Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

Aim: Our primary aim is to capture and use the timings of the characteristic brain responses to olfactory stimulation for mild Alzheimer's disease diagnosis purposes. Proposed method: Our method identifies the timings of short-lived signal segments where characteristic distances between pre- and post-stimulus relative spectral energies are attained for each EEG channel and frequency band. These timings and timing-derived features were subsequently used in a leave-one-subject-out cross-validation scenario to assess the diagnostic performance of our framework. We evaluated seven distinct statistical distance measures to determine the most effective one for characterizing the neurological conditions of the subjects. Results: The average cross-validation performance shows that our framework achieved 87.50% diagnosis performance. The frequently used features were mainly derived from the delta and alpha activity of the prefrontal region (Fp1) and the beta activity of the parietal region (Pz), which agree with the current findings of olfaction biophysics. Comparison with existing methods: We compared the performance of our method with that of four existing methods in the literature. Our method outperformed these four methods. Moreover, our method elicited the highest accuracy when the clinical olfactory score (UPSIT) was included as a feature. Conclusions: Our analysis framework reveals a significant alteration of the timing organization of the brain that emerged upon olfactory stimulation in Alzheimer's patients. The timings of characteristic response and the features calculated via these timings contribute to Alzheimer's disease diagnosis performance remarkably. The perspective proposed here may facilitate early diagnosis, thereby facilitating the exploration of novel therapeutic and treatment strategies.

We present a novel formulation for pattern recognition in biomedical data. We adopt a binary recognition scenario where a control dataset contains samples of one class only, while a mixed dataset contains an unlabeled collection of samples from both classes. The mixed dataset samples that belong to the second class are identified by estimating posterior probabilities of samples for being in the control or the mixed datasets. Experiments on synthetic data established a better detection performance against possible alternatives. The fitness of the method in biomedical data analysis was further demonstrated on real multi-color flow cytometry and multi-channel electroencephalography data. © 2010 Elsevier Ltd. All rights reserved.

Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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