Papers by Reza Sadighzadeh
Classification of epileptic EEG data by using ensemble empirical mode decomposition
In this study, our aim is to distinguish pre-seizure and seizure data from epileptic EEG signals ... more In this study, our aim is to distinguish pre-seizure and seizure data from epileptic EEG signals using Ensemble Empirical Mode Decomposition (EEMD) and various classifiers. For this purpose, epileptic EEG data from 13 epileptic patients have been recorded using surface electrodes at İzmir Kâtip Çelebi University School of Medicine, Neurology Department. First, EEG signals are divided into two parts as pre-seizure and seizure and decomposed into intrinsic mode functions (IMFs) using EEMD. Total power and higher order frequency moments are calculated as features from the first IMF by periodogram method. Extracted features are classified using Naive Bayes, K-nearest neighbors and Linear Discriminant Analysis methods. From the obtained classification results, it is seen that the Naive Bayes classifier outperforms the K-nearest neighbor and Linear Discriminant Analysis classification methods in pre-seizure and seizure data discrimination with maximum 100% and minimum 77% accuracy.
An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG
Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications
2022 30th Signal Processing and Communications Applications Conference (SIU), May 15, 2022
An Assessment of Linear and Nonlinear Features for Detecting Olfactory Stimulus in EEG
2022 Medical Technologies Congress (TIPTEKNO)
Classification of epileptic EEG data by using ensemble empirical mode decomposition
2018 26th Signal Processing and Communications Applications Conference (SIU), 2018
In this study, our aim is to distinguish pre-seizure and seizure data from epileptic EEG signals ... more In this study, our aim is to distinguish pre-seizure and seizure data from epileptic EEG signals using Ensemble Empirical Mode Decomposition (EEMD) and various classifiers. For this purpose, epileptic EEG data from 13 epileptic patients have been recorded using surface electrodes at İzmir Kâtip Çelebi University School of Medicine, Neurology Department. First, EEG signals are divided into two parts as pre-seizure and seizure and decomposed into intrinsic mode functions (IMFs) using EEMD. Total power and higher order frequency moments are calculated as features from the first IMF by periodogram method. Extracted features are classified using Naive Bayes, K-nearest neighbors and Linear Discriminant Analysis methods. From the obtained classification results, it is seen that the Naive Bayes classifier outperforms the K-nearest neighbor and Linear Discriminant Analysis classification methods in pre-seizure and seizure data discrimination with maximum 100% and minimum 77% accuracy.
Real Time Emotion Recognition from Facial Expressions Using CNN Architecture
2019 Medical Technologies Congress (TIPTEKNO), 2019
Emotion is an important topic in different fields such as biomedical engineering, psychology, neu... more Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.
Arrhythmia Detection on ECG Signals by Using Empirical Mode Decomposition
2018 Medical Technologies National Congress (TIPTEKNO), 2018
One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardia... more One of the main causes of sudden deaths is heart disease. Early detection and treatment of cardiac arrhythmias prevent the problem from reaching sudden deaths. The purpose of this study is to develop an arrhythmia detection algorithm based on Empirical Mode Decomposition (EMD). This algorithm consists of four steps: Preprocessing, Empirical Mode Decomposition, feature extraction and classification. Six arrhythmia types were used for differentiate normal and arrhythmic signals obtained from the MIT-BIH Arrhythmia database. These are normal (N), left bundle branch block (LBBB), right bundle branch block (RBBB), premature ventricular contraction (PVC), paced beat and atrial premature beats (APB). Three different classifiers were used to classify ECG signals. The method achieves better result with accuracy of 87% using linear discriminant analysis (LDA) classifier for detection of normal and arrhythmic signals.
Detection of Olfactory Stimulus from EEG Signals for Neuromarketing Applications
2022 30th Signal Processing and Communications Applications Conference (SIU)
2018 Medical Technologies National Congress (TIPTEKNO), 2018
This study investigates improved properties of empirical mode decomposition (EMD) for emotion rec... more This study investigates improved properties of empirical mode decomposition (EMD) for emotion recognition by using electroencephalogram (EEG) signals. The emotion recognition from EEG signals is a difficult study by the reason of nonstationary behavior of the signals. These signals are affected from complicated neural activity of brain. To analyze EEG signals, advanced signal processing techniques are required. In our study, data are collected from one channeled BIOPAC lab system. EEG signals were obtained from visual evoked potentials of 13 female and 13 male volunteers for 12 pleasant and 12 unpleasant pictures. To analyze nonlinear and nonstationary characteristics of EEG signals, an EMD-based method is proposed for emotion recognition. Various time and frequency domain techniques such as power spectral density (PSD), and higher order statistics (HOS) are used to analyze the IMFs extracted by EMD. Support vector machine (SVM), Linear discriminant analysis (LDA), and Naive Bayes classifiers are utilized for the classification of features extracted from the IMFs, and their performances are compared.
Emotion is an important topic in different fields such as biomedical engineering, psychology, neu... more Emotion is an important topic in different fields such as biomedical engineering, psychology, neuroscience and health. Emotion recognition could be useful for diagnosis of brain and psychological disorders. In recent years, deep learning has progressed much in the field of image classification. In this study, we proposed a Convolutional Neural Network (CNN) based LeNet architecture for facial expression recognition. First of all, we merged 3 datasets (JAFFE, KDEF and our custom dataset). Then we trained our LeNet architecture for emotion states classification. In this study, we achieved accuracy of 96.43% and validation accuracy of 91.81% for classification of 7 different emotions through facial expressions.
Effectiveness of Social Marketing Communication Strategies Amid COVID-19 Pandemic, 2022
The purpose of this paper is to examine the effectiveness of different social marketing communica... more The purpose of this paper is to examine the effectiveness of different social marketing communication strategies and evaluate their appropriateness to situational needs amid COVID-19 pandemic. In-Depth semi-structured interviews were conducted with participants using the Zoom video conferencing tool to investigate the effectiveness of three wearing mask campaign developed by i) health service, ii) celebrity musicians and iii) business company pertaining to the COVID-19 pandemic in Turkey. The findings suggest that during predicaments such as outbreaks, social marketing campaigns with a fear factor are effective to encourage communities to behave appropriately. Direct messages giving clear information about the crises and its risks are effective to change individuals' behaviour. Besides, a congruence between the message and the source has a great impact on the effectiveness of a health-related campaign during the COVID-19 pandemic. This study was conducted during the lockdown in COVID-19. To measure individual real-time feeling and thoughts, semi-structured in-depth interview method with open-ended questions was used. Therefore, findings of this study may help government, institutions and companies to provide more effective campaigns during extraordinary times like it is in the case with coronavirus pandemic.
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Papers by Reza Sadighzadeh