Search

Cardiotocography (CTG) is a clinical procedure that is used to track and gauge the severity of fetal distress. Although CTG is the most often used equipment to monitor and assess the health of the fetus, the high rate of false positive results due to visual interpretation significantly contributes to needless surgical delivery or delayed intervention. In this study, a novel approach is introduced where both printing CTG paper is digitized and a machine learning approach is employed to detect the abnormality in the digitized CTG signal. Image processing-based preprocessing steps are employed to make the printing of CTG paper more convenient to extract the CTG signal. Various signal-processing techniques are used to calibrate the extracted CTG signal. Then, Empirical Mode Decomposition (EMD) is used to decompose the CTG signal into its frequency components and instantaneous frequency and spectral entropy features are extracted. After feature normalization and feature selection with ReliefF algorithm, support vector machines (SVM) is used for the classification of the normal and abnormal classes. A novel dataset is used in the experimental works and various performance evaluation metrics are used for the evaluation of the achievement of the proposed method. 10-fold cross-validation-based experiments show that the proposed method is quite efficient in abnormality detection in printing CTG papers where an average accuracy score of around 90.0% is produced.

Emotion perception is critical for behavior prediction. There are many ways to capture emotional states by observing the body and copying actions. Physiological markers such as electroencephalography (EEG) have gained popularity, as facial emotions may not always adequately convey true emotion. This study has two main aims. The first is to measure four emotion categories using deep learning architectures and EEG data. The second purpose is to increase the number of samples in the dataset. To this end, a novel data augmentation approach namely the Extreme Learning Machine Wavelet Auto Encoder (ELM-W-AE) is proposed for data augmentation. The proposed data augmentation approach is both simple and faster than the other synthetic data augmentation approaches. For deep architectures, large datasets are important for performance. For this reason, data multiplexing approaches with classical and synthetic methods have become popular recently. The proposed synthetic data augmentation is the ELM-W-AE because of its efficiency and detail reproduction. The ELM-AE structure uses wavelet activation functions such as Gaussian, groove gap waveguide (GGW), Mexican, Meyer, Morlet, and Shannon. Deep convolutional architectures classify EEG signals as images. EEG waves are scalograms using Continuous Wavelet Transform (CWT). The ResNet18 architecture recognizes emotions. The proposed technique uses GAMEEMO data collected during gameplay. Each of these states is represented in the GAMEEMO data collection. The visual data set created from the signal was divided into two groups 70% training and 30% testing. ResNet18 has been fine-tuned with augmented photos, training images only. It achieved 99.6% classification accuracy in tests. The proposed method is compared with the other approaches on the same dataset, and an approximately 22% performance improvement is achieved.

One of the most dangerous diseases in the world is a brain tumor. A brain tumor destroys healthy tissue in the brain and then multiplies abnormally, causing increased internal pressure in the skull. This can lead to death if not diagnosed early. Magnetic Resonance Imaging (MRI) is a diagnostic method that is frequently used in soft tissues and gives successful results. In this study, a brain tumor was automatically detected from MR images. For feature extraction, a pre-trained Convolutional Neural Network (CNN) model named MobilenetV2 was used. Then, the ReliefF algorithm was used for feature selection. The features extracted with MobileNetV2 and the features selected with the ReliefF algorithm are given separately to the classifiers and the system performance is tested. As a result of experimental studies, it was seen that the highest performance was obtained with the combination of MobileNetV2 feature extraction, ReliefF algorithm feature selection, and KNN classifier.