An artificial neural network to detect EEG seizures.

Sir, Electroencephalography (EEG) in its conventional form is not routinely useful in the diagnosis of any specific epileptic seizure discharge as it produces a huge amount of normal data intermixed with relatively rare clinically significant events. Several computer algorithms and programs for the detection of seizures have been developed, but these methods are found insufficient to recognize the exceptions and minimize the number of false detections. To overcome these problems, artificial neural network (ANN) has been used for the classification of seizures in EEG. It has been suggested that instead of analyzing only the frequency and amplitude changes, obtaining power spectra by fast Fourier Transform (FFT) of EEG provides more information. The experiments were carried out with male Charles Foster rats weighing between 200-250 grams. EEG recording electrodes were implanted on the rat’s head under Urethane (Sigma, USA) anesthesia (1.5 gm/kg i.p). Three stainless steel screw electrodes of 1 mm diameter soldered with flexible radio wires were implanted extradurally. Two electrodes for EEG recordings were placed on the frontal and occipital part of the skull and one reference electrode was placed on the anteriormost part of the skull. To induce epileptic seizures, 50 units of Benzyl Penicillin in a volume of 10ml was injected 2 mm below the cortical surface of the parietal region of the brain. The intracerebral injection of the above-mentioned dose of benzyl penicillin induces spontaneous seizure initiation after 3-5 minutes of injection and observed its continuous presence as spike patterns in EEG up to an average of 30 minutes. The single-channel bipolar EEG signals were recorded with standard amplifier setting just before and continuously for 45 minutes through an electroencephalograph (EEG-8, Madicare, India) from the time of injection of penicillin to the end of seizure patterns. The EEG signals were not only recorded on papers, but also on two minutes separate data files to the computer hard disk after digitization of the traces at 256 Hz. The data were collected using the Visual Lab-M software (ADLink Technology Inc., Taiwan). A three-layered feed forward back propagation program written in C++ programming language was used for the detection of seizures. It has been described earlier that single hidden layer neural networks are universal approxomator and universal classifier; thus the network contains only one hidden layer. Onesecond epochs of seizure and normal patterns were taken and their FFT or Power Spectra calculated separately after filtering the raw data. Selected digital FFT values of seizure and normal patterns were used for the training of the network. The number of neurons in the input layer is fixed by input data from digital values of FFT between 10 Hz to 30 Hz. The patterns of seizure Figure 1: Figure shows the FFT of typical seizure and normal EEG patterns used for training and testing of the ANN. X-axis represents the fraction between EEG frequency and the sampling frequency (256). Yaxis represents the relative power of seizure and normal EEG. The input layer of ANN contains 40 nodes weighted for training and testing of seizure and normal EEG patterns, selected from digital values of FFT between points ‘a’ to ‘b’, i.e. FFT data from 10 Hz to 30 Hz.

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