Mühendislik Fakültesi / Faculty of Engineering
Permanent URI for this collectionhttps://hdl.handle.net/11727/1401
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Item Automated Temporal Lobe Epilepsy And Psychogenic Nonepileptic Seizure Patient Discrimination From Multichannel EEG Recordings Using DWT Based Analysis(2022) Ficici, Cansel; Telatar, Ziya; Erogul, OsmanPsychogenic nonepileptic seizure (PNES) and epileptic seizure resemble each other, behaviorally. This similarity causes misdiagnosis of PNES and epilepsy patients, thus patients suffering from PNES may be treated with antiepileptic drugs which can have various side effects. Furthermore, seizure is diagnosed after time consuming examination of electroencephalography (EEG) recordings realized by the expert. In this study, automated temporal lobe epilepsy (TLE) patient, PNES patient and healthy subject discrimination method from EEG signals is proposed in order to eliminate the misdiagnosis and long inspection time of EEG recordings. Also, this study provides automated approach for TLE interictal and ictal epoch classification, and TLE, PNES and healthy epoch classification. For this purpose, subbands of EEG signals are determined from discrete wavelet transform (DWT), then classification is performed using ensemble classifiers fed with energy feature extracted from the subbands. Experiments are conducted by trying two approaches for TLE, PNES and healthy epoch classification and patient discrimination. Results show that in the TLE, PNES and healthy epoch classification the highest accuracy of 97.2%, sensitivity of 97.9% and specificity of 98.1% were achieved by applying adaptive boosting method, and the highest accuracy of 87.1%, sensitivity of 86.0% and specificity of 93.6% were attained using random under sampling (RUS) boosting method in the TLE patient, PNES patients and the healthy subject discrimination.Item A novel electroencephalography based approach for Alzheimer's disease and mild cognitive impairment detection(2021) Oltu, Burcu; Aksahin, Mehmet Feyzi; Kibaroglu, Seda; 0000-0002-3964-268X; AAJ-2956-2021Background and objective: Alzheimer's disease (AD) is characterized by cognitive, behavioral and intellectual deficits. The term mild cognitive impairment (MCI) is used to describe individuals whose cognitive impairment departing from their expectations for the age that does not interfere with daily activities. To diagnose these disorders, a combination of time-consuming, expensive tests that has difficulties for the target population are evaluated, moreover, the evaluation may yield subjective results. In the presented study, a novel methodology is developed for the automatic detection of AD and MCI using EEG signals. Methods: This study analyzed the EEGs of 35 subjects (16 MCI, 8 AD, 11 healthy control) with the developed algorithm. The algorithm consists of 3 methods for analysis, discrete wavelet transform(DWT), power spectral density (PSD) and coherence. In the first approach, DWT is applied to the signals to obtain major EEG sub-bands, afterward, PSD of each sub-band is calculated using Burg's method. In the second approach, interhemispheric coherence values are calculated. The variance and amplitude summation of each sub-bands' PSD and the amplitude summation of the coherence values corresponding to the major sub-bands are determined as features. Bagged Trees is selected as a classifier among the other tested classification algorithms. Data set is used to train the classifier with 5-fold cross-validation. Results: As a result, accuracy, sensitivity, and specificity of 96.5%, 96.21%, 97.96% are achieved respectively. Conclusion: In this study, we have investigated whether EEG can provide efficient clues about the neuropathology of Alzheimer's Disease and mild cognitive impairment for early and accurate diagnosis. Accordingly, a decision support system that produces reproducible and objective results with high accuracy is developed.