Amjed S. Al-Fahoum

Quality assessment of ECG compression techniques using a wavelet-based diagnostic measure

Electrocardiograph (ECG) compression techniques are gaining momentum due to the huge database requirements and wide band communication channels needed to maintain high quality ECG transmission. Advances in computer software and hardware enable the birth of new techniques in ECG compression, aiming at high compression rates. In general, most of the introduced ECG compression techniques depend on their evaluation performance on either inaccurate measures or measures targeting random behavior of error. In this paper, a new wavelet-based quality measure is proposed. A new wavelet-based quality measure is proposed. The new approach is based on decomposing the segment of interest into frequency bands where a weighted score is given to the band depending on its dynamic range and its diagnostic significance. A performance evaluation of the measure is conducted quantitatively and qualitatively. Comparative results with existing quality measures show that the new measure is insensitive to error variation, is accurate, and correlates very well with subjective tests

Methods of EEG Signal Features Extraction Using Linear Analysis in Frequency and Time-Frequency Domains

Technically, a feature represents a distinguishing property, a recognizable measurement, and a functional component obtained from a section of a pattern. Extracted features are meant to minimize the loss of important information embedded in the signal. In addition, they also simplify the amount of resources needed to describe a huge set of data accurately. This is necessary to minimize the complexity of implementation, to reduce the cost of information processing, and to cancel the potential need to compress the information. More recently, a variety of methods have been widely used to extract the features from EEG signals, among these methods are time frequency distributions (TFD), fast fourier transform (FFT), eigenvector methods (EM), wavelet transform (WT), and auto regressive method (ARM), and so on. In general, the analysis of EEG signal has been the subject of several studies, because of its ability to yield an objective mode of recording brain stimulation which is widely used in brain-computer interface researches with application in medical diagnosis and rehabilitation engineering. The purposes of this paper, therefore, shall be discussing some conventional methods of EEG feature extraction methods, comparing their performances for specific task, and finally, recommending the most suitable method for feature extraction based on performance.

A Smart Infrared Microcontroller-Based Blind Guidance System

Blindness is a state of lacking the visual perception due to physiological or neurological factors. The partial blindness represents the lack of integration in the growth of the optic nerve or visual centre of the eye, and total blindness is the full absence of the visual light perception. In this work, a simple, cheap, friendly user, smart blind guidance system is designed and implemented to improve the mobility of both blind and visually impaired people in a specific area. The proposed work includes a wearable equipment consists of head hat and mini hand stick to help the blind person to navigate alone safely and to avoid any obstacles that may be encountered, whether fixed or mobile, to prevent any possible accident. The main component of this system is the infrared sensor which is used to scan a predetermined area around blind by emitting-reflecting waves. The reflected signals received from the barrier objects are used as inputs to PIC microcontroller. The microcontroller is then used to determine the direction and distance of the objects around the blind. It also controls the peripheral components that alert the user about obstacles shape, material, and direction. The implemented system is cheap, fast, and easy to use and an innovative affordable solution to blind and visually impaired people in third world countries.

Perceptually tuned JPEG coder for echocardiac image compression

In this work, we propose an efficient framework for compressing and displaying medical images. Image compression for medical applications, due to available Digital Imaging and Communications in Medicine requirements, is limited to the standard discrete cosine transform-based joint picture expert group. The objective of this work is to develop a set of quantization tables (Q tables) for compression of a specific class of medical image sequences, namely echocardiac. The main issue of concern is to achieve a Q table that matches the specific application and can linearly change the compression rate by adjusting the gain factor. This goal is achieved by considering the region of interest, optimum bit allocation, human visual system constraint, and optimum coding technique. These parameters are jointly optimized to design a Q table that works robustly for a category of medical images. Application of this approach to echocardiac images shows high subjective and quantitative performance. The proposed approach exhibits objectively a 2.16-dB improvement in the peak signal-to-noise ratio and subjectively 25% improvement over the most useable compression techniques.

A practical reconstructed phase space approach for ECG arrhythmias classification

Abstract Atrial and ventricular arrhythmias are symptoms of the main common causes of rapid death. The severity of these arrhythmias depends on their occurrence either within the atria or ventricles. These abnormalities of the heart activity may cause an immediate death or cause damage of the heart. In this paper, a new algorithm is proposed for the classification of life threatening cardiac arrhythmias including atrial fibrillation (AF), ventricular tachycardia (VT) and ventricular fibrillation (VF). The proposed technique uses a simple signal processing technique for analysing the non-linear dynamics of the ECG signals in the time domain. The classification algorithm is based upon the distribution of the attractor in the reconstructed phase space (RPS). The behaviour of the ECG signal in the reconstructed phase space is used to determine the classification features of the whole classifier. It is found that different arrhythmias occupy different regions in the reconstructed phase space. Three regions in the RPS are found to be more representative of the considered arrhythmias. Therefore, only three simple features are extracted to be used as classification parameters. To evaluate the performance of the presented classification algorithm, real datasets are obtained from the MIT database. A learning dataset is used to design the classification algorithm and a testing dataset is used to verify the algorithm. The algorithm is designed to guarantee achieving both 100% sensitivity and 100% specificity. The classification algorithm is validated by using 45 ECG signals spanning the considered life threatening arrhythmias. The obtained results show that the classification algorithm attains a sensitivity ranging from 85.7–100%, a specificity ranging from 86.7–100% and an overall accuracy of 95.55%.

Combined Bispectral and Bicoherency approach for Catastrophic Arrhythmia Classification

Quantitative classification of cardiac arrhythmia is an important tool in ICU and CCU that enables on line monitoring of the cardiac activities. Among fatal arrhythmias are atrial fibrillation (AF), ventricular tachycardia (VT), and ventricular fibrillation that require special algorithms for detection and so for direct medical actions. In this paper, a combined bispectrum and bicoherency classification algorithm is introduced. It is based on extracting diagnostic features from the bispectrum contours and the bicoherency indices that better describe the arrhythmia. A simple classification scheme utilizing these features showed notable sensitivity and specificity. The obtained results are found comparable to the state of the art algorithms with the ability of being integrated for on line monitoring and classification

A new developed educational approach to improve conventional teaching methodology of the power electronics laboratory

This paper proposes a new computerized educational approach to teach the power electronics laboratory. It describes PSpice implementation of the core power electronic circuits that depend on thyristor circuits to identify behaviors with load variations. It uses the developed simulation models to support and enhance power electronics education at the undergraduate level. These simulations successfully integrate the contents of the power electronic laboratory course. A study of the impact of these simulations on the results of the students showed that it helped them to master the course contents and to gain better grades.

Detection of cardiac ischaemia using bispectral analysis approach

Abstract This paper highlights a new detection method based on higher spectral analysis techniques to distinguish the Electrocardiogram (ECG) of normal healthy subjects from that with a cardiac ischaemia (CI) patient. Higher spectral analysis techniques provide in-depth information other than available conventional spectral analysis techniques usually used with ECG analysis. They provide information within frequency parts and information regarding phase associations. Bispectral analysis- Bispectrum and Quadratic Phase Coupling techniques are utilized to detect as well as to characterize phase combined harmonics in ECG. The work is developed, tested and validated using Normal Sinus Rhythm Data from the MIT-BIH Database and CI data from the ST Petersburg European ST-T Database. The results validate the efficacy of the introduced method by maintaining 100% sensitivity and achieving 93.33% positive predictive accuracy. The simplicity and robustness of the proposed method makes it feasible to be used within available ECG systems.

A new quantitative analysis technique for cardiac arrhythmia using bispectrum and bicoherency

Ventricular tachyarrhythmias, in particular ventricular fibrillation (VF), are the primary arrhythmic events in the majority of patients suffering from sudden cardiac death. Attention has focused upon these articular rhythms as it is recognized that prompt therapy can lead to a successful outcome. There has been considerable interest in analysis of the surface electrocardiogram (ECG) in VF centred on attempts to understand the pathophysiological processes occurring in sudden cardiac death, predicting the efficacy of therapy, and guiding the use of alternative or adjunct therapies to improve resuscitation success rates. Atrial fibrillation (AF) and ventricular tachycardia (VT) are other types of tachyarrhythmias that constitute a medical challenge. In this paper, a high order spectral analysis technique is suggested for quantitative analysis and classification of cardiac arrhythmias. The algorithm is based upon bispectral analysis techniques. The bispectrum is estimated using an AR model, and the frequency support of the bispectrum is extracted as a quantitative measure to classify atrial and ventricular tachyarrhythmias. Results show a significant difference in the parameter values for different arrhythmias. Moreover, the bicoherency spectrum shows different bicoherency values for normal and tachycardia patients. In particular, the bicoherency indicates that phase coupling decreases as arrhythmia kicks in.

Design of fuel control system using fuzzy logic for a pre-designed radial gas turbine driving directly high-speed permanent magnet alternator

Abstract Radial gas turbine of 50-kW power output coupled directly to a high-speed permanent magnet alternator could be a favourable option as an emergency power plant at areas suffering from severe disasters, such as earthquakes, floods and volcanoes. This study aims to use the results of the subtractive clustering algorithm and the least square estimation method to generate a fuzzy model of the pre-designed radial gas turbine system whereby the fuzzy model takes the fuel mass flow rate as an input, and gives the value of the gas turbine net work Wnet as an output. In addition, a suitable controller of the fuel mass flow rate is designed and analysed so that the speed of the gas turbine and the alternator is maintained at 42,000 rpm. A proportional derivative fuzzy controller was built and tested. Results illustrate that the proposed controller achieves the desired performance and stability, and showed the effectiveness of the approach. Conclusions of this study will constitute a base for further studies that could be made to enhance the performance of the proposed emergency power plant system.