We describe an ensemble of classifiers based data fusion approach to combine information from two sources, believed to contain complimentary information, for early diagnosis of Alzheimer's disease. Specifically, we use the event related potentials recorded from the Pz and Cz electrodes of the EEG, which are further analyzed 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 weighted majority voting. Several factors set this study apart from similar prior efforts: we use a larger cohort, specifically target early diagnosis of the disease, use an ensemble based approach rather then a single classifier, and most importantly, we combine information from multiple sources, rather then using a single modality. We present promising results obtained from the first 35 (of 80) patients whose data are analyzed thus far.
-Automated classification is often used in advanced systems to monitor system events. All data, and hence features from all sensors, must be present in order to make a meaningful classification. An ensemble approach, Learn ++ .MF, was recently introduced that allows classification with up to 10% of feature missing, where several classifiers are trained on random subsets of the available sensor data. Given an instance with missing features, only those classifiers trained with the available features are then used in classification. In this paper, we present a modified approach that accommodates up to 30% missing features along with the effect of varying algorithm parameters.
With the number of the elderly population affected by Alzheimer's disease (AD) rising, the need to find an accurate, inexpensive and non-intrusive procedure that can be made available to community healthcare providers for early diagnosis of Alzheimer's disease is becoming more and more urgent as a major health concern. Several recent studies have looked at analyzing electroencephalogram signals through the use of wavelets and neural networks. In this study, multiresolution wavelet analysis, coupled with the ensemble of classifiers based boosting algorithm is used on the P300 component of the event related potentials (ERP) to determine the feasibility of the approach as a diagnostic tool for early diagnosis of AD. The technique and its promising initial results are presented.
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.
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