Classification of Arrhythmias Using Linear Predictive Coefficients and Probabilistic Neural Network

Cardiac arrhythmia, which means abnormality of heart rhythm, in fact refers to disorder in electrical conduction system of the heart. The aim of this paper is to present a classifier system based on Probabilistic Neural Networks in order to detect and classify abnormal heart rates, where besides its simplicity, has high resolution capability. The proposed algorithm has three stages. At first, the electrocardiogram signals impose into preprocessing block. After preprocessing and noise elimination, the exact position of R peak is detected by multi resolution wavelet analysis. In the next step, the extracted linear predictive coefficients (LPC) of QRS complex will enter in to the classification block as an input. A Support Vector Machine classifier is developed in parallel to verify and measure the PNN classifier’s success. The experiments were conducted on the ECG data from the MIT-BIH database to classify four kinds of abnormal waveforms and normal beats such as Normal sinus rhythm, Atrial premature contraction (APC), Right bundle branch block (RBBB) and Left bundle branch block (LBBB). The results show 92.9% accuracy and 93.17% sensitivit

Epilepsy Classification Framework Utilizing Joint Time-Frequency Signal Analysis and Processing

Time Frequency Signal Analysis and Processing (TFSAP) have been proposed in order to analyse the signal in both the time and the frequency domains. Electroencephalography (EEG) as a time-varying frequency signal is an interesting field in which Time Frequency Distribution (TFD) could be used in order to visualize the simultaneous distributions of signal energy in different physiological and pathological brain states. Particularly, epileptic signals due to their great features of seizure activity are introduced as the most attractive research field among researchers. This study outlines an investigation on two main pathologic brain states including, pre-ictal activity and seizure activity compared to normal activity. Pseudo-Wigner -Ville and Choi-William distributions are used in order to visualize the energy content of signals in these states. Different segments of brain electrical activity are analyzed using these distributions. Finally, Renyi’s entropy as an important characteristic which offer insight towards the EEG signal processing has been extracted from TFDs. The results obtained indicate that Renyi’s entropy is a high-quality discriminative feature especially in alpha and delta sub-bands of the EEG signal

Journey through a virtual tunnel: Simulated motion and its effects on the experience of time

This paper examines the relationship between time and motion perception in virtual environments. Previous work has shown that the perception of motion can affect the perception of time. We developed a virtual environment that simulates motion in a tunnel and measured its effects on the estimation of the duration of time, the speed at which perceived time passes, and the illusion of self-motion, also known as vection. When large areas of the visual field move in the same direction, vection can occur; observers often perceive this as self-motion rather than motion of the environment. To generate different levels of vection and investigate its effects on time perception, we developed an abstract procedural tunnel generator. The generator can simulate different speeds and densities of tunnel sections (visibly distinguishable sections that form the virtual tunnel), as well as the degree of embodiment of the user avatar (with or without virtual hands). We exposed participants to various tunnel simulations with different durations, speeds, and densities in a remote desktop and a virtual reality (VR) laboratory study. Time passed subjectively faster under high-speed and high-density conditions in both studies. The experience of self-motion was also stronger under high-speed and high-density conditions. Both studies revealed a significant correlation between the perceived passage of time and perceived self-motion. Subjects in the virtual reality study reported a stronger self-motion experience, a faster perceived passage of time, and shorter time estimates than subjects in the desktop study. Our results suggest that a virtual tunnel simulation can manipulate time perception in virtual reality. We will explore these results for the development of virtual reality applications for therapeutic approaches in our future work. This could be particularly useful in treating disorders like depression, autism, and schizophrenia, which are known to be associated with distortions in time perception. For example, the tunnel could be therapeutically applied by resetting patients’ time perceptions by exposing them to the tunnel under different conditions, such as increasing or decreasing perceived time