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Empirical mode decomposition (EMD) is a recently introduced tool for decomposing signals into so-called intrinsic mode functions (IMF). These IMF represent the data by means of oscillating waves with local zero mean. In some sense the decomposition can be compared with a time-varying filter bank, i.e., signals are decomposed using band limited filters with band widths that vary in time. The main attribute...
Electrocardiographic (ECG) analysis plays an important role in safety assessment during new drug development and in clinical diagnosis. The pre-processing of ECG analysis consists of low-frequency baseline wander (BW) correction and high-frequency artifact noise reduction from the raw ECG. We present approaches for BW correction and de-noising based on discrete wavelet transformation (DWT). We estimate...
New algorithm for quantifying the variation in the QT interval of ECG recording is presented. The algorithm is based on the principal component regression where the eigenvectors of the data correlation matrix are calculated. The eigenvectors are then used for calculation of the principal components and one of them is selected to represent the information about T wave variation. The algorithm is tested...
Evoked potentials are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio. The neural network filtering technique which has the advantage of complex mapping is one of the applicable methods for evoked potentials estimation. The backpropagation algorithm based on second order statistics is commonly used to adapt neural network filters. However it is easily influenced...
For any respiratory sound analysis or assessment, respiratory flow must also be measured simultaneously with the sounds. However, due to difficulties and/or inaccuracy of the most flow measurement techniques, several researchers have attempted to estimate flow from respiratory sounds. However, all of the proposed methods heavily depend on the availability of different rates of flow for calibration...
This study investigates the possibility of using the normalized time-domain features of electrocardiogram (ECG) for improving the capability of human identification. For this purpose, we measured lead-1 rest ECG (normal heart rate) and physically active one (fast heart rate) from the pre-selected group. The characteristic points on the ECG waveform, P, QRS, T are extracted in terms of its time location...
Cardiac arrhythmia is a class of serious heart diseases that threatens many people. Current arrhythmias diagnostic techniques seem to be partially efficient due to the application limitations either in time or in space. The paper presents a real-time continuous arrhythmias detection system (RECAD) platform based on the wireless sensor network technology. This system provides long-term real-time surveillance...
A method for automatic detection of sleep apnea using pulse photoplethysmography signal (PPG) is proposed. This method is based on a detection of decreases on PPG amplitude fluctuations. The proposed detector is composed of three stages: pre-processing, envelope detection, based on root mean square series or Hilbert transform, and decision algorithm based on an adaptive threshold. The detector has...
Electroencephalographic (EEG) signals are normally acquired in the presence of background noise which causes inaccurate or false entropy measurement throughout the signal. In this paper, spectral subtraction is used to pre-process EEG signals to improve the accuracy of computing the subband wavelet entropy (SWE). The silent period in the EEG signal is identified via cepstral distance which allows...
This paper aims at investigating an unsupervised learnt neural networks in classifier applications and comparing them to supervised perceptron type nets. The proposed solutions focus on combing the time-frequency preliminary analysis by means of wavelet transform with application of self organizing maps. Using wavelet transform as a feature extraction tool allowed to reveal important parameters included...
In recent years, scientists, doctors in the field of biomedical engineering and researchers of the correlated fields have been concentrating on study of activities of bioelectricity of different cortex fields of human brain on the condition of different evocable and cognitive stimulations, and try to test human psychology and physiology, and control exterior environment. Independent component analysis...
Heart beat is an unavoidable source of interference during lung sound recording. This disturbance is more significant at low and medium breathing flow rates. Removing heart sounds (HS) from lung sound recordings or vice versa is a challenging task but of great interest for respiratory specialists and cardiologists. In this study, to separate the two signals, a novel HS separation method based on independent...
Frequent arousals during sleep degrade the quality of sleep and result in sleep fragmentation. Visual inspection of physiological signals to detect the arousal events is inconvenient and time-consuming work. The purpose of this study was to develop an automatic algorithm to detect the arousal events. We proposed the automatic method to detect arousals based on time-frequency analysis and the support...
Very slow yogic breathing techniques provide valuable insights into mechanisms of autonomous nervous system regulation that are usually not available for human subjects. This paper presents results of eight sessions of Nadi Shodhana Pranayama practiced at rate of one breath per minute. We characterized statistic and spectral measures of heart rate variability before, during, and after exercises. Significant...
The discrimination of ECG signals using nonlinear dynamic parameters is of crucial importance in the cardiac disease therapy and chaos control for arrhythmia defibrillation in the cardiac system. However, the discrimination results of previous studies using features such as maximal Lyapunov exponent (lambdamax) and correlation dimension (D2) alone are somewhat limited in recognition rate. In this...
In this paper, we propose a measuring device implementation to measure ECG, 50 KHz BIA for getting BIA/respiration, and 10~50 Hz GSR for measuring electrical characteristics of skin. To this end, BIA signal is separated by line filter and high impedance element and the interference between signals could be minimized by switching in time domain to separate ECG and GSR signals having the similar frequency...
A kind of remote monitoring system on heart sound is constructed, which can tele-monitor ECG and PCG. The system integrates embedded Internet technology and wireless technology. As it can send ECG and PCG by Internet, it realizes real-time recording and monitoring of physiology parameter of patients at low cost and both at home and in hospital, and it also can be used for analysis for computer or...
This study aims to determine whether or not the mismatch negativity (MMN) is involved in the processing on time-frequency distribution of acoustic information. Invariable, step down and gradually decreasing time-frequency distribution complex tones compose the three kinds of deviant stimuli, which appear randomly in the repeating standard tones sequence. MMNs were evoked by the deviant stimuli. The...
Dynamic synchronization between different brain regions has long been considered as the underlying neural mechanism of sensory, motor and cognitive functions. Practical methods of accurately quantifying this kind of dynamics by using scalp EEG are plagued by volume conduction effects and background noise. We propose a new method of measuring transient phase locking between independent components underlying...
How to effectively remove the magnetic resonance imaging (MRI) artifacts in the electroencephalography (EEG) recordings, induced when EEG and functional magnetic resonance imaging (FMRI) are simultaneously recorded, is a challenge for integration of EEG and FMRI. According to the temporal-spatial difference between MRI artifacts and EEG, a new method based on sparse component decomposition in the...
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