Sheikh Shanawaz Mostafa

Human emotion recognition using frequency & statistical measures of EEG signal

The purpose of the research is to evaluate the different human emotions through Electroencephalogram (EEG) signal and to receive information about the internal changes of brain state. The paper presents the detection of human emotion based on some salient features of EEG signal. For this purpose, seven emotional states have been specified such as relax, thought, memory related, motor action, pleasant, fear, and enjoying music. Several EEG signals have been collected for these states and analyzed using frequency transform and statistical measures. Different significant features have been extracted from the analyzed signal. Among various statistical measures skewness and kurtosis are chosen which indicate the largest dispersion in different mental states and help to evaluate different human emotions. Frequency analysis shows how the ranges of magnitude vary with different frequency components. On the basis of magnitude ranges different emotional states are identified. EEG signal provides an effective way in the functioning of the brain to study of mental behavior.

Mental states estimation with the variation of physiological signals

In this paper, we describe the consequences of mental conditions due to the variation of electrocardiogram, electroencephalogram and blood pressure using BIOPAC system. The different data sets are collected using BIOPAC system in which subjects were induced to undergo the specific sequence of mental condition or cognitive state. For getting physiological signals during different affective condition, we utilized power point slide show, video clips and question-answer method which elicit mental reactions from the subjects. Data was taken before and after four tasks that encompassed the Motor Action (MA), Thought (TH), Memory Related (MR), Emotion (EM). These measured values were analyzed using BIOPAC AcqKnowledge software. It was found that the effect of motor action and thought states are mainly on blood pressure while memory related and emotion state mainly affect the ECG measurement. The EEG mainly detects the signal of task performed by the specific brain region where the electrodes are placed. Here the electrodes are placed in occipital lobe region which gives mainly the variation in alpha amplitude of EEG with eyes closed and eyes opened. Alpha wave amplitudes vary with the subjects attention to mental tasks performed with eyes closed.

Clench force estimation by surface electromyography for neural prosthesis hand

Clench force estimator is highly desirable in the field of prosthesis hand. It is one of the most used postures among five types of postures. In this paper, we propose to estimate the clench force using two types of Surface Electromyography (SEMG). The SMEG consists of rectified SEMG and integrated SEMG. A two layered artificial neural network (ANN) is used as an estimator to map the SEMG for estimating force. For weight adjustment of the estimator Levenberg-Marquardt (L-M) back propagation algorithm is used. The proposed network is trained and tested using SEMG recorded from five subjects. The estimation result clearly show that integrated SEMG performed 3.53 times better than rectified SEMG in the case of cross correlation coefficient and hence integrated SEMG is recommended for clench force estimation.

A portable wireless device based on oximetry for sleep apnea detection

Obstructive sleep apnea is a highly prevalent sleep related breathing disorder and polysomnography is the gold standard exam for diagnosis. Despite providing results with high accuracy this multi-parametric test is expensive, time consuming and does not fit with the new tendency in health care that is changing the focus to prevention and wellness. Home health care is seen as a possible way to address this problematic by using minimal invasive devices, providing low cost of diagnosis and higher accessibility. To address this, a portable and automated sleep apnea detector was designed and evaluated. The device uses one SpO2 sensor and the analysis is based on the connection between oxygen saturation and apnea events. The measured signals are received in a field-programmable gate array that checks for errors and implements the communication protocols of two wireless transmitters. Two solutions were implemented for processing the data: one based on a smartphone (due to availability and low cost) and another based on a personal computer (for a higher computation capability). The algorithms were implemented in Java, for the smartphone, and in Python, for the computer. Both implementations have a graphical user interface to simplify the device operation. The algorithms were tested using a database consisting of 70 patients with the SpO2 signal collected in a Hospital. The algorithm performance achieved an average accuracy, sensitivity and specificity of 87.5, 79.5 and 90.8% respectively.

Automatic Detection of a Phases for CAP Classification.

The aim of this study is to develop an automatic detector of the cyclic alternating pattern by first detecting the activation phases (A phases) of this pattern, analysing the electroencephalogram during sleep, and then applying a finite state machine to implement the final classification. A public database was used to test the algorithms and a total of eleven features were analysed. Sequential feature selection was employed to select the most relevant features and a post processing procedure was used for further improvement of the classification. The classification of the A phases was produced using linear discriminant analysis and the average accuracy, sensitivity and specificity was, respectively, 75%, 78% and 74%. The cyclic alternating pattern detection accuracy was 75%. When comparing with the state of the art, the proposed method achieved the highest sensitivity but a lower accuracy since the fallowed approach was to keep the REM periods, contrary to the method that is used in the majority of the state of the art publications which leads to an increase in the overall performance. However, the approach of this work is more suitable for automatic system implementation since no alteration of the EEG data is needed.

A Method for Designing Emg Integrator using an FPGA

Electromyography is a method for recording electrical activities of the muscle for different clinical and nonclinical tasks. For extracting more information, integrated electromyography is commonly used than the raw electromyography. This paper presents the design and implementation of integrated electromyography both in software and hardware. Software was implemented in Matlab due to easier implementation whereas hardware was designed on Field Programmable Gate Array (FPGA) due to low cost and flexibility. It can be seen that, the integrator works like a moving average window filter and a hundred-point window size is chosen in the integrator design. To verify the method, real surface electromyography data was collected and used. The mean error between software (Matlab) and hardware is 5.8288e-08 and the correlation coefficient is 1.

Devices for home detection of obstructive sleep apnea: A review

One of the most common sleep-related disorders is obstructive sleep apnea, characterized by a reduction of airflow while breathing during sleep and cause significant health problems. This disorder is mainly diagnosed in sleep labs with polysomnography, involving high costs and stress for the patient. To address this situation multiple systems have been proposed to conduct the examination and analysis in the patients home, using sensors to detect physiological signals that are examined by algorithms. The objective of this research is to review publications that show the performance of different devices for ambulatory diagnosis of sleep apnea. Commercial systems that were examined by an independent research group and validated research projects were selected. In total 117 articles were analysed, including a total of 50 commercial devices. Each article was evaluated according to diagnostic elements, level of automatisation implemented and the deducted level of evidence and quality rating. Each device was categorized using the SCOPER categorization system, including an additional proposed category, and a final comparison was performed to determine the sensors that provided the best results.

Automatic detection of cyclic alternating pattern

The cyclic alternating pattern is a microstructure phasic event, present in the non-rapid eye movement sleep, which has been associated with multiple pathologies, and is a marker of sleep instability that is detected using the electroencephalogram. However, this technique produces a large quantity of information during a full night test, making the task of manually scoring all the cyclic alternating pattern cycles unpractical, with a high probability of miss classification. Therefore, the aim of this work is to develop and test multiple algorithms capable of automatically detecting the cyclic alternating pattern. The employed method first analyses the electroencephalogram signal to extract features that are used as inputs to a classifier that detects the activation (A phase) and quiescent (B phase) phases of this pattern. The output of the classifier was then applied to a finite state machine implementing the cyclic alternating pattern classification. A systematic review was performed to determine the features and classifiers that could be more relevant. Nine classifiers were tested using features selected by a sequential feature selection algorithm and features produced by principal component analysis. The best performance was achieved using a feed-forward neural network, producing, respectively, an average accuracy, sensitivity, specificity and area under the curve of 79, 76, 80% and 0.77 in the A and B phases classification. The cyclic alternating pattern detection accuracy, using the finite state machine, was of 79%.

Comparison of SFS and mRMR for oximetry feature selection in obstructive sleep apnea detection

Obstructive sleep apnea is a disorder characterized by pauses in respiration during sleep. Due to this disturbance in breathing, there is a decrease in the oxygen saturation (SpO2) level. Thus, SpO2 can be used as a source of information for the automatic detection of apnea. Several solutions exist in the literature where different features are used. To find a better discriminant capacity, a subset of few features that obtains higher accuracy with the proper classifier is needed. To face this challenge, this work compares two different feature selection methods. The first one is a filter method named minimum redundancy maximum relevance, and the other one is called sequential forward search. These methods are tested with different classifiers. Two public datasets with 8 and 25 subjects are used to test and compare the performances of the different feature selection methods. A set of features for each classifier is obtained, and the results are compared with the previous work. The results found in this work show a good performance with respect to the state of the art and present a good option for apnea screening with low resources.

Design of sEMG-based clench force estimator in FPGA using artificial neural networks

Hands are the main environmental manipulator for the human being. After losing a hand, the only alternative for the victim is to use a prosthesis. Despite the progress of science, the modern prosthesis has the same age-old problem of accurate force estimation. Among different kinds of force, clench force is the most important one. Because of this importance, this paper presents a hardware system that has been designed and implemented to estimate the desired clench force using surface Electromyography signals recorded from lower-arm muscles. The implementation includes a two-layer artificial neural network with a surface electromyography integrator. The neural network was trained with the Levenberg–Marquardt back propagation algorithm and was implemented in a field programmable gate array using an off-chip training method. The results from 10 datasets, recorded from five subjects, show that the hardware model is very accurate, with an average mean square error of 0.003. This suggests that the proposed design can mimic the behavior of clench force that a real limb does, and therefore this intelligent system could be a useful tool for any application related to prostheses.