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A signal quality classification algorithm is presented to evaluate signal quality in ambulatory monitoring system. Acoustic based signal is classified as good signal, weak signal or noisy signal. Certain features in the acquired signal are extracted and analyzed to differentiate the class of signal quality. With this classification, wrong physiological estimation due to poor signal quality can be...
In this study, we target to automatically detect behavioral patterns of patients with autism. Many stereotypical behavioral patterns may hinder their learning ability as a child and patterns such as self-injurious behaviors (SIB) can lead to critical damages or wounds as they tend to repeatedly harm one single location. Our custom designed accelerometer based wearable sensor can be placed at various...
A real-time method using only accelerometer data is developed for classifying basic human static postures, namely sitting, standing, and lying, as well as dynamic transitions between them. The algorithm uses discrete wavelet transform (DWT) in combination with a fuzzy logic inference system (FIS). Data from a single three-axis accelerometer integrated into a wearable headband is transmitted wirelessly,...
The analgesic effect of morphine is highly individual, calling for objective methods to predict the subjective pain relief. Such methods might be based on alteration of brain response caused by morphine during painful stimuli. The study included 11 healthy volunteers subjectively quantifying perception of painful electrical stimulations in the esophagus. Brain evoked potentials following stimulations...
Practical issues such as accuracy with various subjects, number of sensors, and time for training are important problems of existing brain-computer interface (BCI) systems. In this paper, we propose a hybrid framework for the BCI system that can make machine control more practical. The electrooculogram (EOG) is employed to control the machine in the left and right directions while the electroencephalogram...
An algorithm to detect automatically drowsiness episodes has been developed. It uses only one EEG channel to differentiate the stages of alertness and drowsiness. In this work the vectors features are building combining Power Spectral Density (PDS) and Wavelet Transform (WT). The feature extracted from the PSD of EEG signal are: Central frequency, the First Quartile Frequency, the Maximum Frequency,...
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 system for diagnosing health problems from gait patterns of elderly to support their independent living is proposed in this paper. Motion capture system, which consists of tags attached to the body and sensors situated in the apartment, is used to capture gait of elderly. Position of the tags is acquired by the sensors and the resulting time series of position coordinates are analyzed with machine...
Falling is a common health problem for elderly. It is reported that more than one third of adults 65 and older fall each year in the United States. To address the problem, we are currently developing an acoustic fall detection system, FADE, which automatically detects a fall and reports it to the caregiver. In a previous version, FADE used a 3-microphone linear array to eliminate the false alarms...
High usability myo-controlled devices require robust classification schemes during dynamic contractions. Therefore, this study investigates the impact of the training data set on the performance of several pattern recognition algorithms during dynamic contractions. It is shown that combined with a threshold to detect the onset of the contraction, current pattern recognition algorithms used on static...
This study presents different methods for automatic sleep classification based on heart rate variability (HRV), respiration and movement signals recorded through bed sensors. Two methods for feature extraction have been implemented: time variant-autoregressive model (TVAM) and wavelet discrete transform (WDT); the obtained features are fed into two classifiers: Quadratic (QD) and Linear (LD) discriminant...
A Brain Computer Interface is a system that provides an artificial communication between the human brain and the external world. The paradigm based on event related evoked potentials is used in this work. Our main goal was to efficiently solve a binary classification problem: presence or absence of P300 in the registers. Genetic Algorithms and Support Vector Machines were used in a wrapper configuration...
In the present work we apply a fully automatic procedure to the analysis of signal coming from a sensorized T-shit, worn during the night, for sleep evaluation. The goodness and reliability of the signals recorded trough the T-shirt was previously tested, while the employed algorithms for feature extraction and sleep classification were previously developed on standard ECG recordings and the obtained...
In this study, an automatic and online snore detection algorithm is proposed. The respiratory sound signals were recorded simultaneously with Polysomnography (PSG) data during sleep from 20 patients (10 simple snorers and 10 OSA patients). The sound signals were recorded by two tracheal and ambient microphones. The potential snoring episodes were identified using Vertical Box (V-Box) algorithm. The...
In this paper, we evaluated BCI algorithm using CSP for finding out about realistic possibility of BCI based on CSP. BCI algorithm that was comprised of CSP and least square linear classifier was evaluated in 10 persons. According to the result of the experiment, the effect of combined cue and neurofeedback is evaluated. In case of combined cue, the correlation of combined cue and visual cue is higher...
Heart Rate variability (HRV) is important in characterizing heart functions. However, artifacts and trends are regularly observed to contaminate the HRV sequences. This study proposes a simple and effective preprocessor for the removal of artifacts and trend in the HRV sequences. A thresholding filter is applied to remove artifacts to maintain the HRV sequences in a reasonable range. A wavelet filter...
Due to the artifacts in electroencephalography (EEG) data, the performance of brain-computer interface (BCI) is degraded. On the other hand, in the motor imagery based BCI system, EEG signals are usually contaminated by the misleading trials caused by improper imagination of a movement. In this paper, we present a novel algorithm to detect the abnormal EEG data using genetic algorithm (GA). After...
This study aimed to develop an automatic algorithm to detect the activation phases (A phases) of the Cyclic Alternating Pattern. The sleep EEG microstructure of 4 adult, healthy subjects was scored by a sleep medicine expert. Features were calculated from each of the six EEG bands (low delta, high delta, theta, alpha, sigma and beta), and three additional characteristics were computed: the Hjorth...
Abnormal skeleton muscle activity during REM sleep is characterized as REM Behaviour Disorder (RBD), and may be an early marker for different neurodegenerative diseases. Early detection of RBD is therefore highly important, and in this ongoing study a semi-automatic method for RBD detection is proposed by analyzing the motor activity during sleep. Method: A total number of twelve patients have been...
The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been...
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