Alcoholism classification based on EEG data using Independent Component Analysis (ICA), Wavelet de-noising and Probabilistic Neural Network (PNN)

Alcoholism is a clinical symptom characterized by a tendency to drink more alcohol than planned or commonly called alcoholics. Alcoholics will suffer the damage in some parts of the body, including the brain. One way to detect alcoholics from the brain is to record the electrical activity of the brain through the scalp or called electroencephalography (EEG). EEG records are often disturbed by noise such as muscle movements, eye blinking and heartbeat. Therefore, this research suggests Independent Component Analysis (ICA), as noise removal, Stationary Wavelet Transform (SWT) as a feature extraction method and are classified into two classes, namely alcoholism and normal using Probabilistic Neural Network (PNN). In this research, the result obtained from the ICA noise removal, signal decomposition using Daubechies SWT at level 6 and Probabilistic Neural Network (PNN) is considered effective to extract features and classify the 64 channels alcoholism data. The data come from Neurodynamics Laboratory, State University of New York Health Center. The result of this research generate an accuracy of 85.00% from 100 random data trial using ICA, SWT decomposition level 6, Wavelet Daubechies type 4 and PNN deviation value of 0.6.