EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease (original) (raw)
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Combining EEG signal processing with supervised methods for Alzheimer's patients classification
BMC medical informatics and decision making, 2018
Alzheimer's Disease (AD) is a neurodegenaritive disorder characterized by a progressive dementia, for which actually no cure is known. An early detection of patients affected by AD can be obtained by analyzing their electroencephalography (EEG) signals, which show a reduction of the complexity, a perturbation of the synchrony, and a slowing down of the rhythms. In this work, we apply a procedure that exploits feature extraction and classification techniques to EEG signals, whose aim is to distinguish patient affected by AD from the ones affected by Mild Cognitive Impairment (MCI) and healthy control (HC) samples. Specifically, we perform a time-frequency analysis by applying both the Fourier and Wavelet Transforms on 109 samples belonging to AD, MCI, and HC classes. The classification procedure is designed with the following steps: (i) preprocessing of EEG signals; (ii) feature extraction by means of the Discrete Fourier and Wavelet Transforms; and (iii) classification with tree...
Alzheimer's disease patients classification through EEG signals processing
Alzheimer's Disease (AD) and its preliminary stage -Mild Cognitive Impairment (MCI) -are the most widespread neurodegenerative disorders, and their investigation remains an open challenge. ElectroEncephalography (EEG) appears as a non-invasive and repeatable technique to diagnose brain abnormalities. Despite technical advances, the analysis of EEG spectra is usually carried out by experts that must manually perform laborious interpretations. Computational methods may lead to a quantitative analysis of these signals and hence to characterize EEG time series. The aim of this work is to achieve an automatic patients classification from the EEG biomedical signals involved in AD and MCI in order to support medical doctors in the right diagnosis formulation. The analysis of the biological EEG signals requires effective and efficient computer science methods to extract relevant information. Data mining, which guides the automated knowledge discovery process, is a natural way to approach EEG data analysis. Specifically, in our work we apply the following analysis steps: (i) pre-processing of EEG data; (ii) processing of the EEG-signals by the application of time-frequency transforms; and (iii) classification by means of machine learning methods. We obtain promising results from the classification of AD, MCI, and control samples that can assist the medical doctors in identifying the pathology.
In this paper, we present a method to detect the early stages of Alzheimer’s disease (AD) based on electroencephalogram (EEG) feature extraction. We used a multiway analysis to extract the spatial-frequency characteristics for classification of subjects. The filters obtained from a parallel factor analysis (PARAFAC) model were used to describe the groups and reduce their description to meaningful features in frequency and space, helping the identification of subjects developing Alzheimer’s disease. We analyzed 20-second steady state, artifact free EEG time series recorded over 21 leads from age-matched subjects. The subject database included 38 controls, 22 mild cognitive impairment subjects (MCI) and 23 Alzheimer’s disease patients (AD). We applied a multiway analysis based on the PARAFAC model to extract the multilinear interactions between groups, frequency, and space. In a divide and conquer scheme, we obtained a classification accuracy of 74.7% comparing the control subjects to the demented subjects, and we obtained a classification accuracy of 75.6% comparing MCI subjects to AD patients. This approach combined the multilinear interaction within the tensor formed by subjects X frequency power X regions and provided an interesting interpretation and characterization of Alzheimer’s disease in the early stages from a simple set of features. The multiway modeling of EEG recordings applied to the characterization and classification of Alzheimer’s disease patients in the early stages has not been employed as yet. Even though the classification results are modest compared with the available literature, this method could help extract more interesting features as well as summarize information for classification or diagnosis at a higher level than subject-by-subject EEG analysis. This method, if combined with other features, could reveal itself to be very promising for diagnosing Alzheimer’s patients in the early stages. Moreover, it can be easily generalized as well as improved by numerous linear and nonlinear features of EEGs.
Analyzing EEG Signals with Machine Learning for Diagnosing Alzheimer’s Disease
Abstract-In order to have the greatest treatment impact the early and accurate diagnose of Alzheimer's disease (AD) is essential. In this paper we present a method for analyzing EEG signals with machine learning approach in order to diagnose AD. We show how to extract features out of EEG recordings to be used with a machine learning algorithm for the induction of AD classification model. The obtained results are very promising.
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010
This paper describes a methodology used to classify Alzheimer's disease (AD) and mild cognitive impairment (MCI) with high accuracy using EEG data. The sequential forward floating search (SFFS) was used to select features from relative average power for channel locations in frequency bands delta, theta, alpha, and beta, and coherence between intrahemispheric channel pairs for the same frequency ranges. The selected feature sets allowed us to achieve close to 90% classifier accuracy when classifying MCI patients and normal subjects. Our results showed that selecting features from a combined set of power and coherence features produced better results than the use of either feature independently. The combined feature set also showed better classification rates than a Bayesian classifier fusion approach.
Signal Processing Methods of Diagnosing Alzheimer ’ s Disease Using EEG
Alzheimer's is the most common form of Dementia prevailing in the elderly people. This review paper aims to put forward recent developments in the diagnosis of Alzheimer's disease (AD) using Electroencephalograms (EEG). The extraction of useful information from rough EEG signal using only mathematical algorithm is a tough but promising task. Various modern techniques have enhanced the computerized analysis of EEG in elderly people. All these techniques can exploit the information contained in the EEG signals in time, frequency and time-frequency domain analyses. This work provides an integration of various time, frequency and time-frequency domain methods which facilitate the analysis independently as well as combined thus making it easier to analyze nonstationary and non-deterministic EEG signals. Among these various methods, time-frequency domain tools offer most efficient methods as it can uncover features that remain invisible when only time or frequency domain methods are used. Several of the methods discussed here can be utilized to develop an efficient algorithm for early detection of Alzheimer's disease.
Entropy
The discrimination of early Alzheimer's disease (AD) and its prodromal form (i.e., mild cognitive impairment, MCI) from cognitively healthy control (HC) subjects is crucial since the treatment is more effective in the first stages of the dementia. The aim of our study is to evaluate the usefulness of a methodology based on electroencephalography (EEG) to detect AD and MCI. EEG rhythms were recorded from 37 AD patients, 37 MCI subjects and 37 HC subjects. Artifact-free trials were analyzed by means of several spectral and nonlinear features: relative power in the conventional frequency bands, median frequency, individual alpha frequency, spectral entropy, Lempel-Ziv complexity, central tendency measure, sample entropy, fuzzy entropy, and auto-mutual information. Relevance and redundancy analyses were also conducted through the fast correlation-based filter (FCBF) to derive an optimal set of them. The selected features were used to train three different models aimed at classifying the trials: linear discriminant analysis (LDA), quadratic discriminant analysis (QDA) and multi-layer perceptron artificial neural network (MLP). Afterwards, each subject was automatically allocated in a particular group by applying a trial-based majority vote procedure. After feature extraction, the FCBF method selected the optimal set of features: individual alpha frequency, relative power at delta frequency band, and sample entropy. Using the aforementioned set of features, MLP showed the highest diagnostic performance in determining whether a subject is not healthy (sensitivity of 82.35% and positive predictive value of 84.85% for HC vs. all classification task) and whether a subject does not suffer from AD (specificity of 79.41% and negative predictive value of 84.38% for AD vs. all comparison). Our findings suggest that our methodology can help physicians to discriminate AD, MCI and HC.
EEG Window Length Evaluation for the Detection of Alzheimer’s Disease over Different Brain Regions
Brain Sciences
Alzheimer’s Disease (AD) is a neurogenerative disorder and the most common type of dementia with a rapidly increasing world prevalence. In this paper, the ability of several statistical and spectral features to detect AD from electroencephalographic (EEG) recordings is evaluated. For this purpose, clinical EEG recordings from 14 patients with AD (8 with mild AD and 6 with moderate AD) and 10 healthy, age-matched individuals are analyzed. The EEG signals are initially segmented in nonoverlapping epochs of different lengths ranging from 5 s to 12 s. Then, a group of statistical and spectral features calculated for each EEG rhythm (δ, θ, α, β, and γ) are extracted, forming the feature vector that trained and tested a Random Forests classifier. Six classification problems are addressed, including the discrimination from whole-brain dynamics and separately from specific brain regions in order to highlight any alterations of the cortical regions. The results indicated a high accuracy rang...
2018
Alzheimer's is one of the most costly illnesses that exists today and the number of people with Alzheimer's disease is expected to increase with 100 million until the year 2050 [2][9]. The medication that exists today is most effective if Alzheimer's is detected during early stages since these medications do not cure Alzheimer's but slows down the progression of the disease. Electroencephalography (EEG) is a relatively cheap method in comparison to for example Magnetic Resonance Imaging when it comes to diagnostic tools. However it is not clear how to deduce whether a patient has Alzheimer's disease just from EEG data when the analyst is a human. This is the underlying motivation for our investigation; can supervised machine learning methods be used for pattern recognition using only the spectral power of EEG data to tell whether an individual has Alzheimer's disease or not? The output accuracy of the trained supervised machine learning models showed an average accuracy of above 80%. This indicates that there is a difference in the neural oscillations of the brain between healthy individuals and Alzheimer's disease patients which the machine learning methods are able to detect using pattern recognition. iv Sammanfattning Alzheimers är en av de mest kostsamma sjukdomar som existerar idag och antalet människor med Alzheimer förväntas öka med omkring 100 miljoner människor tills 2050[9]. Den medicinska hjälp som finns tillgänglig idag är som mest effektiv om man upptäcker Alzheimers i ett tidigt stadium eftersom dagens mediciner inte botar sjukdomen utan fungerar som bromsmedicin. Elektroencefalografi är en relativt billig metod för diagnostisering jämfört med Magnetisk resonanstomografi. Det är emellertid inte tydligt hur en läkare eller annan tränad individ ska tolka EEG datan för att kunna avgöra om det är en patient med Alzheimers som de kollar på. Så den bakomliggande motivation till vår undersökning är; Kan man med hjälp av övervakad maskininlärning i kombination med spektral kraft från EEG datorn skapa modeller som kan avgöra om en patient har Alzheimers eller inte. Medelvärdet av våra modellers noggrannhet var över 80%. Detta tyder på att det finns en faktiskt skillnad mellan hjärna signalerna hos en patient med Alzheimers och en frisk individ, och att man med hjälp av maskininlärning kan hitta dessa skillnader som en människa enkelt missar. CONTENTS Conclusion 22 Bibliography 23
Journal of Neuroscience Methods, 2007
The early detection of subjects with probable Alzheimer's disease (AD) is crucial for effective appliance of treatment strategies. Here we explored the ability of a multitude of linear and non-linear classification algorithms to discriminate between the electroencephalograms (EEGs) of patients with varying degree of AD and their age-matched control subjects. Absolute and relative spectral power, distribution of spectral power, and measures of spatial synchronization were calculated from recordings of resting eyes-closed continuous EEGs of 45 healthy controls, 116 patients with mild AD and 81 patients with moderate AD, recruited in two different centers (Stockholm, New York). The applied classification algorithms were: principal component linear discriminant analysis (PC LDA), partial least squares LDA (PLS LDA), principal component logistic regression (PC LR), partial least squares logistic regression (PLS LR), bagging, random forest, support vector machines (SVM) and feed-forward neural network. Based on 10-fold cross-validation runs it could be demonstrated that even tough modern computer-intensive classification algorithms such as random forests, SVM and neural networks show a slight superiority, more classical classification algorithms performed nearly equally well. Using random forests classification a considerable sensitivity of up to 85% and a specificity of 78%, respectively for the test of even only mild AD patients has been reached, whereas for the comparison of moderate AD vs. controls, using SVM and neural networks, values of 89% and 88% for sensitivity and specificity were achieved. Such a remarkable performance proves the value of these classification algorithms for clinical diagnostics.