Patrizia Vergallo

Empirical Mode Decomposition vs. Wavelet Decomposition for the Extraction of Respiratory Signal From Single-Channel ECG: A Comparison

The respiratory signal can be accurately evaluated by single-channel electrocardiogram (ECG) processing, as shown in recent literature. Indirect methods to derive the respiratory signal from ECG can benefit from a simultaneous study of both respiratory and cardiac activities. These methods lead to major advantages such as low cost, high efficiency, and continuous noninvasive respiratory monitoring. The aim of this paper is to reconstruct the waveform of the respiratory signal by processing single-channel ECG. To achieve these goals, two techniques of decomposition of the ECG signal into suitable bases of functions are proposed, such as the empirical mode decomposition (EMD) and the wavelet analysis. The results highlight the main differences between them in terms of both theoretical foundations, and performance achieved by applying these algorithms to extract the respiratory waveform shape from single-channel ECG are presented. The results also show that both algorithms are able to reconstruct the respiratory waveform, although the EMD is able to break down the original signal without a preselected basis function, as it is necessary for wavelet decomposition. The EMD outperforms the wavelet approach. Some results on experimental data are presented.

Entropy Index in Quantitative EEG Measurement for Diagnosis Accuracy

Electroencephalogram (EEG) remains the most immediate, simple, and rich source of information for understanding phenomena related to brain electrical activities. It is certainly a source of basic and interesting information to be extracted using specific and appropriate techniques. The most important aspect in processing EEG signals is to use less co-lateral assets and instrumentation in order to carried out a possible diagnosis; this is the approach of early diagnosis. Advanced estimate spectral analysis can reveal new information encompassed in EEG signals by means of specific parameters or indices. The research proposes a multidimensional approach with a combined use of decimated signal diagonalization (DSD) as basis from which it is possible to work by finding appropriate signal windows for revealing expected information and overcoming signal processing limitations encountered in quantitative EEG. Important information, about the state of the patient under observation, must be extracted from calculated DSD bispectrum. For this aim, it is useful to define an assessment index about the dynamic process associated with the analyzed signal. This information is measured by means of entropy, since the degree of order/disorder of the recorded EEG signal will be reflected in the obtained DSD bispectrum. The general advantage of multidimensional approach is to reveal eventual stealth frequencies “in space and in time” giving a topological vision to be correlated to physical areas which these frequencies emerge from. Long term and sleeping EEG recorded are analyzed, and the results obtained are of interest for an accurate diagnosis of the patients clinical condition.

Mutidimensional analysis of EEG features using advanced spectral estimates for diagnosis accuracy

Electroencephalogram (EEG) is a source of interesting information if one is able to extract them according to appropriate techniques. The conditions of individual under EEG test is a key issue. In general, EEG feature extraction can be associated to other information like Electrocardiogram (ECG), ergospirometry and electromyogram (EMG). However, in some cases, a multidimensional representation is used; bispectrum is an example of such a representation. HOS (high order statistics), for instance, include the bispectrum and the trispectrum (third and fourth order statistics, respectively). Advanced estimate spectral analysis can reveal new information encompassed in EEG signals. That is the reason the author propose an algorithm based on DSD (Decimated Signal Diagonalization) that is able of processing exponentially dumped signals like those that regard EEG features. The version proposed here is a multidimensional one.

Optimizing and Post Processing of a Smart Beamformer for Obstacle Retrieval

Beamforming is one of the most interesting techniques used to know distance systems in order to detect punctual, widespread obstacles. If correctly associated to DOA (Difference of Arrival), it can allow the description of obstacle shape. Distance ranging, for mobile and fixed systems, namely cars, vehicles, vessels and airplanes, that is a key issue for demands of nowadays. Distance between cars and from obstacles can be established and measured using laser and ultrasound. Cloudy and foggy conditions are very important requirements for testing distance ranging facilities. If based on acoustic waves, they can be easily integrated by sophisticated on-board software in order to perform new features. This research presents interesting aspects of defining new requirements for an acoustic scanning capable of reconstructing fixed obstacle features by targeting them using a special array of sensors. The term “acoustic scanning” is intended here as an aspect of sound ranging and reproduction regarding spatial locations of the obstacle, that is spatial shaping. The paper illustrates first an experimental system from which it is possible to derive parameters for setting spatial shaping of scenarios and after a clear identification of DOAs.

Spatial Filtering to Detect Brain Sources from EEG Measurements

In clinical analysis, neural activity in the brain due to groups of neurons located in the head is recorded by means of EEG electrodes positioned on the scalp of the patient. The identification of the sources responsible for this brain activity is of great importance, especially if neurophysiological disorders are detected. The problem is that there is usually an unknown number of signals simultaneously recorded from the electrodes, each one from unknown direction and with unknown amplitudes, and the measurements are always corrupted by noise too. In this work a method of spatial filtering which combines DOA estimation and Beamforming techniques is presented: a DOA estimation method allows to estimate the directions of activation of the sources, while spatial filters of type Beamforming are designed to pass the electrical activity from a given direction and stop that originated from other directions. In this way the brain is explored to detect sources that have determined the recorded activity from EEG.

Identification of Visual Evoked Potentials in EEG detection by emprical mode decomposition

Visual Evoked Potentials (VEPs) are referred to electrical potentials due to brief visual stimuli which can be recorded from scalp overlying visual cortex. A way to measure VEPs is through encephalogram (EEG). VEPs are very important because they can quantify functional integrity of the optic pathway. Their study allows to detect abnormalities that affect the visual pathways or visual cortex in the brain, and so methods that permit to identify VEPs components in EEG signals must be defined. However, the background activity measured from EEG hides VEPs components because they have a low voltage. So it is necessary to define a robust method to extract features, which best describe these potentials of interest. In this work Empirical Mode Decomposition (EMD) method is used to separate the EEG components and to detect VEPs. EMD decomposes a signal into components named Intrinsic Mode Functions (IFM). The results, obtained from the study of EEG records of a normal person, suggest that IMFs may be used to determine VEPs in EEG and to obtain important information related to brain activity by a time and frequency analysis of IMF components. It is well comparable with the known Wavelet Transform method, but it is characterized from a greater simplicity of implementation because the basis used in the analysis is generated by the same analyzed signal.

Decimated Signal Diagonalization Method for Improved Spectral Leak Detection in Pipelines

Leak detection is an important issue in piping that deals with the management of water resources; nowadays large amounts of water in the network are dispersed as reported in current scientific literature. Among the methods for leak detection in water pipes, spectral analysis is very interesting. A classical spectral method is fast Fourier transform, but in this paper, we present an alternative method of spectral analysis, which has higher performance in terms of resolution and fast processing, namely decimated signal diagonalization (DSD). It is a nonlinear, parametric method for fitting time domain signals represented in terms of exponentially damped time signals. The aim is to reconstruct the unknown components as the harmonic variables, estimating the fundamental complex frequencies, and amplitudes. The DSD method partly uses the principles of the filter diagonalization method (FDM), which constructs matrices of a generalized eigenvalue problem directly from measured time signals of arbitrary length. However, the DSD because of its windowing technique produces a considerable reduction of size of the original data matrix, and consequently acquisition time can be shorter. We have tested the DSD method for leak detection problem in an experimental zigzag pipeline. We show as the DSD method produces good results in terms of resolution than FDM one.

Diffusion Tensor Imaging measurements for neuro-detection

The interest of scientific community on brain activities and issues are well-known, especially for neuro-detection of variety of impairments that affect cerebral areas. Various techniques and methods have been using to characterize and to try to understand brain activities for many purposes. Epilepsy, one of them, is a topic of great impact in brain research as well as in Alzheimer issues. Thanks to the development of new biomedical instrumentation it is possible to use appropriate techniques to diagnose the specific pathology. DTI (Diffusion Tensor Imaging) is one of the ultimate technique to have a comprehensive approach to brain activities. This interdisciplinary research highlights the use of DTI to determine preliminarily the ROI (Region Of Interest) for patients with suspected cases of epilepsy. A specific algorithm has been developed to trace out the ROI and the fibers.

Multispectrum Approach in Quantitative EEG: Accuracy and Physical Effort

The detection of neurophysiological features by means of electroencephalogram (EEG) is one of the most recurrent medical exams to be performed on human beings. As it stands, EEG trials are not always sufficient to deliver a clear and precise diagnosis for much pathology. Hence, it must be integrated with other exams. However, we can use all additional instrumental exams to improve the quality of the diagnosis because there are other constraints, namely, financial, medical, and individual. This paper presents an original implementation of EEG signal processing using filter diagonalization method to build a bispectrum and contour representation to discover possible abnormalities hidden in the signal for aided-diagnosis. Two different EEG signals are used for this scope. EEG signals are acquired simultaneously with electrocardiograms (ECG) and ergospirometric ones. ECG signals are also processed along with EEGs. A comparison is made with high order spectra approach. All experimental data regarding EEG, ECG, and ergospirometry are acquired during suspected-patient walking along a path of ~32 m for verifying the impact of fatigue on neurophysiological processes and vice versa.

Spectral analysis of wind profiler signal for environment monitoring

It is already well-known that wind variability plays a key and crucial role in pollutant transportation and climate change. The impact of wind variability is not only on air but also on sea activities because it can cause turbulence and water warming. Hence, it is necessary to have specific and sophisticated instrumentation capable of monitoring local and wide range of wind. One of the most important instrumentation dedicated to wind monitoring is wind profiler that can work in coupled manner with a RASS (Radio Acoustic Sounding System). This paper presents a preliminary study to process signal from a wind profiler in order to detect echoes due to presence of obstacle along to emitted wave path. The scope is to discriminate echoes to obtain the true values of wind, thus, the correct wind profile.