Sleep stages classification based on temporal pattern recognition in neural network approach

Several previous researchers in sleep stages classification often considered that sleep stages were independent events. They have assumed that every epoch in sleep stages is independent. By nature, sleep is a sequence process so that the current sleep stages will affect to the next sleep stages. Ten datasets of single lead ECG signal from healthy people have been collected. Fifteen features can be extracted from raw ECG signal to describe the sleep stages. Smoothing signal using wavelet denoising is done as the preprocessing steps in order to eliminate noise. Data normalization of input value is also used to handle extreme feature values which will be mapped by activation function in neural network approach. This paper evaluate contribution of temporal pattern in the sleep stages classification result based on fact that sleep stages is a time series data. Multlayer perceptron (MLP) and Time Delay Neural network (TDNN) using standard back propagation algorithm and moment technique are applied to analyze the contribution of the temporal pattern. TDNN is an extended of MLP that the inputs are sequence of current epoch and previous epoch. TDNN as a classifier that can learn temporal pattern has shown better performance than MLP. It shows that temporal pattern takes a part to determine the correct classification result in the sleep stages classification. An appropriate memory long of temporal pattern is required to get the optimal classification result because longer memory cannot guarantee that the classification result is always better.