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Artificial Neural Network has been widely applied for solving pattern recognition problems including infant cry classification for detecting infant health and physical status. Feature extraction is usually performed using Mel Frequency Cepstrum Coefficient (MFCC) analysis. If irrelevant features in the MFCC are not removed, the performance of the MLP will be degraded. The use of F-ratio is essential...
In this paper we implement an automatic procedure that is to be embedded in a wearable system in order to discriminate five arrhythmic classes of QRS complexes from normal ones. Due to the limited hardware resources offered by the wearable system, several requirements such as low computational cost, memory usage, reliability and real-time have to be addressed. In order to better comply with these...
The skill of cardiac auscultatory is very important to physicians for accurate diagnosis of many heart diseases. However, it needs some training and experience to improve the skills of medical students in recognizing and distinguishing the primary symptoms of cardiac diseases based on the heart sound that heard. This paper presents a method for feature extraction and classification of heart sound...
Hypothyroidism in infants is caused by insufficient production of hormones by the thyroid gland. Due to stress in the chest cavity as a result of the enlarged liver, their cry signals are unique and can be distinguished from healthy infant cries. Our work investigates the effectiveness of using Multilayer Perceptron classifier to detect infant hypothyroidism. The Mel Frequency Cepstrum coefficients...
This paper presents a new application of the Particle Swarm Optimization (PSO) algorithm to optimize Mel Frequency Cepstrum Coefficients (MFCC) parameters, in order to extract an optimal feature set for diagnosis of hypothyroidism in infants using Multi-Layer Perceptrons (MLP) neural network. MFCC features is influenced by the number of filter banks (fb) and the number of coefficients (nc) used. These...
A new artificial Larynx is currently under development at the University of the Witwatersrand, Johannesburg. This device uses dynamic tongue movement from a palatometer system to infer what the user is trying to say. Feature selection algorithms extract information from the palatometer data and are then used as input to a Multi-Layer Perceptron Neural Network. This paper deals with improving the success...
Hypothyroidism occurs in infants with insufficient production of hormones by the thyroid gland. The cry signals of babies with hypothyroidism have distinct patterns which can be recognized with pattern classifiers such as Multilayer Perceptron (MLP) artificial neural network. This study investigates the performance of the MLP in discriminating between healthy infants and infants suffering from hypothyroidism...
Laryngeal diseases affect many professionals who use their voices as the main working tool, such as teachers, singers, radio and TV presenters, among others. Advanced diagnosis techniques of these diseases are typically invasive, causing much discomfort to the patient. In recent years techniques of digital voice processing have been investigated to obtain non-invasive systems to aid the diagnosis...
A new signal processing scheme is presented for extracting neural control information from the multi-channel surface electromyographic signal (sEMG). The extracted information can be used to proportionally control a multi-degree of freedom (DOF) prosthesis. Four time-domain (TD) features were extracted from the multi-channel sEMG during a series of anisotonic, isometric wrist contractions, which involved...
Murmurs are auscultatory sounds produced by turbulent blood flow in and around the heart. These sounds usually signify an underlying cardiac pathology, which may include diseased valves or an abnormal passage of blood flow. The murmurs are classified based on their occurrence in different parts of the heart cycle; systolic murmurs and diastolic murmurs. This paper investigates features derived from...
We report that combining the interbeat heart rate as measured by the RR interval (RR) and R-peak envelope (RPE) derived from R-peak of ECG waveform may significantly improve the detection of sleep disordered breathing (SDB) from single lead ECG recording. The method uses textural features extracted from normalized gray-level cooccurrence matrices of the time frequency plots of HRV or RPE sequences...
In this study, developing of a different model estimating of alertness level has been studied by using electroencephalogram (EEG) signals recorded during transition from wakefulness to sleep. Developed model is composed of discrete wavelet transform-entropy pair (feature extractor) and multilayer perceptron neural network (classifier). This study, basically, comprises of two stages. In the first stage,...
The aim of this work is to develop a new method for automatic detection and classification of EEG patterns using continuous wavelet transforms (CWT) and artificial neural networks (ANN). Our method consists of EEG data selection, feature extraction and classification stage. For the data selection we use temporal lobe seizures for evaluation recorded from patients during 84 hours at hospital. In feature...
Brain-computer interface (BCI) is a new and promising area of research which is assumed to help in solving a lot of problems especially for handicapped people. Detection of the imagination of the left and right hand movements can be used to control a wheelchair accordingly. Fortunately, modification of the brain activity caused by the imagination of the left or right hand movements is similar to the...
Paroxysmal atrial fibrillation, a really life threatening disease, is the result of irregular and repeated depolarization of the atria. In this paper, an automatic detection and prediction of this critical disease is performed by the use of three groups of features extracted from different parts of ECG signals and classified by KNN, MLP and Bayes optimal classifiers. Finally, the health status of...
This study presents a new method for epilepsy detection based on autoregressive (AR) estimation of EEG signals. In this method, optimum order for AR model is determined by Bayesian Information Criterion (BIC) and then AR parameters of EEG signals (from EEG data set of epilepsy center of the University of Bonn, Germany) and their sub-bands (created with the help of wavelet decomposition) are extracted...
A heart sound feature extraction and classification method has been developed. It used the discrete wavelet decomposition and reconstruction to produce the envelopes of details of the signals for further extracting the features. Some statistical variables were extracted from the processed signals and used as the features for the heart sounds classification. A Multilayer Perceptron Neural Network has...
This paper presents an approach based on the combination of multilayer perceptrons (MLP) and classification tree (CT) to recognising four electrocardiograms (ECG) patterns: normal, left bundle branch block (LBBB), right bundle branch block (RBBB) and premature ventricular contraction (PVC). This study utilises MIT/BIH arrhythmia database as training and testing data. We first apply MLP and CT respectively...
In this study we propose a new approach to analyze data from the P300 speller paradigm using the quadratic B-spline wavelet coefficients and committee machines technique. Data set II from the BCI competition 2005 were used. The data were decomposed into five-octave frequency bands. We used coefficients between 4-8 Hz (theta) and 0.5-4 Hz (delta) frequency ranges as features. Extracted features used...
Unlocking the neural code of the human's brain has long been the main focus of neuroscience studies with intensive research carried out to model the brain. Some of these techniques use the magnetic resonance imaging (MRI) and electroencephalographic (EEG) systems. The EEG signals estimate the cortical activities using non-invasive Brain Computer Interfaces (BCI) with scalp potential measurements targeted...
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