Detection of Insomnia from EEG and ECG

Abstract

Insomnia is a sleep disorder that causes disturbance in a normal sleep pattern resulting from the difficulties to fall asleep or to stay asleep. Biosignals of human body if measured are also affected due to these abnormal conditions. A current practise in diagnosing insomnia is through clinical interview by the physician which is subjective and suffers from human error judgement. Therefore, a more reliable and accurate diagnostic tools are needed to help physician in making decision. The objective of this study is to classify healthy and insomnia by implementing advanced classification technique based on Artificial Neural Network (ANN).

In this study, sleep EEG and ECG signals of 10 insomnia patients and 10 healthy subjects are analysed. Several linear and nonlinear features are extracted from the denoised signals: linear features (power spectral of EEG frequency bands, brain rate, Hjorth parameters, heart rate variability) and nonlinear features (Largest Lyapunov Exponent (LLE), Sample Entropy and Correlation Dimension (CD)).

For classification purpose, a Feedforward Neural Network (FNN) is implemented to classify the two groups. Half of the data is used for training and the other half is used for testing the classifier. The Levenberg Marquardt backpropagation algorithm is used as a training function. Several numbers of hidden layers are tested in order to achieve optimum classification accuracy. A classification accuracy of 81.3 % is obtained for 3 hidden layers This result shows that the combination of features extracted from EEG and ECG signals during sleep and FFN are useful to be adopted as a biomarker and classifier in identifying insomnia.of FNN.