Wos Kapalı Erişimli Yayınlar

Permanent URI for this collectionhttps://hdl.handle.net/11727/10753

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Subject: search.filters.subject.Continuous wavelet transform

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    Detection of multiple sclerosis from photic stimulation EEG signals
    (2021) Karaca, Busra Kubra; Aksahin, Mehmet Feyzi; Ocal, Ruhsen
    Background: Multiple Sclerosis (MS) is characterized as a chronic, autoimmune and inflammatory disease of the central nervous system. Early diagnosis of MS is of great importance for the treatment and course of the disease. In addition to the many methods, cost-effective and non-invasive electroencephalogram signals may contribute to the pre-diagnosis of MS. Objectives: The aim of this paper is to classify male subjects who have MS and who are healthy control using photic stimulation electroencephalogram signals. Methods: Firstly the continuous wavelet transformation (CWT) method was applied to electroencephalogram signals under photic stimulation with 5Hz, 10Hz, 15Hz, 20Hz, and 25Hz frequencies. The sum, maximum, minimum and standard deviation values of absolute CWT coefficients, corresponding to "1-4 Hz" and "4-13 Hz" frequency ranges, were extracted in each stimulation frequency region. The ratios of these values obtained from the frequency ranges "1-4Hz" and "4-13Hz" was decided as features. Finally, various machine learning classifiers were evaluated to test the effectivity of determined features. Results: Consequently, the overall accuracy, sensitivity, specificity and positive predictive value of the proposed algorithm were 80 %, 72.7 %, 88.9 %, and 88.9 %, respectively by using the Ensemble Subspace k-NN classifier algorithm. Conclusions: The results showed how photic stimulation electroencephalogram signals can contribute to the prediagnosis of MS.
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    An efficient automatic arousals detection algorithm in single channel EEG
    (2019) Ugur, Tugce Kantar; Erdamar, Aykut; 0000-0001-8588-480X; 31046987; AAA-6844-2019
    Background and objective: Electroencephalographic arousal is a transient waveform that instantaneously happens in sleep as an inherent component. It has distinctive amplitude and frequency features. However, it is visually difficult to distinguish arousal from the background of the electroencephalogram. This visual scoring is important for brain researches, sleep studies, sleep stage scorings and assessment of sleep disorders. The scoring process is a time-consuming and difficult clinical procedure which is evaluated by sleep experts. It may also have subjective consequences due to the variability of personal expertise of physicians. Conversely, this scoring process can be significantly accelerated with computer-aided automated algorithms. Moreover, reproducible and objective results can be obtained. In this work, we propose a novel algorithm for the automatic detection of electroencephalographic arousals in sleep polysomno-graphic recordings. Methods: The approach uses a well-known time-frequency localization method, the continuous wavelet transform, to identify relevant arousal patterns. Special emphasis was carried out to produce a robust, reliable, fast and artifact tolerant algorithm. In the first part, the electroencephalographic scalogram, the squared magnitude of the continuous wavelet transform, was obtained. The mean and variance of the scalogram coefficients were determined as novel features. Support vector machine was applied as a classifier. Half of the recordings were used for training with five-fold cross-validation and a high accuracy training rate was obtained. Then, the rest of the recordings were used for testing. Results: As a result, the overall sensitivity, specificity, accuracy, and positive predictive value of the algorithm are 94.67%, 99.33%, 98.2%, and 97.93%, respectively. Conclusion: In this paper, we have shown that the electroencephalographic arousal pattern can be characterized by the scalogram in the wavelet domain. The proposed algorithm works with high accuracy, reproducibility and gives objective results without case-specific sensitivity. (C) 2019 Elsevier B.V. All rights reserved.