Ahmed Rabbi

Human performance evaluation based on EEG signal analysis: A prospective review

Electroencephalogram (EEG) signal, the signature of brain activity, can be used to quantify for human performance evaluation. There are ongoing efforts by scientists and researchers in this area. Different traditional and novel signal processing and analysis methods have been applied to evaluate performance, mental workload, and task engagement based on EEG signals. Linear change in the indices with the increase in task difficulty was reported. In addition, EEG index has been used as parameter for performance optimization. In this review article, we will discuss briefly the literature on human performance estimation based on some physiological parameters, EEG in particular. In this paper, the current state of the research field is presented and possible future research options are discussed.

Development of a Mobile Phone Based e-Health Monitoring Application

The use of Electrocardiogram (ECG) system is important in primary diagnosis and survival analysis of the heart diseases. Growing portable mobile technologies have provided possibilities for medical monitoring for human vital signs and allow patient move around freely. In this paper, a mobile health monitoring application program is described. This system consists of the following sub-systems: real-time signal receiver, ECG signal processing, signal display in mobile phone, and data management as well five user interface screens. We verified the signal feature detection using the MIT-BIH arrhythmia database. The detection algorithms were implemented in the mobile phone application program. This paper describes the application system that was developed and tested successfully.

Fuzzy rule-based seizure prediction based on correlation dimension changes in intracranial EEG

In this paper, we present a method for epileptic seizure prediction from intracranial EEG recordings. We applied correlation dimension, a nonlinear dynamics based univariate characteristic measure for extracting features from EEG segments. Finally, we designed a fuzzy rule-based system for seizure prediction. The system is primarily designed based on experts knowledge and reasoning. A spatial-temporal filtering method was used in accordance with the fuzzy rule-based inference system for issuing forecasting alarms. The system was evaluated on EEG data from 10 patients having 15 seizures.

Seizure prediction using adaptive neuro-fuzzy inference system

In this study, we present a neuro-fuzzy approach of seizure prediction from invasive Electroencephalogram (EEG) by applying adaptive neuro-fuzzy inference system (ANFIS). Three nonlinear seizure predictive features were extracted from a patients data obtained from the European Epilepsy Database, one of the most comprehensive EEG database for epilepsy research. A total of 36 hours of recordings including 7 seizures was used for analysis. The nonlinear features used in this study were similarity index, phase synchronization, and nonlinear interdependence. We designed an ANFIS classifier constructed based on these features as input. Fuzzy if-then rules were generated by the ANFIS classifier using the complex relationship of feature space provided during training. The membership function optimization was conducted based on a hybrid learning algorithm. The proposed method achieved highest sensitivity of 80% with false prediction rate as low as 0.46 per hour.

A Comparison among Several P300 Brain-Computer Interface Speller Paradigms

Since the brain-computer interface (BCI) speller was first proposed by Farwell and Donchin, there have been modifications in the visual aspects of P300 paradigms. Most of the changes are based on the original matrix format such as changes in the number of rows and columns, font size, flash/blank time, and flash order. The improvement in the resulting accuracy and speed of such systems has always been the ultimate goal. In this study, we have compared several different speller paradigms including row-column, single character flashing, and two region-based paradigms which are not based on the matrix format. In the first region-based paradigm, at the first level, characters and symbols are distributed over seven regions alphabetically, while in the second region-based paradigm they are distributed in the most frequently used order. At the second level, each one of the regions is further subdivided into seven subsets. The experimental results showed that the average accuracy and user acceptability for two region-based paradigms were higher than those for traditional paradigms such as row/column and single character.

Automatic Detection Of Electrocardiogram ST Segment: Application In Ischemic Disease Diagnosis

The analysis of electrocardiograph (ECG) signal provides important clinical information for heart disease diagnosis. The ECG signal consists of the P, QRS complex, and T-wave. These waves correspond to the fields induced by specific electric phenomenon on the cardiac surface. Among them, the detection of ischemia can be achieved by analysis the ST segment. Ischemia is one of the most serious and prevalent heart diseases. In this paper, the European database was used for evaluation of automatic detection of the ST segment. The method comprises several steps; ECG signal loading from database, signal preprocessing, detection of QRS complex and R-peak, ST segment, and other relation parameter measurement. The developed application displays the results of the analysis. Keywords-Electrocardiogram (ECG); Ischemia; European ST- T database; QRS complex ; ST segment. I. INTRODUCTION Electrocardiographic (ECG) signals information is derived from analysis of the information indirectly reflected on the surface ECG. The ECG signal is able to make of basic information for heart disease, indisposed of the autonomic nervous system and stress. The world Health Organization estimates that 17.5 million people died of cardiovascular disease. It is representing 30% of all global deaths. Out of these, 7.6 million were due to coronary artery disease (CAD)(1). During the last few years, a lot of research has provided the solution of analysis and diagnosis in ECG by adopting new technologies and algorithms. Among them, ischemic heart disease constitutes one of the most common fatal diseases in the world. Myocardial ischemia is caused by a lack of sufficient blood flow to the contractile cells and many lead to myocardial information with its severe sequel of heart failure, arrhythmias and death (2). The ischemic disease is usually identified in the standard ECG by changes in values of measured amplitudes, times and durations on the ST-T complex. The ST-T complex of the ECG reflects the time period from the end of active ventricular depolarization to the end of depolarization in the heart cycle (3-4). Therefore, ST- segment changes are common ECG signal markers of important Myocardial ischemia (5). The several methods for ischemia parameter detection (T wave and ST complex) have been proposed. In generally, all of them are based on the spectral estimation (6) and signal point from the ST segment better characterizes ischemic patterns (3, 7). The various methods have been applied to the ECG for ischemia analysis and detection: used the First Fourier Transform (FFT) to analyze the frequency component (8), fuzzy-logic, neural network, genetic algorithm, support vector machines (SVM), wavelet transform and many more (9). However, most of the algorithms have sensitivity above the 80%. In this study we consider two applications of the ST segment detection and display program: Detection of ischemia episodes and monitoring PC programming. The modifications in the shape parameters have been used for ST segment measurement. II. METHOD AND MATERIAL A. European ST-T Database

Changes in dynamical characteristics of epileptic EEG in rats using recurrence quantification analysis

In this paper, we used Recurrence Quantification Analysis (RQA) in order to study pre-epileptic characteristics in rats EEG recordings. Four adult rats were used to collect epileptic EEG data in an experiment of animal model of epilepsy. Three RQA measures, recurrence rate, determinism, and entropy were calculated from EEG recordings from rats. A moving average filter was used to identify the decreasing trend in pre-epileptic dynamics which will be useful early detection of seizures.

Textile sensor for monitoring vital signs

In this paper, we describe the development of a textile-based design of the microwave coupler for vital signs monitoring and measuring changes in lung water contents. We also describe preliminary results from human clinical study using the developed textile coupler. Benchmarking results using an FDA approved medical device, Propaq LT and a blood pressure cuff, have shown the feasibility of accurately detecting the heartbeat and respiration rates as well as stroke volume from a single microwave measurement. Some of the design and implementation issues such as the coaxial feed and termination of the microwave sensor, contact with human skin issues, as well as the DSP algorithm for extracting the vital signs are discussed. Comparative results between reflection (single coupler) vs. transmission (two sensors), are also presented and discussed.

A Heart Monitoring System for a Mobile Device

There have been major advances in research and development of devices for the diagnosis of patients in the medical field. A light and portable wireless system to monitor human physiological signals has been always a medical personnels dream. An e-health monitoring system is a widely used noninvasive diagnosis tool for an ambulatory patient who may be at risk from latent life threatening cardiac abnormalities. The authors proposed a high performance and intelligent wireless measuring e-health monitoring system for a mobile device that is characterized by the small sized and low power consumption. The hardware system consists of an one-chip microcontroller Atmega 128L, a wireless module, and electrocardigram ECG signal preprocessing including filtering, power noise canceling, and level shifting. The software utilizes a recursive filter and preprocessing algorithm to detect ECG signal parameters, i.e., QRS-complex, Q-R-T points, HR, and QT-interval. To easily interface with a mobile device, an analyzer program operates on a Windows mobile OS. This paper described the system that was developed and successfully tested for a wireless transmission of ECG signals to a mobile device.

Stroke volume estimation from the new noninvasive cardiopulmonary stethoscope

This paper presents advanced signal processing techniques and associated results for measuring the stroke volume using cardiopulmonary stethoscope (CPS), a new noninvasive multiple vital signs sensor. The CPS is a novel sensor based on RF measurements on patients chest. It can noninvasively and accurately measure changes in lung water (CLW), heart rate (HR), and respiration rate (RR) as reported in previous studies. CPS cardiac signal was correlated with arterial blood pressure measurements for studying the feasibility of estimating this important parameter noninvasively. Stroke volume measurement was computed using two methods, namely mean arterial blood pressure and area under the curve. Validation results by comparison with arterial blood pressure waveform are presented.