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The analysis of the electrical activity of the uterus recorded externally, the electrohysterogram (EHG), may find an application in the prediction of labor. In the literature parameters that are supposed to be related to the excitability of the uterine cells have almost exclusively been used for this purpose. In the present paper we evaluate the possible use of synchronization parameters for EHG measured...
The present contribution aims at proposing a comprehensive and tutorial introduction to the practical use of wavelet Leader based multifractal analysis to study heart rate variability. First, the theoretical background is recalled. Second, practical issues and pitfalls related to the selection of the scaling range or statistical orders, minimal regularity, parabolic approximation of spectrum and parameter...
Heart rate variability is a non invasive and indirect measure of the autonomic control of the heart. Therefore, alterations to this control system caused by myocardial ischaemia are reflected in changes in the complex and irregular fluctuations of this signal. Multifractal analysis is a well suited tool for the analysis of this kind of fluctuations, since it gives a description of the singular behavior...
Biomedical signals are customarily overlaid with interferences and noise, furthermore, baseline wandering is another significant drawback to their accurate interpretation, especially if the implementation platform is a wheelchair. The nonlinear processes which generate the physiologic signals, and the disturbances, regularly exclude, or limit, the usage of classical linear techniques, hence, among...
Analogue domain implementations of the Continuous Wavelet Transform (CWT) have proved popular in recent years as they can be implemented at very low power consumption levels. This is essential for use in wearable, long term physiological monitoring systems. Present analogue CWT implementations rely on taking mathematical a approximation of the wanted mother wavelet function to give a filter transfer...
This paper presents a system for touch-less heartbeat detection and a cardiopulmonary signal modeling approach. Using a vector network analyzer, a microwave system is tested for the detection of the heartbeat signal at a distance of 1 m from a person. The proposed system shows the ability of detecting the heartbeat signals with the possibility of tuning both frequency and power. Measurements are performed...
A new class of wavelet functions called data-based autocorrelation wavelets is developed for analyzing Magnetic Resonance Spectroscopic (MRS) signals by means of the continuous wavelet transform (CWT), instead of the traditional wavelet like Morlet wavelet. These new wavelets are derived from the normalized autocorrelation function from metabolite data and then used for detecting the presence of a...
Continuous Analgesia / Nociception balance evaluation during general anesthesia could be of precious help for the optimization of analgesic drugs delivery, limiting the risk of toxicity due to the use of opioid drugs, limiting the risk of post operative hyper algesia, and, probably, reducing time of recovery after surgical procedure. Heart Rate Variability analysis has been shown in several studies...
We have developed the non-invasive blood pressure monitor which can measure the blood pressure quickly and robustly. This monitor combines two measurement mode: the linear inflation and the linear deflation. On the inflation mode, we realized a faster measurement with rapid inflation rate. On the deflation mode, we realized a robust noise reduction. When there is neither noise nor arrhythmia, the...
Frequency domain analyses of changes in electromyographic (EMG) signals over time are frequently used to assess muscle fatigue. Fourier based approaches are typically used in these analyses, yet Fourier analysis assumes signal stationarity, which is unlikely during dynamic contractions. Wavelet based methods of signal analysis do not assume stationarity and may be more appropriate for joint time-frequency...
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,...
During cardiac resuscitation from ventricular fibrillation (VF) it would be helpful if we could monitor and predict the optimal state of the heart to be shocked into a perfusing rhythm. Real-time feedback of this state to the emergency medical staff (EMS) could improve the survival rate after resuscitation. In this paper, using real world out-of-the-hospital human VF data obtained during resuscitation...
This paper presents a generic methodology for time series prediction, based on a wavelet decomposition/ reconstruction technique, together with a feedforward neural networks structure. The proposed methodology combines the flexibility and learning abilities of neural networks with a compact description of the signals, inherent to wavelets. In a first phase a wavelet decomposition of the signal is...
Several different algorithms have been proposed for automatic detection of epileptic seizure based on both scalp and intracranial electroencephalography (sEEG and iEEG). Which modality that renders the best result is hard to assess though. From 16 patients with focal epilepsy, at least 24 hours of ictal and non-ictal iEEG were obtained. Characteristics of the seizures are represented by use of wavelet...
Electroencephalogram (EEG) based vigilance detection of those people who engage in long time attention demanding tasks such as monotonous monitoring or driving is a key field in the research of brain-computer interface (BCI). However, robust detection of human vigilance from EEG is very difficult due to the low SNR nature of EEG signals. Recently, compressive sensing and sparse representation become...
Ballistocardiography is a non-invasive technique that yields information about the cardiovascular system that is not available in other external signals such as the electrocardiogram (ECG). In the last years, several research groups have obtained the ballistocardiogram (BCG) by using instrumentation methods simpler than those available in the 1950s and that did not progress because of their complexity...
In this paper, a miniature low-power Electrocardiogram (ECG) signal processing application specific integrated circuit (ASIC) chip is proposed. This chip provides multiple critical functions for ECG analysis using a systematic wavelet transform algorithm and a novel SRAM-based ASIC architecture, while achieves low cost and high performance. Using 0.18 μm CMOS technology and 1 V power supply, this...
In respect to the main goal of our ongoing work for predicting preterm birth, we analyze in this paper the complexity of the uterine electromyography (EMG) by using the sample entropy (SampEn) algorithm. By considering recent methodological developments, we measure the SampEn over multiple scales using the wavelet packet decomposition method. The results obtained from the analyzed data indicate that...
A novel algorithm is presented for classification of four patterns of diffuse lung disease: normal, emphysema, honeycombing and ground glass opacity, on the basis of textural analysis of high resolution computed tomography (HRCT) lung images. The algorithm incorporates scale-space features based on Gaussian derivative filters and multi-dimensional multi-scale features based on wavelet and contourlet...
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...
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