
Mehmet Akif ÖzdemirHarvard Medical School | HMS · Department of Neurology
Mehmet Akif Özdemir
PhD Candidate
Harvard'22-23
------- TMS, EEG, and AI
About
62
Publications
43,206
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
995
Citations
Introduction
Mehmet Akif Ozdemir is a biomedical engineer, researcher, and AI enthusiast. He received his BSc degrees in Biomed Eng and Comp Eng (Double Major), and his MSc degree in Elec and Comms Eng from Kocaeli Uni. Currently, he is a PhD candidate in Biomed Tech and also a Res Asst in the Dept of Biomed Eng at Izmir Katip Celebi Uni. He is also Senior Assoc Editor at BMC MED INFORM DECIS. His primary research interest includes biomedical signal and image processing, deep learning, and machine learning.
Additional affiliations
March 2018 - present
Publications
Publications (62)
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...
Background
Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.
Methods
A no...
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...
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...
Background
Repetitive transcranial magnetic stimulation (rTMS) is widely used in both research and clinical settings to modulate human brain function and behavior through the engagement of the mechanisms of plasticity. Based upon experiments using single-pulse TMS as a probe, the physiologic mechanism of these effects is often assumed to be via cha...
In this transformative study, machine learning (ML) and t‐distributed stochastic neighbor embedding (t‐SNE) are employed to interpret intricate patterns in colorimetric images of cold atmospheric plasma (CAP)‐treated water. The focus is on CAP's therapeutic potential, particularly its ability to generate reactive oxygen and nitrogen species (RONS)...
Background
The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as user dependency, time-consuming nature, and lack of sensitivity, thus paving the way for automated analysis approaches....
Plasma medicine is gaining attraction in the medical field, particularly the use of cold atmospheric plasma (CAP) in biomedicine. The chemistry of the plasma is complex, and the reactive oxygen species (ROS) within it are the basis for the biological effect of CAP on the target. Understanding how the oxidative power of ROS responds to diverse plasm...
Background: The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as their user dependency and time-consuming nature, and lack of sensitivity, and thus pave the way for automated analysis...
Background
Infectious diseases not only cause severe health problems but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. Both conventional approaches, such as antimicrobial agents, and novel approaches, like antimicrobial peptides (AMPs), are used to treat infections. However, due to the drawbacks...
Cold atmospheric plasma (CAP) applications are widely used to find solutions to various problems in the biomedical field. Nowadays, CAP has become a preferred method to be used in many studies aimed at improving the surface properties of biomaterials. CAP applications, which are frequently used for surface modification of biomaterials, increase the...
Cold atmospheric plasma (CAP) has found successful applications in diverse fields like biological decontamination, altering surface properties, and medical treatments. Its notable effectiveness against microorganisms underscores its established utility. Nevertheless, challenges persist, exemplified by the difficulty in comparing results from differ...
The current trends that incorporate artificial intelligence (AI) and medicine have created new opportunities for improvement in both early diagnosis and treatment of diseases. In this framework, AI might also have the potential to significantly revolutionize the way we approach the field of plasma medicine, an area that is quickly growing and uses...
Plasma is defined as the fourth state of matter, and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The antimicrobial effects of PALs depend on many different variables, which complicates the comparison...
Supplementary materials of https://www.researchgate.net/publication/369038369_Machine_Learning_to_Predict_the_Antimicrobial_Activity_of_Cold_Atmospheric_Plasma-Activated_Liquids
Artificial intelligence (AI)-based technologies assist users in applying the intended action when upper extremity movement cannot be fully provided. Electromyography (EMG), a depiction of muscle activity, offers various advantages when employed with AI-based systems like virtual reality applications and prosthetics controls. In this paper, a fuzzy...
Cold atmospheric plasma (CAP) has various therapeutic effects that have been broadly reported in the literature. Furthermore, the biomedical activity of CAP is primarily attributed to various reactive oxygen species (ROS). Beside the direct treatment, plasma-activated liquids (PALs) may gain similar therapeutic effects as previously reported in the...
Motor imagery (MI) refers to the mental representation of movement without any motor action. Effective classification of MI tasks is promising for patients with motor disabilities. However, achieving a reliable MI task classification by electroencephalography (EEG) is challenging. The purpose of the study is to improve classification performance by...
EEG signals are complex and low-frequency signals. Therefore, they are easily influenced by external factors. EEG artifact removal is crucial in neuroscience because artifacts have a significant impact on the results of EEG analysis. The removal of ocular artifacts is the most challenging among these artifacts. In this study, a novel ocular artifac...
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...
Disruption of the epithelial tissue's integrity leads to wounds. Damaged tissue raises the possibility of infection, which has a negative impact on many aspects of life. Recent interest has focused on several novel approaches to improve quality of life, including photobiomodulation (PBM) research that emphasizes wound modeling. The contribution of...
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...
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...
Background: Infectious diseases not only cause severe health issues but also burden the healthcare system. Therefore, the effective treatment of those diseases is crucial. A variety of conventional therapies including antibiotics as well as novel treatment methods namely antimicrobial peptides (AMP) are utilized for the treatment of infections. How...
EEG signals are complex and low-frequency signals. Therefore, they are easily influenced by external factors. EEG artifact removal is crucial in neuroscience because artifacts have a significant impact on the results of EEG analysis. The removal of ocular artifacts is the most challenging among these artifacts. In this study, a novel ocular artifac...
In this study, the four joint time-frequency (TF) moments; mean, variance, skewness, and kurtosis of TF matrix obtained from Multivariate Synchrosqueezing Transform (MSST) are proposed as features for hand gesture recognition. A publicly available dataset containing surface EMG (sEMG) signals of 40 subjects performing 10 hand gestures, was used. Th...
Plasma is defined as the fourth state of matter and non-thermal plasma can be produced at atmospheric pressure under a high electrical field. The strong and broad-spectrum antimicrobial effect of plasma-activated liquids (PALs) is now well known. The proven applicability of machine learning (ML) in the medical field is encouraging for its applicati...
Machine learning (ML) is an artificial intelligence (AI) technique that makes predictions by obtaining inferences from data using mathematical and statistical operations. ML algorithms are used to identify patterns in data. These patterns are also used to create a predictive data model [1]. ML is a great option in scenarios where classical statisti...
Supplementary File of https://www.researchgate.net/publication/360452403_Hand_gesture_classification_using_time-frequency_images_and_transfer_learning_based_on_CNN
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...
Emotion recognition from EEG signals has gained a great research interest in brain-computer interface (BCI) studies. As the result of the outstanding success of deep neural networks in the image classification area, deep learning methods have become popular in the subject of emotion classification from EEG signals. In this study, we have used the A...
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...
Background: Coronavirus disease 2019 (COVID-19) has become a pandemic since its first appearance in late 2019. Deaths caused by COVID-19 are still increasing day by day and early diagnosis has become crucial. Since current diagnostic methods have many disadvantages, new investigations are needed to improve the performance of diagnosis.
Methods: A n...
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...
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...
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...
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...
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...
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...
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...
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...
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
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...
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...
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...
Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron-emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron-emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of t...
EEG Dataset for Emotion Recognition used in
https://www.researchgate.net/publication/329991114_Emotion_Recognition_from_EEG_Signals_by_Using_Empirical_Mode_Decomposition
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...
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...
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...
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...
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...
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...
Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of t...
Plasma is defined as the fourth state of matter and can be produced at atmospheric pressure, under high electric field and at room temperature (cold). In the present study, the conversion of the sound data into electrical signals obtained during the generation of plasma discharge and the processing of these signals with different algorithms are aim...
The detection of epileptic seizure has a primary role in patient diagnosis with epilepsy. Epilepsy causes sudden and uncontrolled electrical discharges in brain cells. Recordings of the abnormal brain activities are time consuming and outcomes are very subjective, so automated detection systems are highly recommended. In this study, it is aimed to...
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 De...
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...