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The objective of this paper is to present a real time bio-telemetry system using LabVIEW, which acquires, analyzes and processes physiological parameters such as Electromyogram and Blood Pressure. This system enables continuous monitoring of a patient, which in turn helps the doctor to make a better diagnosis, thus ensuring mobility of both the patient and the doctor. In this proposed system we have...
The proposed research focuses on the development of an ambulatory universal 2-electrode biopotential recording device for recording electrocardiogram (ECG), electromyogram (EMG) and electroocculogram (EOG). The designed device will help the health caregivers to quickly acquire the signals in a computer and digitally record the biopotential signals for analysis at a later stage. The digitally recorded...
In this paper, we have analyzed the physical and mental performance of computer user due to the long term use of computer by analyzing the variations in physiological signals. Performances of computer user are monitored by recording and analyzing the variations in Electrocardiogram (ECG), Electromyogram (EMG), Electrooculogram (EOG), and Electroencephalogram (EEG). Detecting the performance of computer...
Surface electromyograms (EMGs) are valuable in the pathophysiological study and clinical treatment. These recordings are critically often contaminated by cardiac artifact. The purpose of this article was to evaluate the performance of an adaptive filter and artificial neural network (ANN) in removing electrocardiogram (ECG) contamination from surface EMGs recorded from the pectoralismajor muscles...
Assessing human stress in real-time is more difficult and challenging today. The present review deals about the measurement of stress in laboratory environment using different stress inducement stimuli by the help of physiological signals. Previous researchers have been used different stress inducement stimuli such as stroop colour word test (CWT), mental arithmetic test, public speaking task, cold...
In the world of technology, human-machine interaction is becoming more common and will perhaps be a part of our life in the future. Human-machine interaction is more natural if machines are able to perceive and respond to human non-verbal communication such as emotions instead of relying only on audio-visual emotion channels. A particle swarm optimization (PSO) of synergetic neural classifier for...
This paper presents the hardware implementation of fast FIR low pass filter for Electromyogram (EMG) removal from Electrocardiogram (ECG) signal. We designed the architecture having less critical delay then convention FIR design and fast enough to remove EMG from ECG signal. We Proposed branched tree architecture for adder connection to reduce the critical delay. The Proposed architecture has been...
In this paper a new method for removing of Power Line Interference (PLI) and ECG Signal from EMG signal is proposed. This method is designed based on filtering of EMG signal corrupted with interference of power line and ECG (EMG+PLI+ECG), by using Matching Pursuit (MP) that is a time-frequency transform. For this reason, according to the cosine nature of PLI and alternative mode of ECG signal, Cosine...
The paper developed a block-wise approach for ICA algorithms which can improve the computational efficiency of ICA without the degradation of performance for the separation of biomedical signals. Source signals including electrocardiogram (ECG), electromyogram (EMG) and 60-Hz sinusoid are linearly mixed for experimental tests. The mean-square errors (MSE) between the original sources and the separated...
This paper describes a fractional integration-based lowpass differentiator filter for processing noisy physiological signals. The proposed scheme combines the two operations of differentiation and high frequency noise filtering into a unique noncausal finite impulse response (FIR) filter with antisymetrical impulse response. The filter coefficients, depending on the fractional order only, can be easily...
This paper presents a feature extraction technique based on the Hilbert-Huang Transform (HHT) method for emotion recognition from physiological signals. Four kinds of physiological signals were used for analysis: electrocardiogram (ECG), electromyogram (EMG), skin conductivity (SC) and respiration changes (RSP). Each signal is decomposed into a finite set of AM-FM mono components (fission process)...
Biopotential recording electrodes have been used to monitor non-invasively electroencephalogram (EEG), electromyogram (EMG) and electrocardiogram (ECG) by means of electrolyte pastes that improve electrode-skin interface. However, the skin preparation of 32 EEG electrodes may take up to 45 min. Despite the low skin-contact impedance and reasonable stability of the standard Ag/AgCl electrodes, several...
Electrocardiogram (EKG) and Electroencephalogram (EEG) are widely used for kinds of disorders detection. In case of EKG, RR interval series is used for heart rate variability (HRV) analysis, which is a reliable reflection of status of autonomic nervous system. HRV is a function of both physical and mental activity. In order to analyze the influence of metal stress on HRV, EKG signals including information...
Recording and processing physiological signals from real life for the purpose of affect detection presents many challenges beyond those encountered in the laboratory. Issues such as finding the proper baseline and normalization take on a time dependent meaning. Physical motion also becomes an important factor as these physiological signals often overwhelm those caused by affect. Motion also has an...
This paper describes our recent experimental results to elucidate the effects of high CO2 on a deep-sea fish. A few species can be captured alive from depths of ca. 400 meters and be used for in vivo CO2 exposure experiments. We have developed an experimental setup that allows us to expose deep-sea organisms to high pressures (up to 20 kPa) at low temperatures (1-2 degC) for extended periods. This...
The purpose of this study is to develop a health monitoring system. This system represents human health condition and human motion simultaneously. This system obtains motion data by optical motion capture system. Electromyogram and electrocardiogram are used for health condition estimation. These are measured synchronously with motion data. Two experiments were performed to evaluate our system. In...
Wireless sensor network (WSN) technologies have been extended to the bio-medical area, and it is called body sensor networks (BSN). BSN systems sense and transmit the vital signs of human, such as electrocardiogram (ECG) and electromyogram (EMG), in unobtrusive and efficient way. Those vital signs are critical to human's life and behavior, so the data should be reliable and transmitted in real-time...
Certification of medical equipment requires thorough testing. Prior to final testing of equipment (such as monitoring equipment) on a human volunteer, it is necessary to test equipment via emulation. This paper presents a bio-simulator that generates electrocardiogram (ECG) and electromyogram (EMG) signals by breaking a signal up into pulses and integrating these pulses, and electroencephalogram (EEG)...
Little attention has been paid so far to physiological signals for emotion recognition compared to audio-visual emotion channels, such as facial expressions or speech. In this paper, we discuss the most important stages of a fully implemented emotion recognition system including data analysis and classification. For collecting physiological signals in different affective states, we used a music induction...
Recent technological advances in sensors, low-power integrated circuits, and wireless communications have enabled the design of low-cost, miniature, lightweight, intelligent physiological sensor platforms that can be seamlessly integrated into a body area network for health monitoring. Wireless body area networks (WBANs) promise unobtrusive ambulatory health monitoring for extended periods of time...
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