A Pilot Study on Visually Stimulated Cognitive Tasks for EEG-Based Dementia Recognition (original) (raw)
Related papers
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.
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.
Alzheimer’s disease (AD) is one of the most common and fastest growing neurodegenerative diseases in the western countries. Development of different biomarkers tools are key issues for the diagnosis of AD and its progression. Prediction of cognitive performance of subjects from electroencephalography (EEG) and identification of relevant biomarkers are some of the research problems. Although EEG is a powerful and relatively cheap tool for the diagnosis of AD and dementia, it does not achieve the standards of clinical performance in terms of sensitivity and specificity to accept as a reliable technique for the screening of AD. Hence, there is an immense need to develop an efficient system and algorithms for diagnosis. Accordingly, the objective of this research paper is to analyze different features for the diagnosis of AD to serve as a possible biomarker to distinguish between AD subject and normal subject. The research is carried out on an experimental database. Three different features such as spectral-, wavelet-, and complexity-based features are computed and compared on the basis of classification accuracy obtained. The classification is carried out using support vector machine classifier giving 96% accuracy on complexity-based features and increased performance in terms of sensitivity and specificity. The results show the improved performance in the diagnosis of AD. It is observed that the severity of AD is depicted in EEG complexity. These features used in research work can be considered as the benchmark for AD diagnosis.
Exploring Classification in Open and Closed Eyes EEG Data for People with Cognitive Disorders
Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies
Cognitive disorders, including Alzheimer's Disease (AD), are health issues concerning all society. The evolution of technology and Artificial Intelligence (AI)/ Machine Learning (ML) in the health domain promises an earlier and more accurate diagnosis for Alzheimer's disease and Dementia. In this study, we examine Healthy patients and patients with AD and Mild Cognitive Impairment (MCI), often a prior step of AD. With the use of EEG, we collect data from their brain activity. After a basic processing step, kernel PCA is applied as a dimensionality reduction method using segments of the multichannel signal, and the transformation output is employed as input for the predictive model. Machine learning functions are used to classify data correctly into Healthy, AD, MCI classes, and a postprocessing step allows for classification at the patient level. The results show that the algorithm can predict with an accuracy of 90 percent and more in total, AD or MCI patients vs. Healthy patients.
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.
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...
Dementia and geriatric cognitive disorders, 2015
The aim of this study was to examine the discriminatory power of quantitative EEG (qEEG) applying the statistical pattern recognition (SPR) method to separate Alzheimer's disease (AD) patients from elderly individuals without dementia and from other dementia patients. The participants were recruited from 6 Nordic memory clinics: 372 unselected patients [mean age 71.7 years (SD 8.6), 54% women] and 146 healthy elderly individuals [mean age 66.5 years (SD 7.7), 60% women]. After a standardized and comprehensive assessment, clinical diagnoses were made according to internationally accepted criteria by at least 2 clinicians. EEGs were recorded in a standardized way and analyzed independently of the clinical diagnoses, using the SPR method. In receiver operating characteristic curve analyses, the qEEGs separated AD patients from healthy elderly individuals with an area under the curve (AUC) of 0.90, representing a sensitivity of 84% and a specificity of 81%. The qEEGs further separat...
Role of EEG as Biomarker in the Early Detection and Classification of Dementia
The early detection and classification of dementia are important clinical support tasks for medical practitioners in customizing patient treatment programs to better manage the development and progression of these diseases. Efforts are being made to diagnose these neurodegenerative disorders in the early stages. Indeed, early diagnosis helps patients to obtain the maximum treatment benefit before significant mental decline occurs. The use of electroencephalogram as a tool for the detection of changes in brain activities and clinical diagnosis is becoming increasingly popular for its capabilities in quantifying changes in brain degeneration in dementia. This paper reviews the role of electroencephalogram as a biomarker based on signal processing to detect dementia in early stages and classify its severity. The review starts with a discussion of dementia types and cognitive spectrum followed by the presentation of the effective preprocessing denoising to eliminate possible artifacts. It continues with a description of feature extraction by using linear and nonlinear techniques, and it ends with a brief explanation of vast variety of separation techniques to classify EEG signals. This paper also provides an idea from the most popular studies that may help in diagnosing dementia in early stages and classifying through electroencephalogram signal processing and analysis.
EEG Signal Processing and Supervised Machine Learning to Early Diagnose Alzheimer’s Disease
Applied Sciences
Electroencephalography (EEG) signal analysis is a fast, inexpensive, and accessible technique to detect the early stages of dementia, such as Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD). In the last years, EEG signal analysis has become an important topic of research to extract suitable biomarkers to determine the subject’s cognitive impairment. In this work, we propose a novel simple and efficient method able to extract features with a finite response filter (FIR) in the double time domain in order to discriminate among patients affected by AD, MCI, and healthy controls (HC). Notably, we compute the power intensity for each high- and low-frequency band, using their absolute differences to distinguish among the three classes of subjects by means of different supervised machine learning methods. We use EEG recordings from a cohort of 105 subjects (48 AD, 37 MCI, and 20 HC) referred for dementia to the IRCCS Centro Neurolesi “Bonino-Pulejo” of Messina, Italy. The find...
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