TR Dizin İndeksli Yayınlar Koleksiyonu / TR Dizin Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/20.500.14365/4
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Article Predictors of Gross Motor Function Level in Spastic Type Cerebral Palsy: a Retrospective Study(Turkey Assoc Physiotherapists, 2024-12-23) Ayaz Tas, Seda; Yakıt Yeşilyurt, Seda; Birinci Olgun, Tansu; Danis, Aysegul; Olgun, Tansu BİRİNCİ; Yeşi̇Lyurt, Seda YAKIT; Yakit Yebilyurt, Seda; Taş, Seda AYAZ; Birinci, TansuPurpose: This study was conducted to identify the determinants of gross motor function in patients with spastic-type Cerebral Palsy (CP) who received physiotherapy from a single center for two years. Methods: One hundred and eight children with spastic-type CP (mean age: 6.43 +/- 4.83 years) were evaluated twice, before and after the two-year physiotherapy. The outcomes were the Gross Motor Function Classification System (GMFCS), Manual Ability Classification System (MACS), Communication Function Classification System (CFCS), and Eating and Drinking Ability Classification System (EDACS). Binary logistic regression analysis was used to determine whether factors such as age, sex, topographical distribution, and levels of GMFCS, MACS, CFCS, and EDACS could predict the improvement in GMFCS level after the two-year physiotherapy. Results: The odds ratio of improvement in GMFCS level was found to vary significantly with the topographical distribution, CFCS level, and EDACS level (p<0.05). Compared to the children with CFCS Level I, children with CFCS Level II, Level III, and Level IV were 0.001, 0.005, and 0.006 times less likely to improve in GMFCS level, respectively. Similarly, children with EDACS Level III and Level IV were respectively 1.605 and 1.548 times less likely to improve in GMFCS level compared to those with Level I. Conclusion: CFCS and EDACS were significant predictors of gross motor function level in spastic- type CP. Healthcare professionals can use CFCS and EDACS to predict the progression of gross motor function levels, thereby providing more appropriate interventions and more realistic predictions.Article Citation - WoS: 11Citation - Scopus: 14Evaluation of Mother Wavelets on Steady-State Visually-Evoked Potentials for Triple-Command Brain-Computer Interfaces(Tubitak Scientific & Technical Research Council Turkey, 2021-09-23) Sayilgan, Ebru; Yuce, Yilmaz Kemal; Isler, YalcinWavelet transform (WT) is an important tool to analyze the time-frequency structure of a signal. The WT relies on a prototype signal that is called the mother wavelet. However, there is no single universal wavelet that fits all signals. Thus, the selection of mother wavelet function might be challenging to represent the signal to achieve the optimum performance. There are some studies to determine the optimal mother wavelet for other biomedical signals; however, there exists no evaluation for steady-state visually-evoked potentials (SSVEP) signals that becomes very popular among signals manipulated for brain-computer interfaces (BCIs) recently. This study aims to explore, if any, the mother wavelet that suits best to represent SSVEP signals for classification purposes in BCIs. In this study, three common wavelet-based features (variance, energy, and entropy) extracted from SSVEP signals for five distinct EEG frequency bands (delta, theta, alpha, beta, and gamma) were classified to determine three different user commands using six fundamental classifier algorithms. The study was repeated for six different commonly-used mother wavelet functions (haar, daubechies, symlet, coiflet, biorthogonal, and reverse biorthogonal). The best discrimination was obtained with an accuracy of 100% and the average of 75.85%. Besides, ensemble learner gives the highest accuracies for half of the trials. Haar wavelet had the best performance in representing SSVEP signals among other all mother wavelets adopted in this study. Concomitantly, all three features of energy, variance, and entropy should be used together since none of these features had superior classifier performance alone.