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Öğe Classification of physical actions from surface EMG signals using the wavelet packet transform and local binary patterns(Institute of Physics Publishing, 2020) Alçin, Ömer Faruk; Budak, Ümit; Aslan, Muzaffer; Akbulut, Yaman; Cömert, Zafer; Akpınar, Muhammed H.; Şengür, AbdulkadirPhysical action recognition is a hot topic in human-machine interactions. It has potential uses in helping disabled people and in various robotic applications. Electromyography (EMG) signals measure the electrical activity of the muscular systems involved in physical actions. In this chapter, an efficient approach is developed for physical action recognition in humans based on EMG signals. The proposed method is composed of signal decomposition, feature extraction and feature classification. The signal decomposition is carried out using the wavelet packet transform (WPT). The WPT successively decomposes an input signal into its approximation and detail coefficients, which offers a more productive signal analysis. A one-dimensional local binary pattern (LBP) is used to code the approximation and detail coefficients of the decomposed EMG signals. The histogram of the LBP is used as the feature vectors of the EMG physical action classes. The support vector machine (SVM), decision tree, linear discriminant, k-nearest neighbors (k-NN), boosted and bagged tree ensemble classifiers are used in the classification stage. A dataset taken from the UCI machine learning repository is used in the experiments. The Delsys EMG apparatus, which has eight electrodes, is used to record the surface EMG signals. Each class of the dataset contains three male subjects and one female subject. The dataset contains ten physical actions, namely hugging, jumping, bowing, clapping, handshaking, running, sitting, standing, walking and waving. The experiments are carried out on each electrode with a ten-fold cross-validation test, and the average accuracy score is calculated. The experimental results show that the proposed method is quite efficient in EMG signal classification. The calculated average accuracy is 100% for each electrode.Öğe Electrocardiogram beat classification using deep convolutional neural network techniques(Institute of Physics Publishing, 2020) Cömert, Zafer; Akbulut, Yaman; Akpınar, Muhammed H.; Alçin, Ömer Faruk; Budak, Ümit; Aslan, Muzaffer; Şengür, AbdulkadirThe electrocardiogram (ECG) is a useful method which enables the monitoring of various cardiac conditions, such as arrhythmia and heart rate variability (HRV). ECG beats help to determine various heart failures such as cardiac disease and ventricular tachyarrhythmia. In the literature, it can be seen that various advanced signal processing and machine learning techniques and deep learning algorithms have been employed for ECG beat categorization. These methods were generally based on either the time domain or frequency domain. Time-frequency (T-F) based techniques have also been proposed for ECG beat classification. In this chapter, a different model is proposed for the ECG beat classification task. In the proposed approach, the ECG beats are initially represented by images. Instead of using a time-frequency approach for converting the ECG beats to ECG images, we opt to use the ECG beats directly to construct the ECG images. In other words, the ECG beat values are directly saved as ECG images. Three deep convolutional neural network (CNN) approaches are considered in ECG beat classification. These approaches ensure end-to-end learning schema, fine-tuning of pre-trained CNN models, extraction of deep features and their classification using a traditional classifier, such as the support vector machine (SVM) or deep machine learning approaches. The well-known MIT-BIH arrhythmia database is considered in the evaluation of the proposed deep learning approaches. The database is separated into two sets, the training and test dataset in proportions of 75% and 25%, respectively. The experimental results are evaluated using the classification accuracy score. The results show that the proposed methods have potential for use in ECG beat classification.Öğe A New Framework for Automatic Detection of Patients With Mild Cognitive Impairment Using Resting-State EEG Signals(IEEE (Institute of Electrical and Electronics Engineers), 2020) Siuly, Siuly; Alçin, Ömer Faruk; Kabir, Enamul; Şengür, Abdulkadir; Wang, Hua; Zhang, Yanchun; Whittaker, FrankMild cognitive impairment (MCI) can be an indicator representing the early stage of Alzheimier's disease (AD). AD, which is the most common form of dementia, is a major public health problem worldwide. Efficient detection of MCI is essential to identify the risks of AD and dementia. Currently Electroencephalography (EEG) is the most popular tool to investigate the presenence of MCI biomarkers. This study aims to develop a new framework that can use EEG data to automatically distinguish MCI patients from healthy control subjects. The proposed framework consists of noise removal (baseline drift and power line interference noises), segmentation, data compression, feature extraction, classification, and performance evaluation. This study introduces Piecewise Aggregate Approximation (PAA) for compressing massive volumes of EEG data for reliable analysis. Permutation entropy (PE) and auto-regressive (AR) model features are investigated to explore whether the changes in EEG signals can effectively distinguish MCI from healthy control subjects. Finally, three models are developed based on three modern machine learning techniques: Extreme Learning Machine (ELM); Support Vector Machine (SVM) and K-Nearest Neighbours (KNN) for the obtained feature sets. Our developed models are tested on a publicly available MCI EEG database and the robustness of our models is evaluated by using a 10-fold cross validation method. The results show that the proposed ELM based method achieves the highest classification accuracy (98.78%) with lower execution time (0.281 seconds) and also outperforms the existing methods. The experimental results suggest that our proposed framework could provide a robust biomarker for efficient detection of MCI patients.Öğe A New Signal to Image Mapping Procedure and Convolutional Neural Networks for Efficient Schizophrenia Detection in EEG Recordings(Institute of Electrical and Electronics Engineers Inc., 2022) Sobahi, Nebras; Çakar, Hakan; Arı, Berna; Alçin, Ömer Faruk; Şengür, AbdulkadirMachine learning has been densely used in most computer-aided medical diagnosis systems. These systems not only supported the physician’s decision but also accelerate the necessitated procedures. Electroencephalography (EEG) is an essential device for measuring the brain’s electrical activities. EEG is used to detect a series of brain disorders such as epilepsy, dementia, Parkinson’s disease, and Schizophrenia (SZ). In this work, a novel method for detecting SZ using EEG recordings is suggested. Initially, the presented technique breaks down each channel of the input EEG recordings into EEG rhythms. The wavelet transform is employed to achieve this. The 1D local binary pattern (LBP) is then used to code the acquired rhythm signals. Each row of the input picture is formed by concatenating the uniform histograms of the 1D LBP coded beats. The rows of the images are formed from the channels of the input EEG signal, while the columns of the images are constructed from the rhythms. Extreme learning machines (ELM) based autoencoders (AE) are utilized at a data augmentation step. After data augmentation, the SZ and healthy cases are classified using well-known deep transfer learning. Deep transfer learning employs a variety of pre-trained deep Convolutional Neural Network (CNN) models. Various performance assessment indicators are used to evaluate the produced outcomes. An EEG dataset that Lomonosov Moscow State University released is used in experiments, and a 97.7% accuracy score is obtained. The obtained results are also compared with several recently published methods. The comparisons show that the proposed method outperforms the compared methods. IEEEÖğe Two-stepped majority voting for efficient EEG-based emotion classification(Springer, 2020) Ismael, Aras Masood; Alçin, Ömer Faruk; Abdalla, Karmand Hussein; Şengür, AbdulkadirIn this paper, a novel approach that is based on two-stepped majority voting is proposed for efficient EEG-based emotion classification. Emotion recognition is important for human-machine interactions. Facial features- and body gestures-based approaches have been generally proposed for emotion recognition. Recently, EEG-based approaches become more popular in emotion recognition. In the proposed approach, the raw EEG signals are initially low-pass filtered for noise removal and band-pass filters are used for rhythms extraction. For each rhythm, the best performed EEG channels are determined based on wavelet-based entropy features and fractal dimension-based features. The k-nearest neighbor (KNN) classifier is used in classification. The best five EEG channels are used in majority voting for getting the final predictions for each EEG rhythm. In the second majority voting step, the predictions from all rhythms are used to get a final prediction. The DEAP dataset is used in experiments and classification accuracy, sensitivity and specificity are used for performance evaluation metrics. The experiments are carried out to classify the emotions into two binary classes such as high valence (HV) vs low valence (LV) and high arousal (HA) vs low arousal (LA). The experiments show that 86.3% HV vs LV discrimination accuracy and 85.0% HA vs LA discrimination accuracy is obtained. The obtained results are also compared with some of the existing methods. The comparisons show that the proposed method has potential in the use of EEG-based emotion classification.
