Norlaili Mat Safri

Wireless Medical Interface Using ZigBee and Bluetooth Technology

Since years, the using of telemedicine has increased although there are some technical issues related to standard and quality. In order to improve the telemedicine quality of service, a robust wireless connection between medical devices and telecommunication network is required. Unfortunately, many medical devices were developed without possibility to connect with telecommunication infrastructure. Due to this condition, a wireless medical interface has been developed. The interface has been implemented based on the advantages of ZigBee and Bluetooth technology. A new protocol has also been developed to enable robust communication between medical devices and telecommunication infrastructure. The interface has been tested to transfer the medical data from vital sign medical devices to a data processing system through wireless network. Measurement result shows that the interface is able to be used in transferring medical data from multi medical devices within range of 10 m with some interference sources.

Effects of concurrent visual tasks on cortico-muscular synchronization in humans

To study the effects of external visual stimulation on motor cortex-muscle synchronization, coherence between electroencephalography (EEG) and electromyography (EMG) was measured in normal subjects under Before, Task (visual task: Ignore or Count, or arithmetic task) and After conditions. The control (Before and After) conditions required the subject to maintain first dorsal interosseous muscle contraction without visual stimulation. In the visual task, a random series of visual stimuli were displayed on a screen while the subjects maintained the muscle contraction. The subjects were asked to ignore the stimuli in the Ignore condition and to count certain stimuli in the Count condition. Also, in the arithmetic task, the subjects were asked to perform a simple subtraction. The EEG-EMG coherence found at C(3) site at 13-30 Hz (beta) was increased and sustained in magnitude during the Ignore and Count conditions, respectively. To examine the cause of the change of coherence, changes of EEG and EMG spectral power were computed for each frequency band. There was little change in the EMG spectral power in any frequency bands. While the spectral power of EEG unchanged in the beta band, it significantly increased and decreased in the range of 8-12 Hz and of 31-50 Hz, respectively, for both Ignore and Count conditions, not only at the C(3) site but at various sites as well. These results were in contrast to those obtained for the arithmetic task: the beta band EEG-EMG coherence was attenuated and the EEG spectral power at 4-7 Hz and at 31-50 Hz were significantly increased and decreased, respectively. As a conclusion, the present results are consistent with the idea that the enhanced 8-12 Hz/decreased 31-50 Hz oscillations affect strength of the beta band cortico-muscular synchronization by suppressing the visual processing.

EEG different frequency sound response identification using neural network and fuzzy techniques

Electroencephalographic (EEG) technology has enabled effective measurement of human brain activity, as functional and physiological changes within the brain may be registered by EEG signals. In this paper, electrical activity of human brain due to sound waves of different frequency, i.e. 40 Hz, 500 Hz, 5000 Hz and 15000 Hz, is studied based on EEG signals. Several signal processing techniques, i.e. Principle Component algorithm, Discrete Wavelet Transform and Fast Fourier Transform, are applied onto the raw EEG signal to extract useful information and specific characteristics from the EEG signals. This research has shown that the characteristics of EEG signals differ with respect to different frequency of sound waves, and hence the EEG signal can be identified with suitable characterization algorithm using artificial intelligent techniques, such as Artificial neural network, fuzzy logic and adaptive neuro-fuzzy system.

Characterization of Ventricular Arrhythmias in Electrocardiogram Signal Using Semantic Mining Algorithm

Ventricular arrhythmias, especially ventricular fibrillation, is a type of arrhythmias that can cause sudden death. The paper applies semantic mining approach to electrocardiograph (ECG) signals in order to extract its significant characteristics (frequency, damping coefficient and input signal) to be used for classification purpose. Real data from an arrhythmia database are used after noise filtration. After features extraction they are statistically classified into three groups, i.e. normal (N), normal patients (PN) and patients with ventricular arrhythmia (V). We found that the V, PN, and N types of ECG signals can be identified by the extracted parameters. It is estimated that the parameters in semantic algorithm can be use to predict the onset of ventricular arrhythmias.

Dynamic ECG features for atrial fibrillation recognition

BACKGROUND Atrial fibrillation (AF) can cause the formation of blood clots in the heart. The clots may move to the brain and cause a stroke. Therefore, this study analyzed the ECG features of AF and normal sinus rhythm signals for AF recognition which were extracted by using a second-order dynamic system (SODS) concept. OBJECTIVE To find the appropriate windowing length for feature extraction based on SODS and to determine a machine learning method that could provide higher accuracy in recognizing AF. METHOD ECG features were extracted based on a dynamic system (DS) that uses a second-order differential equation to describe the short-term behavior of ECG signals according to the natural frequency (ω), damping coefficient, (ξ), and forcing input (u). The extracted features were windowed into 2, 3, 4, 6, 8, and 10 second episodes to find the appropriate windowing size for AF signal processing. ANOVA and t-tests were used to determine the significant features. In addition, pattern recognition machine learning methods (an artificial neural network (ANN) and a support vector machine (SVM)) with k-fold cross validation (k-CV) were used to develop the ECG recognition system. RESULTS Significant differences (p < 0.0001) were observed among all ECG groups (NSR, N, AF) using 2, 3, 4 and 6 second episodes for the features ω and u/ω; 4, 6 and 8 second episodes for features ω and u; 4 and 6 second episodes for features ω, u and u/ω, and; 10 second episodes for the feature ξ. The highest accuracy for AF recognition (AF, NSR) using ANN with k-CV was 95.3% using combination of features (ω and u; ω, u and u/ω) and SVM with k-CV was 95.0% using a combination of features ω, u and u/ω. CONCLUSION This study found that 4 s is the most appropriate windowing length, using two features (ω and u) for AF detection with an accuracy of 95.3%. Moreover, the pattern recognition learning machine uses an ANN with 10-fold cross validation based on DS.

Modelling of the Arabic Plosive Consonants Characteristics Based on Spectrogram

The aim of this study is to determine the place of articulation for Arabic phonemes by subjects on non-arabic spoken language namely Malay. Every phoneme must be pronounced accurately in order to obey the rule of tajweed of holy Quran. By using Fourier analysis technique, the sound waveform is transform into spectrum which is frequency representation of the signal. The spectrogram is used to determine the formant frequency. The spectrogram for each subject is observed to determine its formant frequency. This study shows that second and third formant frequency (F2 and F3) increased as the articulation is made towards into the mouth.

Effect of ECG episodes on parameters extraction for paroxysmal atrial fibrillation classification

Atrial fibrillation is a type of atria arrhythmia which can cause the formation of blood clot in the heart. The blood clot may enlarge or moving to the brain and cause stroke. Therefore, this study monitors the performance of ECG episodes for paroxysmal atrial fibrillation classification. Episode of 2 seconds to 8 seconds were used to observe the performance of electrocardiograph (ECG) signal processing of atrial fibrillation patient classification. Methods of features extraction were based on the concept of describing short-term behaviour of complex physical and biological system, namely second order system (SOS), and with modified algorithm (hybrid with fast-Fourier transform, FFT). Features extracted from the ECG signal of atrial fibrillation patient were defined using three parameters, i.e. natural frequency, forcing input and damping coefficient. A total of twelve parameters were observed. Comparisons of performance between length of ECG episodes were explored for SOS, FFT-SOS and SOS-FFT algorithms. The episode of 4 seconds using SOS algorithm provides the highest accuracy (98 %) during the classification of ECG signal.

Atrial fibrillation classification and association between the natural frequency and the autonomic nervous system

BACKGROUND The feasibility study of the natural frequency (ω) obtained from a second-order dynamic system applied to an ECG signal was discovered recently. The heart rate for different ECG signals generates different ω values. The heart rate variability (HRV) and autonomic nervous system (ANS) have an association to represent cardiovascular variations for each individual. This study further analyzed the ω for different ECG signals with HRV for atrial fibrillation classification. METHODS This study used the MIT-BIH Normal Sinus Rhythm (nsrdb) and MIT-BIH Atrial Fibrillation (afdb) databases for healthy human (NSR) and atrial fibrillation patient (N and AF) ECG signals, respectively. The extraction of features was based on the dynamic system concept to determine the ω of the ECG signals. There were 35,031 samples used for classification. RESULTS There were significant differences between the N & NSR, N & AF, and NSR & AF groups as determined by the statistical t-test (p<0.0001). There was a linear separation at 0.4s(-1) for ω of both databases upon using the thresholding method. The feature ω for afdb and nsrdb falls within the high frequency (HF) and above the HF band, respectively. The feature classification between the nsrdb and afdb ECG signals was 96.53% accurate. CONCLUSIONS This study found that features of the ω of atrial fibrillation patients and healthy humans were associated with the frequency analysis of the ANS during parasympathetic activity. The feature ω is significant for different databases, and the classification between afdb and nsrdb was determined.

ECG features extraction using second-order dynamic system and regeneration using hybrid recurrent network

ECG signals show the hearts condition for each individual. ECG signals characteristic can be extracted by using several methods such as P-wave conditions, RR-interval, fast-Fourier transform, wavelet transform, and etc. This study shows the relationship between features extraction of ECG signals by using second-order dynamic system (SODS) technique and ECG signals regeneration by using hybrid-recurrent network (HRN). HRN technique describes the mathematical proof of the algorithms used in SODS. The algorithm was developed by using Matlab software platform. Comparison was made and it was found that the ECG features extracted from SODS can be used to regenerate the ECG signals based on HRN technique. Therefore, the features extracted from SODS were valid to be used for further analysis of ECG signals.

Dynamic features of handwriting and cortico-cortical functional connectivity during basic geometric drawing based on gender

The aim of the study is to investigate the dynamic features of handwriting and the directional connectivity in brain among young children during basic drawing task. Seven children participated in the study where four of them were female. To exercise motor ability, three different unlined shapes were selected which the subject must gaze and trace on WACOM digitizing tablet. While doing the basic drawing task, brain signals (EEG) were recorded to analyze the information pathway based on partial directed coherence (PDC) method. Result showed that all subjects regardless of gender performed the basic drawing task with preferred rule. Again, regardless of gender, PDC showed that most information sources came from parietal, frontal and occipital areas even-though dynamic features of handwriting (pressure and altitude) showed gender preferences. It is found also that gazing while planning for tracing and actually doing the tracing activity shows almost similar result, i.e. similar sources of information. Based from the pattern of information pathway in the brain among the subjects during gazing, the tracing activity is thought to be well planned. Most of the subjects make use of areas where visual processing, pattern recognition, motor planning and perception midline and route finding are executed during the performances.