Multiresolution wavelet analysis and ensemble of classifiers for early diagnosis of Alzheimer's disease (original) (raw)
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2007
Early diagnosis of Alzheimer's disease (AD) is becoming an increasingly important healthcare concern. Prior approaches analyzing eventrelated potentials (ERPs) had varying degrees of success, primarily due to smaller study cohorts, and the inherent difficulty of the problem. A new effort using multiresolution analysis of ERPs is described. Distinctions of this study include analyzing a larger cohort, comparing different wavelets and different frequency bands, using ensemble-based decisions and, most importantly, aiming the earliest possible diagnosis of the disease. Surprising yet promising outcomes indicate that ERPs in response to novel sounds of oddball paradigm may be more reliable as a biomarker than the more commonly used responses to target sounds. ᭧
Multiresolution analysis for early diagnosis of Alzheimer's disease
2004
Early diagnosis of Alzheimer's disease is a major concern due to large portions of the elderly population it affects and the lack of a standard and effective diagnosis procedure that is available to community healthcare providers. Several studies have been performed using wavelets or other signal processing methods to analyze EEG signals in an attempt to find a biomarker for Alzheimer's disease, which showed varying degrees of success. To date, in part due to lack of a large study cohort, the results of these studies remain largely inconclusive. In this paper, we describe a new effort using multiresolution wavelet analysis on event related potentials of the EEG to investigate whether such a link can be established. Several factors sets this study apart from similar prior efforts: We use a larger cohort, compare different mother wavelets, rather then using one generic wavelet, and most importantly, we specifically target early diagnosis of the disease. Our multi-year effort will include a total of 80 patients, whose ERPs will be analyzed with several different wavelets and automated classifiers. We present some preliminary, yet very promising, results on analysis of EEGs of the first 28 patients analyzed thus far using two types of wavelets.
Stacked generalization for early diagnosis of Alzheimer's disease
2006
The diagnosis of Alzheimer's disease (AD) at an early stage is a major concern due to growing number of elderly population affected by the disease, as well as the lack of a standard diagnosis procedure available to community clinics. Recent studies have used wavelets and other signal processing methods to analyze EEG signals in an attempt to find a noninvasive biomarker for AD. These studies had varying degrees of success, in part due to small cohort size. In this study, multiresolution wavelet analysis is performed on event related potentials of the EEGs of a relatively larger cohort of 44 patients. Particular emphasis was on diagnosis at the earliest stage and feasibility of implementation in a community health clinic setting. Extracted features were then used to train an ensemble of classifiers based stacked generalization approach. We describe the approach, and present our promising preliminary results.
Clinician’s Road Map to Wavelet EEG as an Alzheimer’s disease Biomarker
Clinical EEG and Neuroscience, 2013
Alzheimer’s disease (AD) is considered the main cause of dementia in Western countries. Consequently, there is a need for an accurate, universal, specific and cost-effective biomarker for early AD diagnosis, to follow disease progression and therapy response. This article describes a new diagnostic approach to quantitative electroencephalogram (QEEG) diagnosis of mild and moderate AD. The data set used in this study was composed of EEG signals recorded from 2 groups: (S1) 74 normal subjects, 33 females and 41 males (mean age 67 years, standard deviation = 8) and (S2) 88 probable AD patients (NINCDS-ADRDA criteria), 55 females and 33 males (mean age 74.7 years, standard deviation = 7.8) with mild to moderate symptoms ( DSM-IV-TR). Attention is given to sample size and the use of state of the art open source tools (LetsWave and WEKA) to process the EEG data. This innovative technique consists in associating Morlet wavelet filter with a support vector machine technique. A total of 111 ...
Continuous Wavelet Transform EEG Features of Alzheimer’s Disease
Volume 1: Adaptive Control; Advanced Vehicle Propulsion Systems; Aerospace Systems; Autonomous Systems; Battery Modeling; Biochemical Systems; Control Over Networks; Control Systems Design; Cooperativ, 2012
We have developed a novel approach to elucidate several discriminating EEG features of Alzheimer's disease. The approach is based on the use of a variety of continuous wavelet transforms, pairwise statistical tests with multiple comparison correction, and several decision tree algorithms, in order to choose the most prominent EEG features from a single sensor. A pilot study was conducted to record EEG signals from Alzheimer's disease (AD) patients and healthy age-matched control (CTL) subjects using a single dry electrode device during several eyes-closed (EC) and eyes-open (EO) resting conditions. We computed the power spectrum distribution properties and wavelet and sample entropy of the wavelet coefficients time series at scale ranges approximately corresponding to the major brain frequency bands. A predictive index was developed using the results from statistical tests and decision tree algorithms to identify the most reliable significant features of the AD patients when compared to healthy controls. The three most dominant features were identified as larger absolute mean power and larger standard deviation of the wavelet scales corresponding to 4-8 Hz (θ) during EO and lower wavelet entropy of the wavelet scales corresponding to 8-12 Hz (α) during EC, respectively. The fourth reliable set of distinguishing features of AD patients was lower relative power of the wavelet scales corresponding to 12-30 Hz (β) followed by lower skewness of the wavelet scales corresponding to 2-4 Hz (upper δ), both during EO. In general, the results indicate slowing and lower complexity of EEG signal in AD patients using a very easy-to-use and convenient single dry electrode device.
2006
With the rapid increase in the population of elderly individuals affected by Alzheimer's disease, the need for an accurate, inexpensive and non-intrusive diagnostic biomarker that can be made available to community healthcare providers presents itself as a major public health concern. The feasibility of EEG as such a biomarker has gained a renewed attention as several recent studies, including our previous efforts, reported promising results. In this paper we present our preliminary results on using wavelet coefficients of event related potentials along with an ensemble of classifiers combined with majority vote and decision templates.
An ensemble based data fusion approach for early diagnosis of Alzheimer's disease
Information Fusion, 2008
As the number of the elderly population affected by Alzheimer's disease (AD) rises rapidly, the need to find an accurate, inexpensive and non-intrusive diagnostic procedure that can be made available to community healthcare providers is becoming an increasingly urgent public health concern. Several recent studies have looked at analyzing electroencephalogram (EEG) signals through the use of wavelets and neural networks. While showing great promise, the final outcomes of these studies have been largely inconclusive. This is mostly due to inherent difficulty of the problem, but also -perhaps -due to inefficient use of the available information, as many of these studies have used a single EEG channel for the analysis. In this contribution, we describe an ensemble of classifiers based data fusion approach to combine information from two or more sources, believed to contain complementary information, for early diagnosis of Alzheimer's disease. Our emphasis is on sequentially generating an ensemble of classifiers that explicitly seek the most discriminating information from each data source. Specifically, we use the event related potentials recorded from the Pz, Cz, and Fz electrodes of the EEG, decomposed into different frequency bands using multiresolution wavelet analysis. The proposed data fusion approach includes generating multiple classifiers trained with strategically selected subsets of the training data from each source, which are then combined through a modified weighted majority voting procedure. The implementation details and the promising outcomes of this implementation are presented.
Pattern Analysis and Applications, 2020
Diagnosis of Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy subjects (Healthy) is currently lacking an automated tool. It requires experience of neuropsychologists and has sensibilities of 80% when separating between Healthy and MCI. The aim of this work is to evaluate the performance of a method for classification among the three groups using a database of 17 Healthy, 9 MCI and 15 AD. The method uses wavelet decomposition of the EEG signal (Haar mother wavelet and 5 decomposition levels) to calculate the wavelet entropy and theta and beta relative power of the EEG signal. These features are used as inputs to a three-way classifier consisting in a support vector machine with polynomial kernel and a two-layer neural network. The last implements a vote procedure. Wavelet entropy was evaluated together with the sample entropy and approximated entropy to choose the one that best detected changes in the complexity of the EEG signal. The results show that it is possible to automatically classify a subject of a particular group with an overall accuracy of 92.6%, close to the best result found in the literature that is 97.9%. The method could be the basis for the implementation of a diagnosis-support quantitative tool oriented to aid in clinical diagnosis, especially when the classification between the three groups is not one of the more represented researches in the consulted literature.
Discrete wavelet transform EEG features of Alzheimer'S disease in activated states
Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference, 2012
In this study, electroencephalogram (EEG) signals obtained by a single-electrode device from 24 subjects - 10 with Alzheimer's disease (AD) and 14 age-matched Controls (CN) - were analyzed using Discrete Wavelet Transform (DWT). The focus of the study is to determine the discriminating EEG features of AD patients while subjected to cognitive and auditory tasks, since AD is characterized by progressive impairments in cognition and memory. At each recording block, DWT extracts EEG features corresponding to major brain frequency bands. T-test and Kruskal-Wallis methods were used to determine the statistically significant features of EEG signals from AD patients compared to Controls. A decision tree algorithm was then used to identify the dominant features for AD patients. It was determined that the mean value of the low-δ (1 - 2 Hz) frequency band during the Paced Auditory Serial Addition Test with 2.0 (s) interval and the mean value of the δ frequency band (12 - 30 Hz) during 6 Hz...
Early recognition of Alzheimer's disease in EEG using recurrent neural network and wavelet transform
Wavelet Applications in Signal and Image Processing VIII, 2000
The diagnosis of Alzheimer's disease (AD) at the present time remains dependent upon clinical symptomatology. Lifetime accuracy in the best clinics remains 86-89%, and mean diagnostic delay in the clinical course of the disease remains 3.6 years after symptomatic onset. Although EEG is an obvious quantitative parameter related to the illness, it's limitation is the absence of an identified set of features that discriminates AD EEG abnormalities from those due to confounding conditions. As a consequence, no computerized method exists up to date that can reliably detect those abnormalities. The objective of this study is to develop a robust computerized method for early detection of AD in EEG. We explore the ability of specifically designed and trained recurrent neural networks (RNN), combined with wavelet preprocessing, to discriminate between EEGs of early onset AD patients and their age-matched control subjects. We have used a similar approach previously for predicting the onset of epileptic seizure in EEG . The RNNs are chosen because they can implement extremely nonlinear decision boundaries and possess memory of the state, which is crucial for the considered task. The results on eyes-closed resting EEG reveal particularly favorable network behavior when applied to wavelet-filtered subbands as opposed to original signal data.