified as Sleep Apnea, True Negative (TN) is the number of cases correctly identified as Normal, False Negative (FN) is the number of Sleep Apnea cases incorrectly identified as Normal
Features were extracted from the time domain, wavelet and frequency domain of these frames as discussed earlier
Summary
Sleep is a state of rest and part of a daily rhythm essential for human life which covers about thirty- three percent (33%) of human existence[1].Sleep is an important part of human existence that helps in regulating mental and physical activities [2]. It occurs due to the relaxation of the sensitive tissues at the end of the throat The PSG signalswere scored offline by sleep experts [7] These signals contain of: Electromyogram (EMG); Electrooculogram (EOG), Electrocardiogram (ECG) and Electroencephalogram (EEG) [7]. It has been studied that sleep apnea can be detected by monitoring brain activities [9]. Many sleep studies show that sleep disorders can be identified and predicted through channels (C3-A2 channel or C4-A1 channel) of the EEG signal [9].In this research, we are going to develop a system that automatically recognizes apnea events in patients using the time domain, wavelets, and frequency domains of EEG signals. Features were extracted from these domains and fed into classifiers
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