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Computer-aided fetal monitoring is based on automated analysis of the fetal heart rate (FHR) variability. The first and the main step in the automated signal interpretation is the estimation of the so called FHR baseline. There are various algorithms for baseline estimation, of different efficiency. For its evaluation, the method of modeling of FHR signal based on the preset baseline component has...
This paper presents a real-time electro-encephalogram (EEG) identification system with the goal of achieving hands free control. With two EEG electrodes placed on the scalp of the user, EEG signals are amplified and digitised directly using a ProComp+ encoder and transferred to the host computer through the RS232 interface. Using a real-time multilayer neural network, the actual classification for...
In this paper, we build a mathematical model of the whole-body neuromuscular network and identify its parameters by optical motion capture, inverse dynamics computation, and statistical analysis. The model includes a skeleton, a musculotendon network, and a neuromuscular network. The skeleton is composed of 155 joints representing the inertial property and mobility of the human body. The musculotendon...
We present an architecture of an epileptic seizure prediction system suitable for an implantable implementation. The microsystem comprises a neural interface, a spectral analysis processor and an artificial neural network (ANN). The neural interface and the spectral analysis processor have been prototyped in a 0.35 mum CMOS technology with experimental results are presented. The wavelet-based artificial...
In this paper, autoregressive modeling technique and neural network based modeling techniques are used to model and simulate electroencephalogram (EEG) signals. EEG signal modeling is used as a tool to identify pathophysiological EEG changes potentially useful in clinical diagnosis. The normal, background and epileptic EEG signals are modeled and the dynamical properties of the actual and modeled...
One of the challenges in intensive care is the process of weaning from mechanical ventilation. We studied the differences in respiratory pattern variability between patients capable of maintaining spontaneous breathing during weaning trials and patients that fail to maintain spontaneous breathing. In this work, neural networks were applied to study these differences. 64 patients from mechanical ventilation...
Eight features inherent in the electroencephalogram (EEG) have been extracted and evaluated with respect to their ability to distinguish bursts from suppression in burst-suppression EEG. The study is based on EEG from six full term infants who had suffered from lack of oxygen during birth. The features were used as input in a neural network, which was trained on reference data segmented by an experienced...
The paper aims to identify speech using the facial muscle activity without the audio signals. The paper presents an effective technique that measures the relative muscle activity of the articulatory muscles. Five English vowels were used as recognition variables. This paper reports using moving root mean square (RMS) of surface electromyogram (SEMG) of four facial muscles to segment the signal and...
Synchronization of neuronal activity in the γ-band has been shown to play an important role in higher cognitive functions, by grouping together the necessary information in different cortical areas to achieve a coherent perception. In the present work, we used a neural network of Wilson-Cowan oscillators to analyze the problem of binding and segmentation of high-level objects. Binding is achieved...
Signal analysis provides important clues for diagnosis of disease in many arenas, particularly in cardiology. While each result may give good diagnostic information, a comprehensive decision model requires the combination of results. In the work described here, a neural network model is used to combine various features obtained through signal analysis. The original model is based on electrocardiogram...
The problem of the evaluation of brain connectivity has become a fundamental one in the neurosciences during the latest years, as a way to understand the organization and the interaction of several cortical areas during the execution of cognitive or motor tasks. Following an approach that derives from the graph theory, we analyzed the architectural properties of the networks obtained by the use of...
In this paper, we present a comprehensive neural network based modeling and validation framework for reverse engineering gene regulatory interactions. We employ two approaches, Gene Set Stochastic Sampling and Sensitivity Analysis, to infer these interactions. We first apply these methods to a simulated artificial dataset to ensure their correctness and accuracy. True biological interactions are then...
Understanding the molecular recognition between RNA and proteins is central to elucidation of many biological processes in the cell. Although structural data are available for some protein-RNA complexes, the interaction patterns are still mostly unclear. In this study, support vector machines as well as artificial neural networks have been trained to predict RNA binding residues from five sequence-derived...
This paper describes a driver fatigue detection system using an artificial neural network (ANN). Using electroencephalogram (EEG) data sampled from 20 professional truck drivers and 35 non professional drivers, the time domain data are processed into alpha, beta, delta and theta bands and then presented to the neural network to detect the onset of driver fatigue. The neural network uses a training...
The objective of this study was to classify hysteroscopy images of the endometrium based on texture analysis for the early detection of gynaecological cancer. A total of 418 regions of interest (ROIs) were extracted (209 normal and 209 abnormal) from 40 subjects. Images were gamma corrected and were converted to gray scale. The following texture features were extracted: (i) statistical features, (ii)...
In this study, different feature sets are used in conjunction with (k-nearest neighbors) k-NN and artificial neural network (ANN) classifiers to address the classification problem of respiratory sound signals. A comparison is made between the performances of k-NN and ANN classifiers with different feature sets derived from respiratory sound data acquired from one microphone placed on the posterior...
Sleep is a natural periodic state of rest for the body, in which the eyes are usually closed and consciousness is completely or partially lost. In this investigation we used the EOG and EMG signals acquired from 10 patients undergoing overnight polysomnography with their sleep stages determined by expert sleep specialists based on RK rules. Differentiation between Stage 1, Awake and REM stages challenged...
In this paper we present a novel design for a nonlinear dynamic neural network to implement text-independent speaker recognition without the benefit of exact voice signatures. The dynamic properties between the input neuron and the output neuron make use of a nonlinear high-order synaptic neural model with memory of previous input signals. The dynamic neural network is realized in the short-term-frequency...
We present a refined method and design for building parylene neurocages for in vitro studies of live neural networks. Parylene neurocages are biocompatible and very robust, making them ideally suited for studying the synaptic connections between individual neurons to gain insight into learning and memory. The neurocage fabrication process incorporates electrodes into the neurocages to allow for stimulation...
This paper presents a method of automatic processing the electrocardiogram (ECG) signal for the classification of heart beats. Data were obtained from 48 records of the MIT-BIH arrhythmia database (only one lead). Five types of arrhythmic beats were classified using our method, Premature Ventricular Conduction beat (PVC), Atrial Premature Conduction beat (APC), Right Bundle Branch Block beat (RBBB),...
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