The prevalence of enterotoxigenic Bacteroides fragilis (ETBF) was investigated in stool specimens from 73 patients with colorectal cancer and from 59 control patients. Stool specimens were cultured on Bacteroides Bile Esculin agar and B. fragilis was identified by conventional methods. After DNA extraction, the enterotoxin gene (bft) was detected by PCR in 38% of the isolates from colorectal cancer patients, compared with 12% of the isolates from the control group (p 0.009). This is the first study demonstrating an increased prevalence of ETBF in colorectal cancer patients.

We developed a new method for estimation of vigilance level by using both EEG and EMG signals recorded during transition from wakefulness to sleep. Previous studies used only EEG signals for estimating the vigilance levels. In this study, it was aimed to estimate vigilance level by using both EEG and EMG signals for increasing the accuracy of the estimation rate. In our work, EEG and EMG signals were obtained from 30 subjects. In data preparation stage, EEG signals were separated to its subbands using wavelet transform for efficient discrimination, and chin EMG was used to verify and eliminate the movement artifacts. The changes in EEG and EMG were diagnosed while transition from wakefulness to sleep by using developed artificial neural network (ANN). Training and testing data sets consist of the subbanded components of EEG and power density of EMG signals were applied to the ANN for training and testing the system which gives three situations for the vigilance level of the subject: awake, drowsy, and sleep. The accuracy of estimation was about 98-99% while the accuracy of the previous study, which uses only EEG, was 95-96%.

Analysis and classification of sleep stages is essential in sleep research. In this particular study, an alternative system which estimates sleep stages of human being through a multi-layer neural network (NN) that simultaneously employs EEG, EMG and EOG. The data were recorded through polisomnography device for 7 h for each subject. These collective variant data were first grouped by an expert physician and the software of polisomnography, and then used for training and testing the proposed Artificial Neural Network (ANN). A good scoring was attained through the trained ANN, so it may be put into use in clinics where lacks of specialist physicians.

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In this study, whether the wavelet transform method is better for spectral analysis of the brain signals is investigated. For this purpose, as a spectral analysis tool, wavelet transform is compared with fast Fourier transform (FFT) applied to the electroencephalograms (EEG), which have been used in the previous studies. In addition, the time-domain characteristics of the wavelet transform are also detected. The comparison results show that the wavelet transform method is better in detecting brain diseases.

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