Fabio La Foresta

Multivariate Multi-Scale Permutation Entropy for Complexity Analysis of Alzheimer’s Disease EEG

Abstract: An original multivariate multi-scale methodology for assessing the complexity of physiological signals is proposed. The technique is able to incorporate the simultaneous analysis of multi-channel data as a unique block within a multi-scale framework. The basic complexity measure is done by using Permutation Entropy, a methodology for time series processing based on ordinal analysis. Permutation Entropy is conceptually simple, structurally robust to noise and artifacts, computationally very fast, which is relevant for designing portable diagnostics. Since time series derived from biological systems show structures on multiple spatial-temporal scales, the proposed technique can be useful for other types of biomedical signal analysis. In this work, the possibility of distinguish among the brain states related to Alzheimer’s disease patients and Mild Cognitive Impaired subjects from normal healthy elderly is checked on a real, although quite limited, experimental database. Keywords:

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.

Entropic Measures of EEG Complexity in Alzheimer's Disease Through a Multivariate Multiscale Approach

Alzheimers disease (AD) impact is rapidly growing in western countries. The unavoidable progression of the disease, call for reliable ways to diagnose the AD in its early stages. Recently, it has been shown that the electroencephalography (EEG) complexity analysis could be used to predict the conversion from mild cognitive impairment (MCI) to AD. Despite the EEG analysis does not achieve yet the required clinical performance in terms of both sensitivity and specificity to be accepted as a clinically reliable technique of screening, the researchers count on the easiness and the non-invasiveness of the EEG measuring system. The aim of this paper is to analyze the efficacy of entropic complexity measures as a possible bio-marker to distinguish among the brain states related to the AD patients and MCI subjects from normal healthy elderly. The research is carried out on an experimental database. Three different emerging measures of complexity are compared, namely, permutation entropy, sample entropy, and Lempel-Ziv complexity. Because time series derived from biological systems show structures on multiple spatial-temporal scales and there exists a significant inter-channel correlation among the EEG channels, a multiscale multivariate approach is also implemented. Limited to the analyzed data, the results show that the severity of the AD reflects in the EEG dynamic complexity leaving the hope of early diagnosis based on simple EEG.

A New Approach to Detection of Muscle Activation by Independent Component Analysis and Wavelet Transform

Recent works have demonstrated that the Independent Components (ICs) of simultaneously-recorded surface Electromyography (sEMG) recordings are more reliable in monitoring repetitive movements and better correspond with ongoing brain-wave activity than raw sEMG recordings. In this paper we propose to detect single muscle activation, when the arms reach a target, by means of ICs time-scale decomposition. Our analysis starts with acquisition of sEMG (surface EMG) signals; source separation is performed by a neural net-work that implements on Independent Component Analysis algorithm. In this way we obtain a signal set each representing single muscle activity. The wave-let transform, lastly, is utilised to detect muscle activation intervals.

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.

EEG Complexity Modifications and Altered Compressibility in Mild Cognitive Impairment and Alzheimer’s Disease

The objective of this work is to respond to the question: can quantitative electroencephalography (EEG) distinguish among Alzheimer’s Disease (AD) patients, mild cognitive impaired (MCI) subjects and elderly healthy controls? In other words, are there nonlinear indexes extracted from raw EEG data that are able to manifest the background difference among EEG? The response we give here is that a synthetic index of entropic complexity (Permutation Entropy, PE) as well as a measure of compressibility of the EEG can be used to discriminate among classes of subjects. An experimental database has been analyzed to make these measurements and the results we achieved are encouraging also in terms of disease evolution. Indeed, it is clearly shown that the condition of MCI has intermediate properties with respect to the analyzed markers: thus, these markers could in principle be used to evaluate the probability of transition from MCI to mild AD.

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.

Longitudinal study of alzheimer's disease degeneration through EEG data analysis with a NeuCube spiking neural network model

Motivated by the dramatic rise of neurological disorders, we propose a SNN technique to model electroen-cephalography (EEG) data collected from people affected by Alzheimers Disease (AD) and people diagnosed with mild cognitive impairment (MCI). An evolving spatio-temporal data machine (eSTDM), named the NeuCube architecture, is used to analyse changes of neural activity across different brain regions. The model developed allows for studying AD progression and for predicting whether a patient diagnosed with MCI is more likely to develop AD.

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.

Artifact cancellation from electrocardiogram by mixed Wavelet-ICA filter

In this paper a novel method, called WICA, based on the joint use of wavelet transform (WT) and independent component analysis (ICA) is discussed. The main advantage of this method is that it encompasses the characteristics of WT and ICA. In order to show the novelty of our method, we present a biomedical signal processing application in which ICA has poor performances, whereas WICA yields good results. In particular, we discuss the artifact cancellation in electrocardiographic (ECG) signals. The results show the ability of WICA to cancel some artifact from ECG when only two signals are recorded.