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• MakaleAçık Erişim2026Scopus SJRQ1/r/gumushane/13725
  A 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.

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  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/gumushane/13425
  Machine 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.

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  AtıfScopus1

  İncele
• MakaleYalnız künye2025Scopus SJRQ3/r/gumushane/14301
  Golden 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.

  AtıfScopus9·WoS10·OpenAlex12

  İncele
• MakaleYalnız künye2022Scopus SJRQ2/r/gumushane/14755
  Detection of 2D and 3D Video Transitions Based on EEG Power

  Manshouri, Negin; Melek, Mesut; Kaylkcloglu, Temel

  Despite the long and extensive history of 3D technology, it has recently attracted the attention of researchers. This technology has become the center of interest of young people because of the real feelings and sensations it creates. People see their environment as 3D because of their eye structure. In this study, it is hypothesized that people lose their perception of depth during sleepy moments and that there is a sudden transition from 3D vision to 2D vision. Regarding these transitions, the EEG signal analysis method was used for deep and comprehensive analysis of 2D and 3D brain signals. In this study, a single-stream anaglyph video of random 2D and 3D segments was prepared. After watching this single video, the obtained EEG recordings were considered for two different analyses: the part involving the critical transition (transition state) and the state analysis of only the 2D versus 3D or 3D versus 2D parts (steady state). The main objective of this study is to see the behavioral changes of brain signals in 2D and 3D transitions. To clarify the impacts of the human brain's power spectral density (PSD) in 2D-to-3D (2D_3D) and 3D-to-2D (3D_2D) transitions of anaglyph video, nine visual healthy individuals were prepared for testing in this pioneering study. Spectrogram graphs based on short time Fourier transform (STFT) were considered to evaluate the power spectrum analysis in each EEG channel of transition or steady state. Thus, in 2D and 3D transition scenarios, important channels representing EEG frequency bands and brain lobes will be identified. To classify the 2D and 3D transitions, the dominant bands and time intervals representing the maximum difference of PSD were selected. Afterward, effective features were selected by applying statistical methods such as standard deviation, maximum (max) and Hjorth parameters to epochs indicating transition intervals. Ultimately, k-nearest neighbors, support vector machine and linear discriminant analysis (LDA) algorithms were applied to classify 2D_3D and 3D_2D transitions. The frontal, temporal and partially parietal lobes show 2D_3D and 3D_2D transitions with a good classification success rate. Overall, it was found that Hjorth parameters and LDA algorithms have 71.11% and 77.78% classification success rates for transition and steady state, respectively.

  AtıfScopus6·WoS4·OpenAlex6

  İncele
• MakaleYalnız künye2022Scopus SJRQ3/r/gumushane/11506
  Roza: 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.

  AtıfScopus7·WoS8·OpenAlex9

  İncele
• MakaleYalnız künye2021Scopus SJRQ4/r/gumushane/14912
  Brain-computer interface systems for smart homes-a review study

  Maleki, Masoud; Manshouri, Negin; Kayikcioglu, Temel

  Brain-computer Interface (BCI) systems, usually using signals taken from users' brain through electroencephalography (EEG), control various devices around and provide the user's com-mand by interacting. Improving the quality of life of people with disabilities is the main goal of BCI systems. The importance of BCI-based smart home systems is further increasing as a smart home system directly affects the life of a disabled individual. On the other hand, few BCI systems can be run directly using smart home systems. The importance of the BCI-based smart home and the few existing systems require more work in this vital field. In addition, no reviews have described BCI systems in a smart home. In this study, we reviewed all the papers on BCI-based smart home systems published in the last 6 years. These studies investigated and evaluated BCI systems from nine different perspectives. In addition, all studies were examined in terms of signal processing methods. Finally, the problems and challenges of these systems were discussed and new solutions were pro-posed.

  AtıfScopus6·WoS5·OpenAlex4

  İncele
• MakaleYayıncıda açık erişim2021Scopus SJRQ2/r/gumushane/13285
  An automatic EEG-based sleep staging system with introducing NAoSP and NAoGP as new metrics for sleep staging systems

  Melek, Mesut; Manshouri, Negin; Kayikcioglu, Temel

  Different biological signals are recorded in sleep labs during sleep for the diagnosis and treatment of human sleep problems. Classification of sleep stages with electroencephalography (EEG) is preferred to other biological signals due to its advantages such as providing clinical information, cost-effectiveness, comfort, and ease of use. The evaluation of EEG signals taken during sleep by clinicians is a tiring, time-consuming, and error-prone method. Therefore, it is clinically mandatory to determine sleep stages by using software-supported systems. Like all classification problems, the accuracy rate is used to compare the performance of studies in this domain, but this metric can be accurate when the number of observations is equal in classes. However, since there is not an equal number of observations in sleep stages, this metric is insufficient in the evaluation of such systems. For this purpose, in recent years, Cohen’s kappa coefficient and even the sensitivity of NREM1 have been used for comparing the performance of these systems. Still, none of them examine the system from all dimensions. Therefore, in this study, two new metrics based on the polygon area metric, called the normalized area of sensitivity polygon and normalized area of the general polygon, are proposed for the performance evaluation of sleep staging systems. In addition, a new sleep staging system is introduced using the applications offered by the MATLAB program. The existing systems discussed in the literature were examined with the proposed metrics, and the best systems were compared with the proposed sleep staging system. According to the results, the proposed system excels in comparison with the most advanced machine learning methods. The single-channel method introduced based on the proposed metrics can be used for robust and reliable sleep stage classification from all dimensions required for real-time applications.

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  AtıfScopus13·WoS9·OpenAlex16

  İncele
• MakaleAçık Erişim2020Scopus SJRQ3/r/gumushane/14945
  Low-cost brain-computer interface using the emotiv epoc headset based on rotating vanes

  Melek, Mesut; Manshouri, Negin; Kayikcioglu, Temel

  In the brain-computer interface system (BCI), electroencephalography (EEG) signals are converted into digital signals and analyzed, allowing direct communication between humans and the electronic devices around them. The convenience of the user and the speed of communication with the surrounding devices are the most important challenges of BCI systems. The Emotiv Epoc headset minimizes the discomfort of the user thanks to its wet electrodes and easy handling. In the continuation of our previous works, in this paper, we developed our BCI system based on the gaze at the rotating vanes using the inexpensive Emotiv Epoc headset. In addition to user comfort, our design has an acceptable mean accuracy rate (ACC) and mean information transfer rate (ITR) compared to similar systems.

  AtıfScopus11·WoS10·OpenAlex18

  İncele
• MakaleYalnız künye2020Scopus SJRQ2/r/gumushane/14731
  An EEG-based stereoscopic research of the PSD differences in pre and post 2D&3D movies watching

  Manshouri, Negin; Maleki, Masoud; Kayikcioglu, Temel

  Despite knowing the reality of three-dimensional (3D) technology in the form of eye fatigue, this technology continues to be retained by people (especially the young community). To check the influences of the human brain's power spectrum density (PSD) pre and post a 2D and 3D movie watching, a five-member test group was arranged. In this study, electroencephalogram (EEG) was used as a neuroimaging method. EEG recordings of five individuals were taken both before and after watching 2D and 3D movies. The main framework of this study was to analyze the effects of power spectrum density (PSD) of the human brain by testing all EEG frequency bands before and after 2D and 3D movie watching, also to determine effective brain lobes, and consequently, EEG channels. After 2D/3D EEG recording, this record was divided into three stages for analysis. These stages consisted of Relax, Watching, and Rest. This benchmarking analysis included I) before and after watching the 2D movie (R2b and R2a), II) before and after watching the 3D movie (R3b and R3a), and III) after watching the 2D/3D movie (R2a and R3a). In the Relax and Rest stages, the 2D/3D EEG power differences in all channels of brain regions for the five EEG bands, including delta (δ), theta (θ), alpha (α), beta (β), and gamma (γ), were analyzed and compared. The PSD based on short-time Fourier transform (STFT) was used to select the dominant bands in this study. Feature extraction was performed on the preprocessed EEG signals using STFT and discrete wavelet transform (DWT). In the 2D analysis, δ, θ, α, and β acted as dominant bands, in 3D, δ, θ, and α were dominant bands, and in 2D/3D, δ and α were selected as meaningful and dominant bands. Partial least-squares regression (PLSR) and support vector machine (SVM) classification algorithms were considered in order to classify the obtained R2a and R3a EEG signals. After the dominant band selection, the correct choice of effective channel combinations in these bands resulted in the percentage of high success of classifiers for Stage III. Taking into account the optimum number of channels, the best percentage of classification accuracy for the average of PLSR and SVM classification results by considering the best channels of SVM and DWT feature extraction method is 91.83% and 80.73% respectively.

  AtıfScopus56·WoS43

  İncele
• BildiriYalnız künye2018/r/gumushane/15137
  Power spectrum analysis of EEG for watching 2D & 3D videos and resting state

  Manshouri, Negin; Maleki, Masoud; Kayikcioglu, Temel

  Analysis of the brain waves has become useful over than 80 years. Along with the medical application, the brain signal is also used for other research works in the last fifteen years. In this study, by considering the depth factor in the image of a video, the effects of it are checked on EEG. A video is displayed in two-dimensional (2D) and three-dimensional (3D) format to the subjects. After recording the EEG signals in video watching, the resting state was recorded. Three states are considered for a better understanding of these effects on EEG. These states involve 2D&3D, 2D&resting and 3D&resting. Frequency analysis of the signals is evaluated by two methods. These methods are Fast Fourier Transform (FFT) and Welch method.

  AtıfScopus2·WoS1·OpenAlex3

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