Classification of EEG Signal by Using Optimized Quantum Neural Network

Abstract

In recent years the algorithms of machine learning were used for brain signals identification as a useful technique for diagnosing diseases like Alzheimer's and epilepsy. In this paper, the Electroencephalogram (EEG) signals are classified using an optimized Quantum neural network (QNN) after normalizing these signals. The wavelet transform (WT) and the independent component analysis (ICA) were utilized for feature extraction. These algorithms were used to reduce the dimensions of the data, which is an input to the optimized QNN for the purpose of performing the classification process after the feature extraction process. This research uses an optimized QNN, a form of feedforward neural network (FFNN), to recognize the EEG signals.The Particle swarm optimization (PSO) algorithm was used to optimize the quantum neural network, which improved the training process of the system's performance. The optimized QNN provided us with somewhat faster and more realistic results. According to simulation results, the total classification for ICA is 82.4 percent, while the total classification for WT is 78.43 percent; from these results, using the ICA for feature extraction is better than using WT.

Electrocardiograph signal recognition using wavelet transform based on optimized neural network

“…These impacts are caused by the patient's physiology, gender, and age, as well as the measurement system's characteristics [21]. The normalization reduces the influence of feature extraction methods on the peak-to-peak magnitudes and the signal offset [22]. Discrete wavelet transform (DWT) is used to de-noise ECG data after baseline corrections and normalizations at three, five, and eight levels.…”