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الرئيسية > Classification Of EEG Signals...>الوصف

Classification Of EEG Signals Using Adaptive Weighted Distance Nearest Neighbor Algorithm

المصدر: مجلة جامعة الملك سعود - علوم الحاسب والمعلومات
الناشر: جامعة الملك سعود
المؤلف الرئيسي: Parvinnia, E. (Author)
مؤلفين آخرين: Jahromi, M. Zolghadri (Co-Author), Boostani, R. (Co-Author), Sabeti, M. (Co-Author)
المجلد/العدد: مج26, ع1
محكمة: نعم
الدولة: السعودية
التاريخ الميلادي: 2014
الصفحات: 1 - 6
DOI: 10.33948/0584-026-001-001
ISSN: 1319-1578
رقم MD: 972985
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: science
مواضيع:
علوم الحاسوب | الخوارزميات | تخطيط الدماغ | الطب النفسي العصبي
كلمات المؤلف المفتاحية:
Nearest Neighbor | Noisy Training Data | EEG Signal Classification | Band Power | Fractal Dimension | Autoregressive Coefficient
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المستخلص: Electroencephalogram (EEG) signals are often used to diagnose diseases such as seizure, alzheimer, and schizophrenia. One main problem with the recorded EEG samples is that they are not equally reliable due to the artifacts at the time of recording. EEG signal classification algorithms should have a mechanism to handle this issue. It seems that using adaptive classifiers can be useful for the biological signals such as EEG. In this paper, a general adaptive method named weighted distance nearest neighbor (WDNN) is applied for EEG signal classification to tackle this problem. This classification algorithm assigns a weight to each training sample to control its influence in classifying test samples. The weights of training samples are used to find the nearest neighbor of an input query pattern. To assess the performance of this scheme, EEG signals of thirteen schizophrenic patients and eighteen normal subjects are analyzed for the classification of these two groups. Several features including, fractal dimension, band power and autoregressive (AR) model are extracted from EEG signals. The classification results are evaluated using Leave one (subject) out cross validation for reliable estimation. The results indicate that combination of WDNN and selected features can significantly outperform the basic nearest-neighbor and the other methods proposed in the past for the classification of these two groups. Therefore, this method can be a complementary tool for specialists to distinguish schizophrenia disorder.
ISSN: 1319-1578

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