Sürdürülebilir Kalkınma Amaçları
Yayınlar
- MakaleAçık Erişim2026A leakage-free analytics framework for subject-wise electrocardiogram-based sleep apnea detection using machine learning
Melek, Negin
Sleep apnea is associated with impaired sleep quality and elevated cardiovascular risk, creating a need for reliable automated screening approaches. Although electrocardiogram (ECG)-based detection methods have shown promising results, reported performance values can be affected by evaluation strategies that allow information leakage between training and testing data. This study investigates ECG-based sleep apnea detection under strict subject-wise conditions using the PhysioNet Apnea-ECG database. Thirty-five annotated single-lead ECG recordings sampled at 100 Hz were divided into non-overlapping 60-s segments according to minute-wise apnea labels. Statistical ECG descriptors and time-domain heart rate variability features were extracted and evaluated using k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF) classifiers. In addition, a lightweight one-dimensional convolutional neural network (1D-CNN) was trained directly on filtered ECG segments. Repeated holdout validation with subject-level train-test separation was adopted, and normalization parameters were estimated exclusively from training data in each repetition to prevent information leakage. Model performance was assessed using accuracy, balanced accuracy, precision, recall, specificity, F1-score, ROC-AUC, Cohen's kappa, and Golden Distance (GD). In the representative subject-wise evaluation, Random Forest achieved the most balanced performance, yielding the highest balanced accuracy (0.70) together with the lowest GD (0.56). Across the repeated subject-wise holdout experiments, SVM achieved the strongest average overall performance, producing the highest mean accuracy (0.72), balanced accuracy (0.69), ROC-AUC (0.77), and Cohen's kappa (0.39), and the lowest mean GD value (0.58). The lightweight 1D-CNN converged stably during training, but its performance on unseen subjects remained lower than that of the handcrafted feature-based models.
- MakaleAçık Erişim2026Machine learning–enabled ECG arrhythmia classification: a systematic and educational study from signal processing to decision support
Melek, Negin
Electrocardiogram (ECG) signals play a critical role in the early detection of cardiac arrhythmias, which remain a major cause of morbidity and mortality worldwide. While deep learning approaches have achieved high classification accuracy, their increasing complexity often limits interpretability and practical applicability. This study presents a systematic and interpretable framework for multi-class ECG arrhythmia classification, examining the effects of signal processing, feature extraction, feature selection, and evaluation strategies on classification performance. Experiments were conducted on the MIT-BIH Arrhythmia Database using two feature representations: (i) morphological and temporal features, and (ii) a compact wavelet-based representation. Sequential Forward Feature Selection (SFFS) revealed that classification performance saturates at approximately 15 features, indicating that most discriminative information is captured within a compact subset. Using this feature space, the support vector machine (SVM) achieved the best overall performance, reaching 98.54% accuracy with stable results across different configurations. The wavelet-based representation further improved performance balance, yielding lower Golden Distance (GD) values (as low as 0.0249), indicating more consistent behavior across evaluation metrics. Overall, the results demonstrate that carefully designed feature extraction and selection enable classical machine learning methods, particularly SVM, to achieve high and reliable performance, providing an interpretable alternative to more complex black-box models in ECG arrhythmia classification.
- MakaleYalnız künye2025Golden Distance: A New and Comprehensive Metric Definition Study Facilitating Classification Performance Evaluations
Melek, Mesut; Melek, Negin
Machine/deep learning has great potential in many classification studies that make our lives easier. Performance evaluation of the classification algorithm is of great importance for making accurate predictions in machine learning, which, itself, learns from data without any human intervention. In these learning models, high-dimensional data in classes with complex features belonging to balanced or unbalanced distributions are analysed with different classification algorithms and take their place in the decision stage. While the difficulties in implementing classification algorithms continue today, evaluating the superiority of developed systems over each other continues to be both a time-consuming and very difficult process. In models proposed in the literature, only a single parameter, such as accuracy, is not sufficient to determine model adequacy. In this context, among different classification algorithm performance parameters, some metrics such as sensitivity, specificity, F-measure (F1), Jaccard index, area under the curve, and kappa coefficient are used for system evaluation. As these parameters are placed in the analysis tables, the study should be evaluated at length by including large tables in the article. Instead of using various parameters that make performance analysis difficult in system evaluation, this study defines a new Golden Distance-based parameter that summarizes the model comprehensively from all dimensions. To understand this new metric’s working principle, a detailed comparative analysis of research conducted on three datasets was performed. Based on the final evaluation, the proposed inclusive metric demonstrated successful and fair behaviour in all conditions. Thus, a fair and simple comparison will be achieved with a single metric, and long analyses can be avoided by presenting many performance parameters in systems’ superiority indicator tables.
- MakaleYalnız künye2022Roza: a new and comprehensive metric for evaluating classification systems
Melek, Mesut; Melek, Negin
Many metrics such as accuracy rate (ACC), area under curve (AUC), Jaccard index (JI), and Cohen’s kappa coefficient are available to measure the success of the system in pattern recognition and machine/deep learning systems. However, the superiority of one system to one other cannot be determined based on the mentioned metrics. This is because such a system can be successful using one metric, but not the other ones. Moreover, such metrics are insufficient when the number of samples in the classes is unequal (imbalanced data). In this case, naturally, by using these metrics, a sensible comparison cannot be made between two given systems. In the present study, the comprehensive, fair, and accurate Roza (Roza means rose in Persian. When different permutations of the metrics used are superimposed in a polygon format, it looks like a flower, so we named it Roza.) metric is introduced for evaluating classification systems. This metric, which facilitates the comparison of systems, expresses the summary of many metrics with a single value. To verify the stability and validity of the metric and to conduct a comprehensive, fair, and accurate comparison between the systems, the Roza metric of the systems tested under the same conditions are calculated and comparisons are made. For this, systems tested with three different strategies on three different datasets are considered. The results show that the performance of the system can be summarized by a single value and the Roza metric can be used in all systems that include classification processes, as a powerful metric.
- MakaleAçık Erişim2021Eeg sinyallerini kullanarak 2B ve 3B hibrit bir videonun ayrıntılı kanal/lob analizi
Melek, Negin; Melek, Mesut; Kayikcioglu, Temel
İnsan gözünün yapısına göre 2 Boyutlu (2B) ve 3B parçalardan oluşan video analizinde ani geçiş ve sabit durumların değerlendirilmesi önemlidir. Bu çalışmada, insan beyin sinyallerinin güç spektrum yoğunluğu (GSY), 2B ve 3B hibrit video izleme sonucunda analiz için dikkate alınmıştır. İnsanların yorulunca derinlik algısının kaybettiğini daha önceki çalışmalarımızda iddia etmiştik. Bu çalışmada, rastgele 2B ve 3B parçalardan oluşan, tek akışlı bir anaglif video sağlanmıştır. 2B ve 3B hibrit video çalışmasında, beyin sinyal analizinde kısa zamanlı Fourier dönüşümüne (KZFD) dayalı GSY ve spektrogram adı verilen görsel temsil kullanılmıştır. Tüm EEG frekans bantları test edildikten sonra, spektrogram çizelgesindeki 2B ve 3B parçaların karşılaştırılmasında GSY farkı dikkate alınarak, delta bandı baskın bant olarak seçilmiştir. Bu banttan çıkarılan öznitelikler iki popüler sınıflandırıcı tarafından sınıflandırılmıştır. Bunlar destek vektör makinesi (DVM) ve doğrusal ayırma analizi (DAA) algoritmalarıdır. Sonuç olarak, frontal ve temporal loblar, 2B ve 3B geçişlerin sınıflandırılmasında diğer loblara göre daha iyi sonuçlar göstermektedir. Öznitelik çıkarma yöntemi olarak kullanılan istatistiksel fonksiyonlar ve Hjorth parametreleri sonucunda DVM ve DAA algoritmaları için sınıflandırma başarısı sırasıyla %68 ve %79 olarak hesaplanmıştır.
- MakaleAçık Erişim2020EEG Kullanarak 2D&3D Video Geçişinin Tespitinde Frontal ve Temporal Lobların Etkinliği
Melek, Negin; Melek, Mesut; Kayikcioglu, Temel
İnsan gözünün üç boyutlu (3B) yapısı nedeniyle, bu araştırmada 3B teknolojisi kullanılmıştır. Binoküler görmenin yorgunluk esnasında boyut kaybedeceğini iddia ederek 2B ve 3B’ye geçiş analizi önemlidir. Böylece rastgele 2B ve 3B parçalardan oluşan tek akışlı bir video dokuz gönüllü tarafından izlenilmiştir. Gönüllülerin beyin sinyallerinin dinamik davranışı ve güç spektral yoğunluğu (GSY), kapsamlı bir nicel analiz için dikkate alınmıştır. Baskın EEG bantları ve zaman aralıkları, kısa zamanlı Fourier dönüşümüne (KZFD) dayalı güç farklılıklarını analiz ederek 2B’den 3B’ye (2B_3B) ve 3B’den 2B’ye (3B_2B) geçişlerde seçilmiştir. Bu bilgiler dikkate alınarak, etkili özellik çıkarımı ve sınıflandırma teknikleri uygulanarak, gönüllülerin beyin loblarını temsil eden kanalların davranış kalıpları analiz edilmiştir. Öznitelik çıkarma yöntemleri olarak Hjorth parametreleri ve istatistiksel yöntemler kullanılmıştır. 2B_3B ve 3B_2B geçişlerini sınıflandırmak için en yakın komşular (e-YK) ve doğrusal diskriminant analiz (DDA) algoritmaları uygulanmıştır. Sonuçlar, 2B_3B ve 3B_2B geçişlerinin kapsamlı sınıflandırma analizi sayesinde beyin korteksinin aktivite gücünün değişiminin temsil edilebileceğini ortaya koymuştur. Bu geçişleri temsil eden kanallar arasındaki temporal ve frontal lobların baskınlığı göz ardı edilemez.