Aydin Akan

Aydin Akan
Izmir University of Economics · Department of Electrical & Electronics Engineering

Professor

About

379
Publications
76,193
Reads
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2,556
Citations
Citations since 2017
175 Research Items
1817 Citations
20172018201920202021202220230100200300400500
20172018201920202021202220230100200300400500
20172018201920202021202220230100200300400500
20172018201920202021202220230100200300400500
Introduction
Biomedical Signal and Image Processing, Machine Learning and Deep Learning Solutions to Biomedical Engineering Problems, Intelligent Systems.
Additional affiliations
February 2020 - present
Izmir University of Economics
Position
  • Professor (Full)
February 2020 - present
Izmir University of Economics
Position
  • Professor (Full)
March 2017 - January 2020
Izmir Katip Celebi Universitesi
Position
  • Professor (Full)
Education
August 1992 - April 1996
University of Pittsburgh
Field of study
  • Electrical Engineering
September 1988 - February 1991
Istanbul Technical University
Field of study
  • Electronics and Communications Engineering
September 1984 - July 1988
Uludag University
Field of study
  • Electronics Engineering

Publications

Publications (379)
Conference Paper
Full-text available
A variety of artificial intelligence (AI) approaches are applied for the classification of hand movements in systems that use electromyography (EMG), which measures the electrical activity of muscles. In AI approaches, machine learning (ML) is frequently preferred and researched for this classification issue. In this study, hand gesture classificat...
Conference Paper
Many innovations have occurred with today's technologies, the issue of detecting and analyzing human behavior through image processing techniques has also gained popularity recently. However, studies carried out with image processing in the field of education are not very common and many students' interests in class courses are unknown. Aim of this...
Conference Paper
The sense of smell is one of the oldest senses of humankind and is able to provide valuable information from the mood of a person to purchase intention. In this study, five non-linear features; 3 Hjorth Parameters namely, activity, complexity, and mobility, Higuchi's Fractal Dimension, and Lempel-Ziv Complexity were used to differentiate EEG signal...
Article
Background Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis o...
Conference Paper
Full-text available
Artificial intelligence is effectively utilized for hand gesture classification in myoelectric systems. In this study, hand movement classification is performed with ML algorithms using electromyography (EMG) signals of 7 hand gestures. The Hilbert-Huang Transform (HHT) was applied to the preprocessed EMG signals to obtain the Hilbert-Huang spectru...
Article
Hand gesture-based systems are one of the most effective technological advances and continue to develop with improvements in the field of human–computer interaction. Surface electromyography (sEMG) is utilized as a popular input data for gesture classification with elevated accuracy and advanced control capability. This paper presents a comparative...
Article
Dementia is one of the most common neurological disorders causing defection of cognitive functions, and seriously affects the quality of life. In this study, various methods have been proposed for the detection and follow-up of Alzheimer's dementia (AD) with advanced signal processing methods by using electroencephalography (EEG) signals. Signal de...
Conference Paper
In this study, a method is proposed to detect the presence of olfactory stimuli from Electroencephalogram (EEG) signals to be used in neuromarketing applications. Odor is used in different ways in neuromarketing applications since it stimulates various emotions. Multi-channel EEG signals were recorded from the volunteers while they were subjected t...
Data
Supplementary File of https://www.researchgate.net/publication/360452403_Hand_gesture_classification_using_time-frequency_images_and_transfer_learning_based_on_CNN
Article
Full-text available
This paper presents an electromyography (EMG) signal dataset for use in human-computer interaction studies. The dataset includes 4-channel surface EMG data from 40 participants with an equal gender distribution. The gestures in the data are rest or neutral state, extension of the wrist, flexion of the wrist, ulnar deviation of the wrist, radial dev...
Article
Background: Transcutaneous electrogastrography is a novel modality to assess the human stomach's gastric myoelectrical activity. The purpose of this study was to compare functional dyspepsia, joint hypermobility, and diabetic gastroparesis patients with healthy control subjects in terms of gastric motility abnormalities through electrogastrography...
Article
Full-text available
Computational complexity is one of the drawbacks of orthogonal frequency division multiplexing (OFDM)-index modulation (IM) systems. In this study, a novel IM technique is proposed for OFDM systems by considering the null subcarrier locations (NSC-OFDM-IM) within a predetermined group in the frequency domain. So far, a variety of index modulation t...
Book
Time-Frequency Signal Analysis and Processing (TFSAP) is a collection of theory and algorithms used for analysis and processing of nonstationary signals, as found in a wide range of applications including telecommunications, radar, and biomedical engineering. This book brings together the main knowledge of TFSAP, from theory to applications, in a u...
Chapter
Attention Deficit Hyperactivity Disorder (ADHD) is a neuropsychiatric disorder that affects children and adults. The fact that ADHD symptoms differ from individual to individual, that similar symptoms are seen in other psychiatric diseases, and that the tests used do not contain objectivity are important obstacles to the correct diagnosis of the di...
Article
Epilepsy is a persistent and recurring neurological condition in a community of brain neurons that results from sudden and abnormal electrical discharges. This paper introduces a new form of assessment and interpretation of the changes in electroencephalography (EEG) recordings from different brain regions in epilepsy disorders based on graph analy...
Article
This research presents a new method for detecting obsessive–compulsive disorder (OCD) based on time–frequency analysis of multi-channel electroencephalogram (EEG) signals using the multi-variate synchrosqueezing transform (MSST). With the evolution of multi-channel sensor implementations, the employment of multi-channel techniques for the extractio...
Article
Epilepsy is one of the most common brain disorders worldwide. The most frequently used clinical tool to detect epileptic events and monitor epilepsy patients is the EEG recordings. There have been proposed many computer-aided diagnosis systems using EEG signals for the detection and prediction of seizures. In this study, a novel method based on Fou...
Article
Most real-life signals exhibit non-stationary characteristics. Processing of such signals separately in the time-domain or in the frequency-domain does not provide sufficient information as their spectral properties change over time. Traditional methods such as the Fourier transform (FT) perform a transformation from time-domain to frequency-domain...
Article
Background: Quadratus lumborum (QL) discrete region extensions might change depending on whether leg length discrepancy (LLD) individually has any extra erector spinae action in the lumbar spine, which can result in serious injury to the lower extremities and lumbar vertebrae. Objective: This study aims to investigate the effect of QL muscle act...
Conference Paper
Full-text available
Özetçe-Majör depresif bozukluk (MDD), dünya genelinde sıklıkla görülen bir duygu durum hastalığıdır. Hastalığın belirtileri kişiyi olumsuz etkilediği için erken teşhisi ve tedaviye başlanması büyük önem taşımaktadır. Bu çalışmanın amacı, MDD hastalarının sağlıklı bireylerden ayrılmasını sağlayan objektif bir yöntem geliştirmektir. 16 MDD hastası ve...
Article
Empirical Mode Decomposition (EMD) provides an adaptive signal processing tool, and its multivariate extension is useful to model multichannel signals. Recently, EMD and multivariate EMD have successfully been applied to solve different signal processing problems. Electroencephalogram signals are often employed to explore the emotional concepts for...
Article
Three main requirements of a successful application of deep learning are the network architecture, a large enough training dataset, and a good optimization algorithm. In this paper we mainly focus on the optimization part. We propose a training algorithm for convolutional neural networks which makes use of both first and second order derivatives fo...
Article
The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed...
Conference Paper
Full-text available
Epilepsy is a neurological disease that is very common worldwide. In the literature, patient's electroencephalography (EEG) signals are frequently used for an epilepsy diagnosis. However, the success of epileptic examination procedures from quantitative EEG signals is limited. In this paper, a high-resolution time-frequency (TF) representation call...
Conference Paper
Full-text available
Epilepsy is a neurological disorder that affects many people all around the world, and its early detection is a topic of research widely studied in signal processing community. In this paper, a new technique that was introduced to solve problems of fluid dynamics called Dynamic Mode Decomposition (DMD), is used to classify seizure and non-seizure e...
Article
Dynamic mode decomposition (DMD) is a new matrix decomposition method proposed as an iterative solution to problems in fluid flow analysis. Recently, DMD algorithm has successfully been applied to the analysis of non-stationary signals such as neural recordings. In this study, we propose single-channel, and multi-channel EEG based DMD approaches fo...
Article
Epilepsy is a neurological disease that is very common worldwide. Patient’s electroencephalography (EEG) signals are frequently used for the detection of epileptic seizure segments. In this paper, a high-resolution time-frequency (TF) representation called Synchrosqueezing Transform (SST) is used to detect epileptic seizures. Two different EEG data...
Conference Paper
Full-text available
The heart is the most critical organ for the sustainability of life. Arrhythmia is any irregularity of heart rate that causes an abnormality in your heart rhythm. Clinical analysis of Electrocardiogram (ECG) signals is not enough to quickly identify abnormalities in the heart rhythm. This paper proposes a deep learning method for the accurate detec...
Conference Paper
Full-text available
The Electromyography (EMG) signal is a non-stationary bio-signal based on the measurement of the electrical activity of the muscles. EMG based recognition systems play an important role in many fields such as diagnosis of neuromuscular diseases, human-computer interactions, console games, sign language detection, virtual reality applications, and a...
Conference Paper
Full-text available
Özetçe-Bu çalışmada derin öğrenme yöntemi kullanılarak yüz görüntülerinden duygu durum tespiti yapılması hedeflenmiştir. Etik kurul onayı alınmış çalışmada, 7 farklı yüz ifadesini (mutlu, üzgün, şaşırmış, kızgın, iğrenmiş, korkmuş ve tarafsız) taklit ederken 20 adedi erkek ve 20 adedi kadın katılımcıdan alınan videolar kullanılarak özel veri seti o...
Conference Paper
Full-text available
Computer systems working with artificial intelligence can recognize movements and gestures to be used for many purposes. In order to perform recognition, the electrical activity of the muscles can be utilized which is represented by electromyography (EMG) and EMG is not a stationary biological signal. EMG based movement recognition systems have an...
Conference Paper
Full-text available
Time-frequency representation (TFR) provides a good analysis for periodic signals; however, they are insufficient for nonstationary signals. The synchrosqueezing transform (SST) provides a strong analysis of nonstationary signals. The signal has different synchrosqueezing transformations that are implemented using different TFR. This paper provides...
Conference Paper
Full-text available
Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures. Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become...
Chapter
Full-text available
Electroencephalography (EEG) signals are frequently used for the detection of epileptic seizures. In this chapter, advanced signal analysis methods such as Empirical Mode Decomposition (EMD), Ensembe (EMD), Dynamic mode decomposition (DMD), and Synchrosqueezing Transform (SST) are utilized to classify epileptic EEG signals. EMD and its derivative,...
Conference Paper
Full-text available
Özetçe—Aritmi, kalp ritminizde anormallik oluşmasına sebep olan kalp hızının düzensizliğidir. Elektrokardiyografi (EKG) sinyalinin manuel analizi, kalp ritmindeki anormallikleri hızlı bir şekilde tanımlamak için yeterli değildir. Bu çalışma, beş farklı aritmi tipinin tespiti için 2B evrişimsel sinir ağları (ESA) mimarisine dayanan derin öğrenme yak...
Article
Obsessive-compulsive disorder (OCD) is one of the neuropsychiatric disorders qualified by intrusive and iterative annoying thoughts and mental attitudes that are activated by these thoughts. In recent studies, advanced signal processing techniques have been favored to diagnose OCD. This research suggests four different measurements- intrinsic phase...
Data
Also, you can reach the source code, re-labeled DEAP dataset, and the location data of this study via this link ; https://github.com/mkfzdmr/Deep-Learning-based-Emotion-Recognition
Preprint
Full-text available
Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and provides a diagnostic mean for heart-related diseases. An arrhythmia is any irregularity of heartbeat that causes an abnormality in one’s heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of...
Preprint
Full-text available
Background: Electrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis...
Preprint
Full-text available
The emotional state of people plays a key role in physiological and behavioral human interaction. Emotional state analysis entails many fields such as neuroscience, cognitive sciences, and biomedical engineering because the parameters of interest contain the complex neuronal activities of the brain. Electroencephalogram (EEG) signals are processed...
Article
Full-text available
The application of EEG-based emotional states is one of the most vital phases in the context of neural response decoding. Emotional response mostly appears in the presence of visual, auditory, tactile, and gustatory arousals. In our work, we use visual stimuli to evaluate the emotional feedback. One of the best performing methods in emotion estimat...
Article
Full-text available
Background: Epilepsy is one of the most common neurological disorders associated with disruption of brain activity. In the classification and detection of epileptic seizures, electroencephalography (EEG) measurements, which record the electrical activities of the brain, are frequently used. Empirical mode decomposition (EMD) and its derivative, en...
Article
Full-text available
Indocyanine green (ICG) provides an advantage in the imaging of deep tumors as it can reach deeper location without being absorbed in the upper layers of biological tissues in the wavelengths, which named “therapeutic window” in the tissue engineering. Unfortunately, rapid elimination and short‐term stability in aqueous media limited its use as a f...
Article
The prevalence of metabolic disorders has increased rapidly as such they become a major health issue recently. Despite the definition of genetic associations with obesity and cardiovascular diseases, they constitute only a small part of the incidence of disease. Environmental and physiological effects such as stress, behavioral and metabolic distur...
Chapter
Abstract- Attention Deficit Hyperactivity Disorder (ADHD), one of the most common chronic disorders of our times, is a neuropsychiatric disorder in which individuals experience degradation in personal, academic, and social functioning due to excessive hyperactivity, inattention and concentration disorder and impulsivity symptoms. The health expert...
Data
EEG Dataset for Emotion Recognition used in https://www.researchgate.net/publication/329991114_Emotion_Recognition_from_EEG_Signals_by_Using_Empirical_Mode_Decomposition
Conference Paper
Full-text available
Arrhythmia is irregular changes of normal heart rhythm and effective manual identifying of them require a lot of time and depends on experience of clinicians. This paper proposes deep learning-based novel 2-D convolutional neural network (CNN) approach for accurate classification of five different arrhythmia types. The performance of the proposed a...
Conference Paper
Full-text available
Emotions are complex and may vary from person to person in a situation. The purpose of this study is to perform emotion analysis by using specific signal processing algorithms, to find the features and channels that are effective in the emotion recognition by using 60 visual stimuli with obtained EEG signals from the 32-channel EEG device that is b...
Conference Paper
Full-text available
In this study, Raspberry Pi 3B+ based Electrocardiogram (ECG) device has been designed for real-time detection of cardiac arrhythmia. ECG signals that were taken by using AD8232 heart rate sensor have been displayed with developed software using Python in real-time. By using R-peak detection algorithm, we determined beats per minute (bpm) and arrhy...
Conference Paper
Full-text available
Emotions play a significant role in daily life by encouraging the individual in the survival, decision making, guessing, and communication processes. Through emotions can be explained with the activation of anatomical structures in certain regions of brain with nervous system the emotions can be understood by electroencephalogram (EEG) signals. In...
Conference Paper
Full-text available
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 Netw...
Conference Paper
Full-text available
Emotion detection is very crucial role on diagnosis of brain disorders and psychological disorders. Electroencephalogram (EEG) is useful tool that obtain complex physiological brain signals from human. In this paper, we proposed a novel approach for emotional state estimation using convolutional neural network (CNN) based deep learning technique fr...
Conference Paper
Full-text available
This study focuses on a signal processing method, which qualifies the relation between the emotional stimulation and emotional changes in healthy participants. For this purpose, an emotional EEG-based database was created by using stimuli which represented to the participants by using Nencki Affective Picture System (NAPS) for the scope of this stu...
Conference Paper
Full-text available
Counting of microbial colonies is crucial due to the applications of medical microbiology to search and detect the causes of diseases. While different tasks performed, the counting process of bacteria colonies is provided either by the searcher manually or by a common software, nowadays. The manual counting of bacteria colonies is tiresome, eye-str...