Detection of Premature Ventricular Contractions Using Machine Learning

2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703Premature Ventricular Contractions (PVCs), a form of abnormal heartbeat that can be identified through electrocardiogram (ECG) signals, play a crucial role in detecting potentially life-threatening ventricular arrhythmias. In this study, three features (RR interval, QRS width, and R amplitude) are extracted from the MIT-BIH Arrhythmia Database and used Decision Tree (DT), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN) as classifiers. The classifiers achieved satisfactory results, with average accuracy rates of 94 % for KNN(K = 5) and 93% for KNN (K = 7), 87% for SVM, and 93% for DT. In addition, the classifiers were tested with the St. Petersburg Institute of Cardiological Technics 12-lead Arrhythmia database and obtained a convincing result of 74% accuracy for the SVM classifier, 70% for the KNN (K=5) and 68% KNN(K = 7) classifier, and 95% for the DT classifier. These results highlight the potential of feature selection and classification techniques in accurately identifying PVC beats from ECG signals, which is crucial for the early detection and effective treatment of ventricular arrhythmias. © 2023 IEEE.Türkiye Bilimsel ve Teknolojik Araştırma Kurumu, TÜBİTA

Bi-Rads Categories and Breast Lesions Classification of Mammographic Images Using Artificial Intelligence Diagnostic Models

According to BI-RADS criteria, radiologists evaluate mammography images, and breast lesions are classified as malignant or benign. In this retrospective study, an evaluation was made on 264 mammogram images of 139 patients. First, data augmentation was applied, and then the total number of images was increased to 565. Two computer-aided models were then designed to classify breast lesions and BI-RADS categories. The first of these models is the support vector machine (SVM) based model, and the second is the convolutional neural network (CNN) based model. The SVM-based model could classify BI-RADS categories and malignant-benign discrimination with an accuracy rate of 86.42% and 92.59%, respectively. On the other hand, the CNN-based model showed 79.01% and 83.95% accuracy for BI-RADS categories and malignant benign discrimination, respectively. These results showed that a well-designed machine learning-based classification model can give better results than a deep learning model. Additionally, it can be used as a secondary system for radiologists to differentiate breast lesions and BI-RADS lesion categories

Determination of the Optimal Eeg-Based Features To Detect Adhd by Machine Learning Algorithms

2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703This study proposes a highly accurate and fast algorithm for the diagnosis of attention deficit hyperactivity disorder (ADHD), which will reduce reliance on time-consuming subjective assessments, the findings of which are likely to be mistaken with other neurodevelopmental diseases. Time, frequency and nonlinear features were extracted from electroencephalographic (EEG) signals which recording based on visual attention task obtained from 61 ADHD and 60 healthy participants. In this study, Least Absolute Shrinkage and Selection Operator (LASSO) was used to find reliable features; and four machine learning classifiers such as support vector machine (SVM), k-nearest neighbors (KNN), decision tree and ensemble learning were evaluated for classifying ADHD and healthy children. The results were indicated that using LASSO with SVM can be useful for classifying ADHD and the highest average accuracy was reached in this study was 96.3%. In addition, the features selected with LASSO had shown that signals from the temporal, parietal, and occipital lobes might have the possible biomarkers for ADHD, at least in tasks that require visual attention. © 2023 IEEE

Identifying the Spectral-Based Neurophysiological Biomarkers To Detect Panic Disorder From Alpha Band Using Machine Learning Algorithms

2023 Medical Technologies Congress, TIPTEKNO 2023 -- 10 November 2023 through 12 November 2023 -- 195703Panic Disorder (PD) is a debilitating condition marked by sudden, intense fear episodes with physical symptoms. Swift and accurate PD detection is crucial for effective intervention. This study aimed to propose an optimal combination of spectral features of the Alpha band to detect PD. For this purpose, 21 PD-diagnosed individuals and 26 healthy controls attended a 5-minute eyes-closed resting state Electroencephalography (EEG) recording session. Welch method was applied to calculate the power spectral density of EEG signals and then the sum, average, maximum, relative power of alpha band, and individual alpha frequency (IAF) were extracted. Relief and nearest component analysis (NCA) methods were performed to select highly relevant features. The maximum average accuracy was reached when commonly selected features between two selection methods were used as inputs of classifiers. Adaboost classifier reached the highest average accuracy with $89.03 ±6.73% rate. © 2023 IEEE