Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/11727/4809
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Item CNN-Based Severity Prediction Of Neurodegenerative Diseases Using Gait Data(2022) Erdas, Cagatay Berke; Sumer, Emre; Kibaroglu, Seda; https://orcid.org/0000-0003-3467-9923; 35111334; AGA-5711-2022Neurodegenerative diseases occur because of degeneration in brain cells but can manifest as impairment of motor functions. One of the side effects of this impairment is an abnormality in walking. With the development of sensor technologies and artificial intelligence applications in recent years, the disease severity of patients can be estimated using their gait data. In this way, decision support applications for grading the severity of the disease that the patient suffers in the clinic can be developed. Thus, patients can have treatment methods more suitable for the severity of the disease. The presented research proposes a deep learning-based approach using gait data represented by a Quick Response code to develop an effective and reliable disease severity grading system for neurodegenerative diseases such as amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. The two-dimensional Quick Response data set was created by converting each one-dimensional gait data of the subjects with a novel representation approach to a Quick Response code. This data set was regressed with the convolutional neural network deep learning method, and a solution was sought for the problem of grading disease severity. Further, to demonstrate the success of the results obtained with the novel approach, native machine learning approaches such as Multilayer Perceptron, Random Forest, Extremely Randomized Trees, and K-Nearest Neighbours, and ensemble machine learning methods, such as voting and stacking, were applied on one-dimensional data. Finally, the results obtained on the prediction of disease severity by testing one-dimensional gait data with a convolutional neural network architecture that operates on one-dimensional data were included. The results showed that, in most cases, the two-dimensional convolutional neural network approach performed the best among all methods.Item Functional electrical stimulation cycling in patients with chronic spinal cord injury: a pilot study(2021) Gurcay, Eda; Karaahmet, Ozgur Zeliha; Cankurtaran, Damla; Nazli, Fatma; Umay, Ebru; Guzel, Sukran; Gurcay, Ahmet Gurhan; 0000-0001-9852-0917; 33998960; AAG-3148-2021Objective To examine the therapeutic value of lower extremity functional electrical stimulation (FES) - evoked cycling on functional independence, health status, gait parameters, pulmonary functions, and biochemical values in patients with chronic complete/incomplete spinal cord injury (SCI). Materials and Methods Fifteen patients with SCI (duration of more than 6 months) who were able to stand up and walk with long leg braces or assistive devices and had stable neurological status and trunk balance undertook FES cycling for 6 weeks (three times per week). The main outcomes were: Functional Independence Measure (FIM), Nottingham Health Profile (NHP), 6-minute walk test (6MWT), and 20-meter walk test (20MWT). Secondary outcomes include measurements of pulmonary function tests and biochemical values. All parameters were evaluated at the beginning and end of the program. Results Improvements were seen in motor and total scores of FIM (p = 0.007), physical mobility subscale of NHP (p = 0.011), 6MWT (p = 0.001), and 20MWT (p = 0.011). In pulmonary functions, only forced vital capacity (FVC) levels demonstrated a significant increase compared with baseline (p = 0.011). Biochemical values reached no significant level. Conclusion The results of this study showed that the FES cycling exercise program improves motor and total FIM scores, gait parameters, and FVC values of pulmonary functions in patients with chronic SCI experience. The FES cycle might be a valuable and well-tolerated intervention in clinical rehabilitation.