Luay Fraiwan

Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier

In this work, an efficient automated new approach for sleep stage identification based on the new standard of the American academy of sleep medicine (AASM) is presented. The propose approach employs time-frequency analysis and entropy measures for feature extraction from a single electroencephalograph (EEG) channel. Three time-frequency techniques were deployed for the analysis of the EEG signal: Choi-Williams distribution (CWD), continuous wavelet transform (CWT), and Hilbert-Huang Transform (HHT). Polysomnographic recordings from sixteen subjects were used in this study and features were extracted from the time-frequency representation of the EEG signal using Renyis entropy. The classification of the extracted features was done using random forest classifier. The performance of the new approach was tested by evaluating the accuracy and the kappa coefficient for the three time-frequency distributions: CWD, CWT, and HHT. The CWT time-frequency distribution outperformed the other two distributions and showed excellent performance with an accuracy of 0.83 and a kappa coefficient of 0.76.

Classification of Sleep Stages Using Multi-wavelet Time Frequency Entropy and LDA

BACKGROUND The process of automatic sleep stage scoring consists of two major parts: feature extraction and classification. Features are normally extracted from the polysomnographic recordings, mainly electroencephalograph (EEG) signals. The EEG is considered a non-stationary signal which increases the complexity of the detection of different waves in it. OBJECTIVES This work presents a new technique for automatic sleep stage scoring based on employing continuous wavelet transform (CWT) and linear discriminant analysis (LDA) using different mother wavelets to detect different waves embedded in the EEG signal. METHODS The use of different mother wavelets increases the ability to detect waves in the EEG signal. The extracted features were formed based on CWT time frequency entropy using three mother wavelets, and the classification was performed using the linear discriminant analysis. Thirty-two data sets from the MIT-BIH database were used to evaluate the performance of the proposed method. RESULTS Features of a single EEG signal were extracted successfully based on the time frequency entropy using the continuous wavelet transform with three mother wavelets. The proposed method has shown to outperform the classification based on a CWT using a single mother wavelet. The accuracy was found to be 0.84, while the kappa coefficient was 0.78. CONCLUSIONS This work has shown that wavelet time frequency entropy provides a powerful tool for feature extraction for the non-stationary EEG signal; the accuracy of the classification procedure improved when using multiple wavelets compared to the use of single wavelet time frequency entropy.

A wireless home safety gas leakage detection system

A wireless safety device for gas leakage detection is proposed. The device is intended for use in household safety where appliances and heaters that use natural gas and liquid petroleum gas (LPG) may be a source of risk. The system also can be used for other applications in the industry or plants that depend on LPG and natural gas in their operations. The system design consists of two main modules: the detection and transmission module, and the receiving module. The detection and transmitting module detects the change of gas concentration using a special sensing circuit built for this purpose. This module checks if a change in concentration of gas(es) has exceeded a certain pre-determined threshold. If the sensor detects a change in gas concentration, it activates and audiovisual alarm and sends a signal to the receiver module. The receiver module acts as a mobile alarm device to allow the mobility within the house premises. The system was tested using LPG and the alarm was activated as a result of change in concentration.

Voiceless Arabic vowels recognition using facial EMG

This work attempts to recognize the Arabic vowels based on facial electromyograph (EMG) signals, to be used for people with speech impairment and for human computer interface. Vowels were selected since they are the most difficult letters to recognize by people in Arabic language. Twenty subjects (7 females and 13 males) were asked to pronounce three Arabic vowels continuously in a random order. Facial EMG signals were recorded over three channels from the three main facial muscles that are responsible for speech. The EMG signals are then pre-processed to eliminate noise and interference signals. Segmentation procedure was implemented to extract the time event that corresponds to each vowel based on a moving standard deviation window. The accuracy of the segmentation procedure was found to be 94%. The recognition of the vowels was carried out by extracting features from the EMG in three domains: the temporal, the spectral, and the time frequency using the wavelet packet transform. Classification of the extracted features was then finally performed using different classification methods implemented in the WEKA software. The random forest classifier with time frequency features showed the best performance with an accuracy of 77% evaluated using a 10-fold cross-validation.

ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM FOR AUTOMATIC SLEEP MULTISTAGE LEVEL SCORING EMPLOYING EEG, EOG, AND EMG EXTRACTED FEATURES

A new system for sleep multistage level scoring by employing extracted features from twenty five polysomnographic recording is presented. For the new system, an adaptive neuro-fuzzy inference system (ANFIS) is developed for each sleep stage. Initially, three types of electrophysiological signals including electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) were collected from twenty five healthy subjects. The input pattern used for training the ANFIS subsystem is a set of extracted features based on the entropy measure which characterize the recorded signals. Finally an output selection subsystem is utilized to provide the appropriate sleep stage according to the ANFIS stage subsystems outputs. The developed system was able to provide an acceptable estimation for six sleep stages with an average accuracy of about 76.43% which confirmed its ability for multistage sleep level scoring based on the extracted features from the EEG, EOG and EMG signals compared to other approaches.

Real time virtual prosthetic hand controlled using EMG signals

Patients with defects in their limbs use prosthetic devices that require a great mental and physical effort during early stages of training where many patients gave up the use of these prosthetics due to the difficulties during training. This work presents a start point for training patient on using prosthetic devices using virtual reality prosthesis. The proposed system consists mainly of an electromyography (EMG) system connected to the patient arm (biceps and triceps muscles) and interfaced with a PC using a data acquisition system. The PC uses Matlab to enhance the EMG signals and detect the presence of events in them. These events are used to control a virtual hand with two movements; grasping and wrist rotation. The system was tested on a subject who performed the grasping and wrist rotation for 90 trials. The overall success rate was found to be 84%.

ECG-based wireless home infant apnoea monitor

A safe and wireless system for infant apnoea monitoring and treatment is proposed. It consists of two separate modules that detect apnoea based on a microcontroller processing and transmitting signals to the remote unit using wireless communication arrangement. Depending on the ECG signal, this system records the infants heart and respiratory rates. Recognition of the consecutive QRS complexes within the ECG signal helps in calculating these vitals. Abnormal breathing rate or heart rate (apnoea) triggers both an alarm system on the remote unit to inform parents of this abnormality and a treatment device that stimulates the infant to breathe using vibration transducer. Different ECG signals from the MIT-Physionet database were used to test the prototype. The system successfully extracted, analysed and transmitted required vitals and decisions to the receiving unit. Finally, the system was bench tested on 25 adult volunteers and 12 young children; voluntarily cessations of breathing were detected successfully for all subjects.

Parkinson's disease hand tremor detection system for mobile application.

Abstract Parkinson’s disease currently affects millions of people worldwide and is steadily increasing. Many symptoms are associated with this disease, including rest tremor, bradykinesia, stiffness or rigidity of the extremities and postural instability. No cure is currently available for Parkinson’s disease patients; instead most medications are for treatment of symptoms. This treatment depends on the quantification of these symptoms such as hand tremor. This work proposes a new system for mobile phone applications. The system is based on measuring the acceleration from the Parkinson’s disease patient’s hand using a mobile cell phone accelerometer. Recordings from 21 Parkinson’s disease patients and 21 healthy subjects were used. These recordings were analysed using a two level wavelet packet analysis and features were extracted forming a feature vector of 12 elements. The features extracted from the 42 subjects were classified using a neural networks classifier. The results obtained showed an accuracy of 95% and a Kappa coefficient of 90%. These results indicate that a cell phone accelerometer can accurately detect and record rest tremor in Parkinson’s disease patients.

Medical waste management practices in southern Jordan

The purpose of this work was to study medical waste practices in southern Jordan. Five major hospitals in that area were used to conduct this study. It involved distributing a modified version of the questionnaire that was adopted by the World Health Organization (WHO). The surveyed hospitals had total number of beds ranging from 72 to 131 beds, while the average amount of medical waste generated in these hospitals was 0.73 kg bed−1 day–1. The practices of medical waste management were found to be suffering from major defects owing to the lack of both training and proper education.

Noninvasive transdermal insulin delivery using ultrasound transducers

Noninvasive transdermal delivery of insulin is suggested in this paper using ultrasound transducers to improve the quality of life of diabetic patients. It is a preferable technique over the traditional invasive and painful subcutaneous insulin injections. Ten piston-shaped ultrasound transducers operating in the mid frequency range (100–200 kHz) were housed to include a reservoir that intended to hold insulin during in vivo transdermal delivery. Eleven rabbits were divided into two groups. The control group (n=6) did not receive ultrasound while the exposure group (n=5) received ultrasound for only ten minutes. Over the recording period of 60 minutes, blood Glucose levels in the control group remained around 125 mg/dl; while for the exposure group, it reduced from 132.4 mg/dl at the beginning of the experiment to 87.6 mg/dl after 60 minutes. Piston transducers in the mid range frequency were found feasible in transdermal insulin delivery in vivo using local rabbits. More investigations are required to test more frequency ranges with different hyperglycemic rabbit models.