Automatic Diagnosis of Obstructive Sleep Apnea/Hypopnea Events Using Respiratory Signals.

Obstructive sleep apnea is a sleep disorder which may lead to various results. While some studies used real-time systems, there are also numerous studies which focus on diagnosing Obstructive Sleep Apnea via signals obtained by polysomnography from apnea patients who spend the night in sleep laboratory. The mean, frequency and power of signals obtained from patients are frequently used.

Muscle fatigue detection in EMG using time-frequency methods, ICA and neural networks.

The electromyography (EMG) signals give information about different features of muscle function. Real-time measurements of EMG have been used to observe the dissociation between the electrical and mechanical measures that occurs with fatigue. The purpose of this study was to detect fatigue of biceps brachia muscle using time-frequency methods and independent component analysis (ICA).

Time frequency based coherence analysis between EEG and EMG activities in fatigue duration.

In voluntary movements, functional role of synchronized neuronal activity in the human motor system is important to detect and diagnose of the some diseases. In some previous studies, EEG signals and responses belong to an exercise are examined and an increased EEG activity reported in alpha frequency band. The reason of this is not clear whether a change is a direct result of the exhaustion or whether it is an adaptation.

Comparison of AR and Welch methods in epileptic seizure detection.

Brain is one of the most critical organs of the body. Synchronous neuronal discharges generate rhythmic potential fluctuations, which can be recorded from the scalp through electroencephalography. The electroencephalogram (EEG) can be roughly defined as the mean electrical activity measured at different sites of the head.

Wavelet neural network classification of EEG signals by using AR model with MLE preprocessing.

Since EEG is one of the most important sources of information in therapy of epilepsy, several researchers tried to address the issue of decision support for such a data. In this paper, we introduce two fundamentally different approaches for designing classification models (classifiers); the traditional statistical method based on logistic regression and the emerging computationally powerful techniques based on artificial neural networks (ANNs). Logistic regression as well as feedforward error backpropagation artificial neural networks (FEBANN) and wavelet neural networks (WNN) based classifiers were developed and compared in relation to their accuracy in classification of EEG signals.