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    Diagnosis Model Based on Chest Correlation Map Derived from Lung Sounds
    (Ieee, 2025) Arishel, Ozgur; Ergen, Ege; Sargin, Serhat Ismet; Sen, Ipek
    The increasing number of respiratory diseases worldwide increases the need for accurate diagnostic methods. Today, stethoscopes and pulmonary function tests, which are widely used in clinical practice, may be insufficient in diagnostic accuracy. To overcome this deficiency, new approaches based on computerised analysis of lung sounds are being developed. In this study, a model based on chest correlation maps is proposed for the detection of respiratory diseases. Within the scope of the study, 14-channel lung sound recordings of 120 participants (60 healthy, 60 asthma) were used and the correlation coefficients between the microphones located in the middle and lower regions were calculated according to the reference microphones placed in the upper lung region. Various features were extracted by dividing the obtained correlation coefficients by different threshold levels, and these features were given as input to the Bayesian classifier. According to the classification results, the highest F1 scores were calculated as 0.793 for the upper left reference microphone and 0.754 for the upper right reference microphone. These findings show that the proposed model provides an effective approach for the detection of respiratory diseases.
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    EEG Band Power Feature Based Electrode Combination Analysis for Epilepsy Detection
    (Institute of Electrical and Electronics Engineers Inc., 2025) Sargin, Serhat Ismet; Jafarifarmand, Aysa
    Epilepsy is a chronic neurological disorder characterised by seizures caused by abnormal electrical activity in the brain. Today, the diagnosis of epilepsy is largely based on the analysis of electroencephalogram (EEG) signals. However, the nonlinear and chaotic nature of EEG signals makes visual inspection a time-consuming, labour-intensive, and specialised process. In this context, this study proposes a computer-aided diagnostic method that aims to analyse EEG signals more efficiently. Within the scope of the study, power values belonging to different frequency bands were obtained from EEG signals collected from 121 participants through 35 electrodes. In determining the electrode combinations, the individual classification performance of each electrode was analysed, the electrode with the highest performance was selected and combined with the other electrodes in the vector space, thus creating effective electrode combinations. The resulting feature vectors were presented as input to the decision tree classifier, and the model's performance was evaluated with metrics such as accuracy, F1 score, sensitivity, and specificity. Using a combination of theta band power features obtained from F3C3, C3P3, T6O2, T5O1, FP2A2, O2A2 and P4A2 electrodes, the model showed a high success in epilepsy detection with 84.6% accuracy and 81.1% F1 score. © 2025 IEEE.
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    Feature Selection for Differential Diagnosis of Asthma and COPD: A Preliminary Study
    (IEEE, 2023) Sargin, Serhat Ismet; Sen, Ipek
    Asthma and chronic obstructive pulmonary disease (COPD) are two obstructive pulmonary diseases whose differential diagnosis is difficult due to overlapping symptoms and inadequacy of classical methods. The main aim of this study is to understand which combination of acoustic properties is more discriminative, and to develop a new method that can be used in clinical practice. Accordingly, a total of 26 features under eight different feature types have been calculated using pulmonary sounds acquired from 50 volunteers diagnosed with asthma (30 subjects) and 20 copd (20 subjects), and forward sequential feature selection has been performed using k-nearest neighbor (k-NN) classifier as it was observed to have better performance than Bayesian classifier on the singular features extracted. Consequently, the feature set composed of F-max, BIN5, AR(3), P-max, and F-95 has been selected to be the best feature set by the k-NN classifier with 96% accuracy in asthma and COPD discrimination. To develop a more reliable method for the differential diagnosis of asthma and COPD, the feature set should be augmented and different types of classifiers should also be used.
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    Leveraging Classical Methods for Accurate Epilepsy Detection with EEG Signals
    (Ieee, 2025) Karatas, Mustafa Can; Sargin, Serhat Ismet; Farmand, Aysa Jafari
    Epilepsy is a neurological disorder characterized by abnormal electrical activity in the brain and its diagnosis is usually based on the analysis of electroencephalogram (EEG) signals. However, due to the inherent nonlinearity and chaotic nature of EEG signals, visual inspection is a time-consuming, labor-intensive, and experience-demanding process. Therefore, the main objective of this study is to present an efficient computer-aided approach for the analysis of EEG signals aiming at epilepsy detection. Here, 23 features were extracted from EEG signals obtained from 121 healthy and epileptic participants. These features were then fed individually to three different machine learning classifiers to evaluate their discriminative abilities. Among the extracted features, the theta band feature extracted from the F3C3 electrode showed a high performance with the decision tree classifier, achieving an F1 score of 0.774 and an accuracy of 0.813.
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    Respiratory Adventitious Sound Classification Using Third-order Difference Plots
    (Ieee, 2025) Sevil, Arif Emre; Sargin, Serhat Ismet; Sen, Ipek
    This study aims to classify abnormal respiratory sounds such as crackles and wheezes using third order difference plots. After obtaining difference plots from normal respiratory sound segments and segments with crackles and wheezes, the eigenvalues of the covariance matrices were calculated and used as features for classification. Binary classifications were performed using the decision tree algorithm, where the performances were evaluated through 10-fold cross-validation. Generally, higher performances were observed in distinguishing crackles and in the inspiration phase. The highest performance was achieved in crackle vs. normal classification in inspiration with an accuracy rate of 82%. This performance, obtained without additional features, suggests that difference plots are suitable tools for the classification of normal and abnormal respiratory sounds. In the future, it is aimed to test other classification algorithms, to improve the methodology with preprocessing steps applied on the signals and with new discriminative features calculated from the plots, and finally, to develop the three-class classifier for the form of usage that will benefit the clinical practice.
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    The Relevance of Auscultation Site in Healthy versus Restrictive Pulmonary Disease Classification
    (Ieee, 2024) Sargin, Serhat Ismet; Sen, Ipek
    The aim of this study was to perform a quantitative analysis to assess the relevance of the auscultation site on the chest wall in classifying restrictive lung diseases versus the healthy class. Accordingly, 41 features were calculated from 14-channel pulmonary sounds acquired on the posterior chest wall of 20 healthy subjects and 20 subjects with restrictive lung disease (5 with pneumonia and 15 with interstitial lung disease, ILD). These features were then used to train Bayes, decision tree (DT), and support vector machine (SVM) classifiers in leave-one-subject-out scheme. The two symmetric sites at the bases of the right and left lungs achieved the highest subject-level F1 scores of 0.974 and 0.976, respectively, while also maintaining the smallest deviation in flow cycle-level accuracies across subjects. These results were obtained using the DT classifier with f(50) and MFCC5.