Analysis of human PPG, ECG and EEG signals by eigenvector methods

This paper presents eigenvector methods for analysis of the photoplethysmogram (PPG), electrocardiogram (ECG), electroencephalogram (EEG) signals recorded in order to examine the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) upon the human electrophysiological signal behavior. The features representing the PPG, ECG, EEG signals were obtained by using the eigenvector methods. In addition to this, the problem of selecting relevant features among the features available for the purpose of discrimination of the signals was dealt with. Some conclusions were drawn concerning the efficiency of the eigenvector methods as a feature extraction method used for representing the signals under study

Analysis of sleep EEG activity during hypopnoea episodes by least squares support vector machine employing AR coefficients

This paper presents the application of least squares support vector machines (LS-SVMs) for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. The obstructive sleep apnoea hypopnoea syndrome (OSAH) means ¿cessation of breath¿ during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. Decision making was performed in two stages: feature extraction by computation of autoregressive (AR) coefficients and classification by the LS-SVMs. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the LS-SVMs. The performance of the LS-SVMs was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed LS-SVM has potential in detecting changes in the human EEG activity due to hypopnoea episodes

Adaptive neuro-fuzzy inference system employing wavelet coefficients for detection of alterations in sleep EEG activity during hypopnoea episodes

The Obstructive Sleep Apnoea Hypopnoea Syndrome (OSAH) means "cessation of breath" during the sleep hours and the sufferers often experience related changes in the electrical activity of the brain and heart. This paper describes the application of adaptive neuro-fuzzy inference system (ANFIS) model for automatic detection of alterations in the human electroencephalogram (EEG) activities during hypopnoea episodes. Decision making was performed in two stages: feature extraction by computation of wavelet coefficients and classification by the ANFIS trained with the backpropagation gradient descent method in combination with the least squares method. The EEG signals (pre and during hypopnoea) from three electrodes (C3, C4 and O2) were used as input patterns of the three ANFIS classifiers. To improve diagnostic accuracy, the fourth ANFIS classifier (combining ANFIS) was trained using the outputs of the three ANFIS classifiers as input data. The proposed ANFIS model combined the neural network adaptive capabilities and the fuzzy logic qualitative approach. Some conclusions concerning the saliency of features on detecting any possible changes in the human EEG activity due to hypopnoea (mild case of cessation of breath) occurrences were drawn through analysis of the ANFIS. The performance of the ANFIS model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed ANFIS model has potential in detecting changes in the human EEG activity due to hypopnoea episodes

Alterations in sleep electroencephalography and heart rate variability during the obstructive sleep apnoea and hypopnoea

Sleep is a vital physiological function and high quality sleep is essential for maintaining the good health. Sleep disorders however are amongst the most common disorders suffered by humans and it is rare for most people to regularly enjoy the amount of quality sleep they need. The behavioural and social causes of sleep disorders are typically the result of modern lifestyle, which are usually linked to Obstructive Sleep Apnoea Hypopnea Syndrome (OSAHS). Healthcare professionals and sleep researchers are currently looking for ways to improve the clinical diagnosis of OSAH sufferers

On the ballistic performance of the AA7075 based functionally graded material with boron carbide reinforcement

In this study, the functionally graded materials (FGMs) bearing three layers were produced via hot pressing to investigate their ballistic performances against 7.62 mm armor piercing (AP) projectile. In the FGM samples, the bottom layer was considered as unreinforced AA7075 alloy, whereas the middle and top layers were made of the AA7075 composite layers having various proportions of B4C particles. Prior to the ballistic testing, the hardness change in the layers with respect to aging time at different temperatures was determined. And then, the ballistic testing of the samples was performed at a ballistic laboratory using 7.62 mm AP projectile. In the ballistic testing, five separate specimens for each FGM group having fixed composition and thickness were used. The experimental results showed that the ballistic impact resistance of the investigated FGMs increased with increasing boron carbide content and the thickness in the layers. Moreover, there were no separations observed between the layers in the failed samples