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BAŞPINAR, ULVİ

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BAŞPINAR

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ULVİ

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    PublicationOpen Access
    Detection of fetal electrocardiogram signals from maternalabdominal ecg recordings
    (2023-03-01) BAŞPINAR, ULVİ; BAŞPINAR U., KÖYLÜ Y.
    Fetal electrocardiogram (fECG) is a signal that contains vital information about the health of the fetus throughout pregnancy. During pregnancy, it is important to monitor and analyse this signal because it represents the electrical activity of the developing fetal heart. Early detection of fetal ECG problems during the fetus' development is crucial because it allows early treatment and provides knowledge about diseases that may emerge at a later time. Extraction of fetal ECG from the abdomen ECG signal is valuable in these aspects. In order to extract the fetal ECG from the recorded abdomen ECG signals correctly, it must be handled appropriately. It could be challenging to separate the fetal ECG signal from other physiological artifacts and noises in the mother abdominal signal. In this study, signal processing techniques were used to separate the fetus ECG signal from real abdominal ECG recordings. These methods include Ensemble Empirical Based Denoising, Finite Impulse Response Filter, Independent Component Analysis, and Pan & Tompkins approach. The results show that utilizing only the ICA technique to extract fECG signals is insufficient and that additional algorithms, such as those indicated above, should be used together. The mECG and fECG signals can be successfully extracted using the suggested approach.
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    Developing a diabetes management system integrated with salivary glucose sensors
    (2023-05-16) BAŞPINAR, ULVİ; SATMAN İ., ÖZTÜRK S., BAŞPINAR U., KARAHOCA A., CANDAN Z., GEZER M., ÇALIKOĞLU B. F., PALANDUZ A., KUBAT ÜZÜM A., HACİŞAHİNOĞULLARI H., et al.
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    El rehabilitasyonu için geliştirilen robotik harici el iskelet sisteminin sağlıklı bireyler üzerinde test edilmesi
    (2021-04-17) ACAR, GÖNÜL; BAL, ABDULLAH; BAŞPINAR, ULVİ; BAKIR, BARKIN; Yıldız E., Kısacık M., BAL A., BAKIR B., BAŞPINAR U., Bayrak F., ACAR G.
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    PublicationOpen Access
    Classification of motor imagery eeg signals for using in neuro-rehabilitation applications
    (2022-10-21) BAŞPINAR, ULVİ; BAŞPINAR U., Taştan Y., Bulut Okay B.
    Brain computer interfaces which are developed for the rehabilitation systems decode motor imagery EEG signals to control external devices. However, the extraction of the features from the EEG imagery signals and classification of it is an important problem. In this paper, common spatial pattern analysis, which is widely used in motor image applications, was preferred for getting features. As a classifier, the accuracy performances of Artificial neural network (%93), Convolutional Neural Network (%91), Support Vector Machine (%84) and K- Nearest Neighbour Algorithm (%90) were compared. As a result of the comparison, Artificial Neural Network method was the most successful classifier with %93.9 accuracy.
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    PublicationOpen Access
    The Usage of Constrained Independent Component Analysis to Reduce Electrode Displacement Effects in Real-Time Surface Electromyography-Based Hand Gesture Classifications
    (2022-12-01) BAŞPINAR, ULVİ; BAŞPINAR U., Taştan Y., VAROL H. S.
    Aim: In real-time control of prosthesis, orthosis, and human–computer interface applications, the displacement of surface electrodes may cause a total disruption or a decline in the classification rates. In this study, a constrained independent component analysis (cICA) was used as an alternative method for addressing the displacement problem of surface electrodes. Materials and Methods: The study was tested by classifying six‑hand gestures offline and in real‑time to control a robotic arm. The robotic arm has five degrees of freedom, and it was controlled using surface electromyography (sEMG) signals. The classification of sEMG signals is realized using artificial neural networks. cICA algorithm was utilized to improve the performance of classifiers due to the negative effect of electrode displacement issues. Results: In the study, the classification results of the cICA applied and unapplied sEMG signals were compared. The results showed that the proposed method has provided an increase between 4% and 13% in classifications. The average classification rates for six different hand gestures were calculated as 96.66%. Conclusions: The study showed that the cICA method enhances classification rates while minimizing the impact of electrode displacement. The other advantage of the cICA algorithm is dimension reduction, which is important in real time applications. To observe the performance of the cICA in the real-time application, a robotic arm was controlled using sEMG signals. Keywords: Classifier, constrained independent component analysis, electrode, surface electromyography
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    Eli̇ dışardan destekleyen roboti̇k el rehabi̇li̇tasyon si̇stemi̇ni̇n İnmeli̇ hastalarda etki̇nli̇ği
    (2022-07-08) ACAR, GÖNÜL; BAŞPINAR, ULVİ; Bayrak F., ACAR G., BAŞPINAR U.
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    PublicationOpen Access
    Investigation of the effects of different arm positions and angles in sEMG-based hand gesture recognition on classification success
    (2024-01-01) BAŞPINAR, ULVİ; PARLAK E., BAŞPINAR U.
    Günlük yaşamda etkin bir şekilde kullanılması için tasarlanan yüzeysel elektromiyografi (sEMG) sinyal tabanlı kontrol edilen aktif protezler ve insan makine etkileşimli sistemlerin kolun farklı açı ve pozisyonlarında da yüksek doğrulukta çalışması önemlidir. Bu amaçla çalışmada kolun üç farklı pozisyonunda ve üç farklı açıda alınan sEMG kayıtları, ivme ve jiroskop verileri ile desteklenerek dört farklı el hareketinin sınıflandırılması yapılmıştır. Sınıflandırmada kullanılan veri (8 kanal sEMG, ivme ve jiroskop) 13 katılımcının sağ ön kolundan alınmıştır. Çalışmada sEMG sinyallerinden altı adet, ivme ve jiroskop verilerinden de üç adet özellik kullanılarak veri seti oluşturulmuştur. Sonuç olarak, farklı kol pozisyonlarının ve açılarının el hareketlerinin sınıflandırılmasını nasıl etkilediği metodik olarak araştırılmıştır. Hareketin yapıldığı farklı kol pozisyonları ve açılar nedeniyle ortaya çıkan olumsuz etkilerin ivme ve jiroskop verileri kullanılarak giderilip giderilemeyeceği noktasında da değerlendirmeler yapılarak sınıflandırıcı performanslarına etkilerine yer verilmiştir. Sınıflandırıcı olarak Yapay Sinir Ağları (YSA) ve Destek Vektör Makineleri (DVM) tercih edilmiştir. Beş farklı kategoride yapılan incelemelerde DVM sınıflandırıcıları ortalama %83 başarı sağlarken YSA sınıflandırıcıları ortalama %82 başarı sağlamaktadır. Farklı pozisyonlarda ivme ve jiroskop verilerinin hareket sınıflama performansına çok az bir katkı sunduğu belirlenmiştir. Yapılan değerlendirme sonucu sEMG tabanlı bir sistemin eğitiminde ön kolun tüm pozisyon ve açılarında eğitim verisinin toplanması sınıflandırma sonuçlarını iyileştirdiği tespit edilmiştir.