Karaçalı, Bilge
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Karacali, B
Karaçalı, B.
Karaçalı, B
Karaçali, Bilge
Karacali, Bilge
Karaçali, B.
Karacali, B.
Karaçalı, B.
Karaçalı, B
Karaçali, Bilge
Karacali, Bilge
Karaçali, B.
Karacali, B.
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bilgekaracali@iyte.edu.tr
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03.05. Department of Electrical and Electronics Engineering
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61
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1873
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17
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34
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714
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21
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76784/19229
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5
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909
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1040
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17.48
Scopus Citations per Publication
20.00
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38
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11
| Journal | Count |
|---|---|
| 2010 15th National Biomedical Engineering Meeting, BIYOMUT2010 | 3 |
| Nature Biotechnology | 2 |
| Molecular Informatics | 2 |
| 2012 20th Signal Processing and Communications Applications Conference, SIU 2012, Proceedings | 2 |
| Biomedical Signal Processing and Control | 2 |
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Doctoral Thesis Beyin Fonksiyon Değişimlerini Elektroensefalografi ile Değerlendirmek için İşlemsel Bir Beyin Bağlantılılık Çerçevesi(2025) Onay, Fatih; Karaçalı, BilgeParkinson hastalığı, beynin sinirsel aktiviteyi esnek bir şekilde koordine etme yeteneğini bozar. Bu durum, özellikle bazal gangliya ve ona bağlı kortiko-talamik döngüleri içeren devrelerde görülür. Sağlıklı bir beyin, motor ve bilişsel görevler sırasında, verimli kaynak tahsisini yansıtan dinamik senkronizasyon ve desenkronizasyon örüntüleri gösterir. Parkinson hastalığında ise bu sinirsel mekanizmalar patolojik sinirsel dinamiklere karşı daha savunmasız hale gelir, bu da beynin sorunsuz bilişsel ve motor kontrol için gereken aktivasyon örüntüleri arasında verimli bir şekilde geçiş yapma yeteneğini zayıflatır. Bu tez, bu değişikliklerin kontrollü pedal çevirme görevi sırasında nasıl ortaya çıktığını sinirsel değişkenliği inceleyerek araştırmaktadır. Bu amaçla, Parkinson hastalarından (donma gösteren hastalar dahil - PDFOG) ve sağlıklı kontrol gruplarından alt ekstremite pedal çevirme görevi sırasında toplanan EEG kayıtları kullanılmıştır. Pedal çevirme görevi sırasında senkronizasyon ve değişkenlik örüntülerini saptamak için sinirsel dinamikleri denemeler arası tutarlılık (inter-trial coherence - ITC) ve entropi ölçümleri aracılığıyla analiz ettik. ITC analizi, sağlıklı kontrol grubunun frontoparietal ağlarda güçlü delta bandı senkronizasyonunu koruduğunu ortaya koyarken, Parkinson hastalarının düşük frekanslı ITC'de giderek kötüleşme gösterdiğini ve PDFOG hastalarının en ciddi azalmalara sahip olduğunu gösterdi. Hasta gruplarını ayırt eden biyobelirteçler olarak, sensorimotor hazırlık sırasında delta baskınlığından beta bandı aktivitesine doğru sistematik frekans kaymaları keşfedildi. Entropi analizi ile Parkinson hastalığı ilerledikçe azalan karmaşıklık ve bilgi işleme kapasitesi tespit edildi. Vasicek ve permütasyon entropi ölçümlerini kullanarak, frontal, parietal ve oksipital bölgelerde azalan sinirsel değişkenlik ve motor aktivite başlatma sırasında azalmış karmaşıklık tespit ettik. Her iki yaklaşım da, sağ parietal ve frontoparietal ağların hastalığa bağlı işlev bozukluğuna karşı savunmasız olduğunu gösterdi. Bu bulgular, sinirsel senkronizasyon ve karmaşıklıktaki denemeler arası değişkenliğin, hastalık ilerlemesi için hassas belirteçler olarak hizmet ettiğini ortaya koymaktadır. Motor-bilişsel ağlardaki sinirsel kararlılık ve adaptasyonun kademeli olarak bozulması, yürüme donması mekanizmalarına dair yeni bilgiler sunmaktadır. Böylece, elde edilen bulgular entropi ve ITC yöntemlerinin nörodejeneratif bozuklukların tespitinde EEG tabanlı biyobelirteç olarak kullanılabileceğini göstermiştir.Article Citation - WoS: 5Citation - Scopus: 8Usul and Makam Driven Automatic Melodic Segmentation for Turkish Music(Taylor and Francis Ltd., 2014) Bozkurt, Barış; Karaosmanoglu, M. Kemal; Karaçalı, Bilge; Ünal, ErdemAutomatic melodic segmentation is a topic studied extensively, aiming at developing systems that perform grouping of musical events. Here, we consider the problem of automatic segmentation via supervised learning from a dataset containing segmentation labels of an expert. We present a statistical classification-based segmentation system developed specifically for Turkish makam music. The proposed system uses two novel features, a makam-based and an usul-based feature, together with features commonly used in literature. The makam-based feature is defined as the probability of a note to appear at the phrase boundary, computed from the distributions of boundaries with respect to the piece’s makam pitches. Likewise, the usul-based feature is computed from the distributions of boundaries with respect to beats in the rhythmic cycle, usul of the piece. Several experimental setups using different feature groups are designed to test the contribution of the proposed features on three datasets. The results show that the new features carry complementary information to existing features in the literature within the Turkish makam music segmentation context and that the inclusion of new features resulted in statistically significant performance improvement.Article Citation - WoS: 3Citation - Scopus: 4Hierarchical Motif Vectors for Prediction of Functional Sites in Amino Acid Sequences Using Quasi-Supervised Learning(Institute of Electrical and Electronics Engineers Inc., 2012) Karaçalı, BilgeWe propose hierarchical motif vectors to represent local amino acid sequence configurations for predicting the functional attributes of amino acid sites on a global scale in a quasi-supervised learning framework. The motif vectors are constructed via wavelet decomposition on the variations of physico-chemical amino acid properties along the sequences. We then formulate a prediction scheme for the functional attributes of amino acid sites in terms of the respective motif vectors using the quasi-supervised learning algorithm that carries out predictions for all sites in consideration using only the experimentally verified sites. We have carried out comparative performance evaluation of the proposed method on the prediction of N-glycosylation of 55,184 sites possessing the consensus N-glycosylation sequon identified over 15,104 human proteins, out of which only 1,939 were experimentally verified N-glycosylation sites. In the experiments, the proposed method achieved better predictive performance than the alternative strategies from the literature. In addition, the predicted N-glycosylation sites showed good agreement with existing potential annotations, while the novel predictions belonged to proteins known to be modified by glycosylation.Conference Object Improved Quasi-Supervised Learning by Expectation-Maximization(Institute of Electrical and Electronics Engineers Inc., 2013) Karaçalı, BilgeIn this paper, a new statistical learning method was developed that implements the quasi-supervised learning method in an expectation-maximization loop. First, automatic strategies were generated that separated the samples drawn from different distributions into respective sample sets using the posterior probabilities computed via quasi-supervised learning based on partially separated samples. An expectation-maximization loop was then constructed by combining this procedure with the posterior probability computation step using the new separated sample sets. In controlled experiments on recognition problems with varying difficulties, the proposed method was observed to consistently outperform the plain quasi-supervised learning method.Article Citation - WoS: 2Citation - Scopus: 2A Computational Analysis of Turkish Makam Music Based on a Probabilistic Characterization of Segmented Phrases(Taylor and Francis Ltd., 2015) Bozkurt, Barış; Karaçalı, BilgeThis study targets automatic analysis of Turkish makam music pieces on the phrase level. While makam is most simply defined as an organization of melodic phrases, there has been very little effort to computationally study melodic structure in makam music pieces. In this work, we propose an automatic analysis algorithm that takes as input symbolic data in the form of machine-readable scores that are segmented into phrases. Using a measure of makam membership for phrases, our method outputs for each phrase the most likely makam the phrase comes from. The proposed makam membership definition is based on Bayesian classification and the algorithm is specifically designed to process the data with overlapping classes. The proposed analysis system is trained and tested on a large data set of phrases obtained by transferring phrase boundaries manually written by experts of makam music on printed scores, to machine-readable data. For the task of classifying all phrases, or only the beginning phrases to come from the main makam of the piece, the corresponding F-measures are.52 and.60 respectively.Publication İki Faz Kipleyicisi ve Kromatik Dağılım ile Dalgaboyu-adımlayan Bir Lazer Kullanılarak Optik Bölgesi Seyrek Örnekleme Temelinde Çalışan Optik Eş-fazlı Tomografi Cihazının Geliştirilmesi(2020) Tozburun, Serhat; Yetiş, Ozan; Karaçalı, Bilge; Pekkan, KeremFourier-bölgesi Optik Eş-fazlı Tomografi (OCT) görüntüleme tekniğinin bir alt kolu olan dalga-boyu-tarayan-kaynaklı OCT bu talepleri karşılayacak bir potansiyele sahiptir. Bu tür sistemlerde dikkat çeken noktalardan birisi lazer kaynağıdır. Farklı optik dalga boyu tarama teknikleri kullanılarak çeşitli lazer kovukları tasarlanmıştır. Şimdiye kadar, üzerinde çalışılmış lazer tasarımlarının dalga boyu tarama kısmında mekanik harekete sahip aynalar ya da dağılım fiberlerinde kip kilitleme tekniği yaygın olarak incelendi. Özellikle, bu lazerlerle kurulan dalga-boyu-tarayan kaynaklı OCT sistemleriyle yapılan Doppler çalışmalarında, birbirinden ayrı A-çizgisi tetik sinyalleri ya da işleme sonrası faz düzeltme algoritmaları kullanıldı. Diğer taraftan, lazer kaynaklarında sağlanan gelişmeler ile üretilen hızlı girişim sinyallerini (OCT sinyallerini) dijitalize edecek gigahertz örnekleme hızındaki elektronik veri edinim kartlarına olan ihtiyacı ortaya çıkarttı. Bu ihtiyaç, sürekli olarak veri edinim kartlarındaki teknolojik gelişmelere bağlı kalmaya neden oldu ve OCT teknolojisini sınırlayıcı bir zorluk haline geldi. Son yıllarda geliştirilmeye başlanan ve proje yürütücüsünün de aktif katılım sağladığı optik-bölgesi seyrek örnekleme yöntemi çözüm adresi olarak ortaya konuldu. Arası olmayan ayrık spektrum (diğer bir değişle fırça tipi spektrum) tipli lazer çıkışını kullanan bu teknik ile hızlı OCT sinyallerinin edinimi için istenilen bant aralığı makul bir seviyeye (3 GS/s) çekilebileceği önerildi. İşbu rapor, optik-bölgesi seyrek örnekleme yöntemini kullanan yeni nesil bir OCT cihazının laboratuvar ortamında geliştirilmesi adımlarını ve ilgili deneysel çalışmaları sunar. Bu kapsamda edilen bulgulara göre geliştirilen toplam 64 optik kipi 33 MHz tekrar hızında adımlayan (tarayan) lazer, 17,5 ?m aksiyel çözünürlüğü yaklaşık 1,2 mm eş faz boyunca üretti. Optik-bölgesi seyrek örnekleme temelinde çalışan OCT cihazı ile 216 ?m, 347 ?m ve 442 ?m çapında mikro-kanallardaki akışı görüntülendi. Sonuç, not edilen teknik bulgular geliştirilen özgün dalga-boyu-tarayan-kaynaklı OCT cihazının optik-bölgesi seyrek örnekleme yönteminde kullanılabilecek nicelik ve niteliktedir.Conference Object Citation - WoS: 1Citation - Scopus: 2Fisher's Linear Discriminant Analysis Based Prediction Using Transient Features of Seismic Events in Coal Mines(Institute of Electrical and Electronics Engineers Inc., 2016) Köktürk Güzel, Başak Esin; Karaçalı, BilgeIdentification of seismic activity levels in coal mines is important to avoid accidents such as rockburst. Creating an early warning system that can save lives requires an automated way of predicting. This study proposes a prediction algorithm for the AAIA'16 Data Mining Challenge: Predicting Dangerous Seismic Events in Active Coal Mines that is based on transient activity features along with average indicators evaluated by a Fisher's linear discriminant analysis. Performance evaluation experiments on the training datasets revealed an accuracy level of around 0.9438 while the performance on the test dataset was at a level of 0.9297. These results suggest that the proposed approach achieves high accuracy in predicting danger seismic events while maintaining low complexity.Doctoral Thesis Development of a Unified Analysis Framework for Multicolor Flow Cytometry Data Based on Quasi-Supervised Learning(Izmir Institute of Technology, 2017) Köktürk Güzel, Başak Esin; Karaçalı, BilgeIn this dissertation, automatic compensation and gating strategies are investigated for multi-color flow cytometry data analysis. We propose two clustering algorithms that combine the quasi-supervised learning algorithm with an expectation-maximization routine for automatic gating. The quasi-supervised learning algorithm estimates the posterior probabilities of the different cell populations at each sample in a dataset in a manner that does not involve fitting a parametric model to the data. We have developed two different binary divisive clustering algorithms based on expectation maximization with responsibility values calculated using the quasi-supervised learning algorithm instead of the probabilistic models used in conventional expectation maximization applications. Our clustering algorithms determine the number of clusters in run-time by measuring the overlap between the estimated clusters in each provisional division and comparing it with the previous one to determine whether the division is warranted or not. Since this type of clustering is indifferent to the underlying distribution of dataset, it is well suited to automatic flow cytometry gating. The second clustering algorithm improves upon the first one using a simulated annealing approach. Its iterative structure allows finding the global minimum of a cost functional that achieves the best separation point by gradually smoothing the decision regions in each iteration. Finally, we have developed a joint diagonalization and clustering method for automatic compensation of flow data based on the methods above. The proposed method identifies cell sub groups using the annealing-based model-free expectation-maximization algorithm and finds a data transformation matrix that achieves orthogonality of the covariance structure of each identified cell cluster using fast Frobenius diagonalization. We have tested all proposed algortihms on both synthetically created datasets and real multi-color flow cytometry datasets. The results show that our automated gating algorithms are very successful in identifying the distinct cell groups so long as there is enough statistical evidence for their presence. In addition, the automated compensation procedure was also successfully applied on the synthetically created dataset and real multi-color flow cytometry data of lymphocytes that are a low autofluorescence cell group. However, the automated compensation algorithm needs further study to be generalized to high autofluorescence cell types where proper compensation does not necessarily coincide with an orthogonal covariance structure.Article Citation - Scopus: 1Annealing-Based Model-Free Expectation Maximisation for Multi-Colour Flow Cytometry Data Clustering(Inderscience Enterprises Ltd., 2016) Köktürk, Başak Esin; Karaçalı, BilgeThis paper proposes an optimised model-free expectation maximisation method for automated clustering of high-dimensional datasets. The method is based on a recursive binary division strategy that successively divides an original dataset into distinct clusters. Each binary division is carriedout using a model-free expectation maximisation scheme that exploits the posterior probability computation capability of the quasi-supervised learningalgorithm subjected to a line-search optimisation over the reference set size parameter analogous to a simulated annealing approach. The divisions arecontinued until a division cost exceeds an adaptively determined limit. Experiment results on synthetic as well as real multi-colour flow cytometrydatasets showed that the proposed method can accurately capture the prominent clusters without requiring any prior knowledge on the number of clusters ortheir distribution models.Master Thesis A Comparative Analysis of Coherence Measures for Electroencephalography(Izmir Institute of Technology, 2018) Çağdaş, Serhat; Karaçalı, BilgeFunctional connectivity is often used in brain-computer interface studies as well as other neuroscience fields as a feature extraction method. In the functional connectivity using electroencephalography (EEG), connectivity patterns are extracted by a dependency matrix showing the coherence between electrode pairs. A variety of dependence measures can be used to calculate this matrix. In this study, a total of 15 coherence measures were analyzed comparatively in terms of computation time, accuracy and statistical significance in discriminating motor/motor imagery activities. As dependence measures, in addition to methods used in the literature for brain connectivity, five other methods used as contrast function in independent component analysis and two novel mutual information calculators proposed in this study were evaluated. Furthermore, a novel hierarchical clustering based statistical test procedure was also proposed for motor/motor imagery activity comparison, along with a similar statistical significance test applied on data from 103 subjects on four different activity types. In experiments on real data set, significance results of dependence measures differed according to the type of activity and time window duration of activity signals. Considering both computation time and accuracy performances on synthetic data, a number of methods with high statistical significance and different dependence characteristics were identified as feasible for a connectivity based brain-computer interface.