Giuseppina Inuso

Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings

Electroencephalographic (EEG) recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper the issue of artifact extraction from Electroencephalographic data is addressed and a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA), is presented and compared to two other techniques based on ICA and wavelet denoising. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts. This dataset was processed by WICA and the two other methods. The proposed technique had the best artifact separation performance for every kind of artifact also allowing for the minimum information loss.

Brain Activity Investigation by EEG Processing: Wavelet Analysis, Kurtosis and Renyi's Entropy for Artifact Detection

Electroencephalographic (EEG) recordings are employed in order to investigate the brain activity in neuro pathological subjects. Unfortunately EEG are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. In this paper we propose a multiresolution analysis, based on EEG wavelet processing, to extract the cerebral EEG rhythms. We also present a method based on Renyis entropy and kurtosis to automatically identify the Wavelet components affected by artifacts. Finally, we discuss as the joint use of wavelet analysis, kurtosis and Renyis entropy allows for a deeper investigation of the brain activity and we discuss the capability of this technique to become an efficient preprocessing step to optimize artifact rejection from EEG. This is the first technique that exploits the peculiarities of EEG to optimize EEG artifact detection.

Clustering of entropy topography in epileptic electroencephalography

Epileptic seizures have been considered sudden and unpredictable events for centuries. A seizure seems to occur when a massive group of neurons in the cerebral cortex begins to discharge in a highly organized rhythmic pattern, then it develops according to some poorly described dynamics. As proved by the results reported by different research groups, seizures appear not completely random and unpredictable events. Thus, it is reasonable to wonder when, where and why the epileptogenic processes start up in the brain and how they result in a seizure. In order to detect these phenomena from the very beginning (hopefully minutes before the seizure itself), we introduced a technique, based on entropy topography, that studies the synchronization of the electric activity of neuronal sources in the brain. We tested it over 3 EEG data set from patients affected by partial epilepsy and 25 EEG recordings from patients affected by generalized seizures as well as over 40 recordings from healthy subjects. Entropy showed a very steady spatial distribution and appeared linked to the brain zone where seizures originated. A self-organizing map-based spatial clustering of entropy topography showed that the critical electrodes shared the same cluster long time before the seizure onset. The healthy subjects showed a more random behaviour.

Multiresolution ICA for artifact identification from electroencephalographic recordings

This paper addresses the issue of artifact extraction from Electroencephalographic (EEG) signals and introduces a new technique for EEG artifact removal, based on the joint use of Wavelet transform and Independent Component Analysis (WICA). In fact, EEG recordings are often contaminated by the artifacts, signals that have non-cerebral origin and that might mimic cognitive or pathologic activity and therefore distort the analysis of EEG. The proposed technique extracts the artifacts taking into account the frequencies of the four major EEG rhythms. An artificial artifact-laden EEG dataset was created mixing a real EEG with a set of synthesized artifacts and the performance of WICA was measured. WICA had the best artifact separation performance for every kind of artifact with respect to other techniques and allowed for minimum information loss.

Algorithms and topographic mapping for epileptic seizures recognition and prediction

Epileptic seizures seem to result from an abnormal synchronization of different areas of the brain, as if a kind of recruitment occurred from a critical area towards other areas of the brain, until the brain can no longer bear the extent of this recruitment and it triggers the seizure in order to reset this abnormal condition. In order to catch these recruitment phenomena, a technique based on entropy is introduced to study the synchronization of the electric activity of neuronal sources in the brain. This technique was tested over 25 EEG dataset from patients affected by absence seizures as well as on 40 EEG dataset from healthy subjects. The results show an abnormal coupling among the electrodes that will be involved in seizure development can be hypothesized before the seizure itself, in particular, the frontal/temporal area appears steadily associated to an underlying high synchrony in absence seizure patients.

Information theoretic learning for inverse problem resolution in bio-electromagnetism

This paper addresses the issue of learning directly from the observed data in Blind Source Separation (BSS), a particular inverse problem. This problem is very likely to occur when we are dealing with two or more independent electromagnetic sources. A powerful approach to BSS is Independent Component Analysis (ICA). This approach is much more powerful if no apriori assumption about data distribution is made: this is possible transferring as much information as possible to the learning machine defining a cost function based on an information theoretic criterion. In particular, Renyis definition of entropy and mutual information are introduced and MERMAID (Minimum Renyis Mutual Information), an algorithm for ICA based on such these definitions, is here described, implemented and tested over a popular BSS problem in bio-electromagnetism: fetal Electrocardiogram (fECG) extraction. MERMAID was compared to the well known algorithm INFOMAX and it showed to better learn from data and to provide a better source separation. The extracted fECG signals were finally post-processed by wavelet analysis.

Measures of Brain Connectivity through Permutation Entropy in Epileptic Disorders

Most of the scientist assume that epileptic seizures are triggered by an abnormal electrical activity of groups of neural populations that yields to dynamic changes in the properties of Electroencephalography (EEG) signals. To understand the pathogenesis of the epileptic seizures, it is useful detect them by using a tool able to identify the dynamic changes in EEG recordings. In the last years, many measures in the complex network theory have been developed. The aim of this paper is the use of Permutation Entropy (PE) with the addition of a threshold method to create links between the different electrodes placed over the scalp, in order to simulate the network phenomena that occur in the brain. This technique was tested over two EEG recordings: a healthy subject and an epileptic subject affected by absence seizures.

Dynamic Modeling of Heart Dipole Vector for the ECG and VCG Generation

The electrocardiogram (ECG) is the major diagnostic instrument for the analysis of cardiac electrophysiology; this is due to two simple reasons, first because it is not invasive and secondly because an ECG is a source of accurate information about the heart functionality. For these reasons, in the last years, the ECG has attracted the interest of many scientists, who have developed algorithms and models to investigate the cardiac disorders. The aim of this paper is to introduce a novel dynamic model to simulate pathologic ECGs. We discuss a generalization of a well known model for normal ECG signals generation and we show that it can be extended to simulate the effects on ECG of some cardiac diseases. We also represent the 3D vector trajectory of the cardiac cycle by reconstructing the heart dipole vector (HDV) from the Frank lead system. Finally, we propose to generate the complete 12-lead ECG system by the HDV projection. The results shows this a powerful tool for pathologic ECG generation, future research will be devoted to set up an extensive synthetic ECG database which could open the door to new theories about the genesis of the ECG as well as new models of heart functionality.

Automatic detection of critical epochs in coma-EEG using independent component analysis and higher order statistics

Previous works showed that the joint use of Principal Component Analysis (PCA) and Independent Component Analysis (ICA) allows to extract a few meaningful dominant components from the EEG of patients in coma. A procedure for automatic critical epoch detection might support the doctor in the long time monitoring of the patients, this is why we are headed to find a procedure able to automatically quantify how much an epoch is critical or not. In this paper we propose a procedure based on the extraction of some features from the dominant components: the entropy and the kurtosis. This feature analysis allowed us to detect some epochs that are likely to be critical and that are worth inspecting by the expert in order to assess the possible restarting of the brain activity.

Evolution Characterization of Alzheimer’s Disease Using eLORETA’s Three-Dimensional Distribution of the Current Density and Small-World Network

Alzheimer’s disease (AD) is the most common neurodegenerative disorder characterized by cognitive and intellectual deficits and behavior disturbance. The electroencephalogram (EEG) has been used as a tool for diagnosing AD for several decades. In the pre-clinical stage of AD, no reliable and valid symptoms are detected to allow a very early diagnosis. There are four different stages associated with AD. The first stage is known as Mild Cognitive Impairment (MCI), and corresponds to a variety of symptoms which do not significantly alter daily life. In the mild stage, an impairment of learning and memory is usually notable. The next stages (Mild and Moderate AD) are characterized by increasing cognitive deficits and decreasing independence, culminating in the patient’s complete dependence on caregivers and a complete deterioration of personality (Severe AD). In this paper, we propose the study of the evolution of Alzheimer’s disease using eLORETA’s three-dimensional distribution of the current density and Small-world network. Our goal is to see the changes of MCI patients’ EEG (called EEG T0) after three months (EEG T1). The results show that small-world is a valid technique to see the temporal evolution of the disease.