Browsing by Author "Ekmekçi, Ahmet Hakan"

Now showing 1 - 2 of 2

Automatic Phase Reversal Detection in Routine Eeg
(CHURCHILL LIVINGSTONE, 2020) Yıldırım, Sema; Koçer, Hasan Erdinç; Ekmekçi, Ahmet Hakan
Electroencephalograph (EEG), a valuable tool in the clinical evaluation, is readily available, safe and provides information about brain function. EEG interpretation is important for the diagnosis of neurological disorders. The long-term EEG data may be required to document and study neurosciences that include many epileptic activities and phase reversal (PR) etc. However, analyze of the long-term EEG done by an expert neurologist is much time consuming and quite difficult. Therefore, an automatic PR determination method for analyzing of long-term EEG is described in this study. The presented technique was applied to the pathological EEG recordings that were obtained from two different datasets gathered as a retrospective in Selcuk University Hospital (SUH) and Boston Children's Hospital (BCH). With this method, PR in the dataset was determined and then compared with the ones detected by the specialist doctor. Two tests were carried out in the SUH dataset and the classification success of the method was 83.22% for test 1 and 85.19% for test 2. On the other hand, three tests were carried out for two different position values for BCH dataset. The highest classification success of the six tests was 75% for test 5, while the lowest classification success appeared as 58.33% for test 6. As a result, the overall success in the detection of PR with the conducted method is 84.20% for SUH and 66.7% for BCH. According to these results, the determination of PR that is known to be indicative of neurological disorders and presenting them to expert information will accelerate the interpretation of long-term EEG recordings.
Citation - WoS: 2
Citation - Scopus: 2
Quantitative Analysis of Eeg Slow Wave Activity Based on Minpeakprominence Method
(INT INFORMATION & ENGINEERING TECHNOLOGY ASSOC, 2021) Yıldırım, Sema; Koçer, Hasan Erdinç; Ekmekçi, Ahmet Hakan
Persistent, unchanging, and non-reactive focal or generalized abnormal Slow Wave (SW) activities in an awake adult patient are examined pathologically. Although these waves in Electroencephalogram (EEG) are much less prominent than transient activities in some areas, it is not possible to understand them easily by looking at the EEG. For this reason, reliable computer programs that can sort out Slow Waves (SWs) correctly are needed. In this study, a new method based on MinPeakProminence that can detect abnormal SW activities was developed. To test the performance of the study, the data collected from Selcuk University Hospital (22 subjects - epilepsy and various neurological diseases) and Bonn Hospital (only normal A dataset) were used. Various statistical performance measurement methods were used to search the results. The results of this analysis revealed that the classification success, sensitivity and specificity values obtained with the SUH dataset were 96.5%, 93.3% and 96.1%, respectively. In the results of the experiments made with the Bonn dataset, 100% classification success was achieved. Besides, according to the analyses, it was found that SWs are frequently seen in the posterior regions of the brain, especially in the parietal and occipital regions in the SUH dataset.