1
|
Uyulan C, Erguzel TT, Turk O, Farhad S, Metin B, Tarhan N. A Class Activation Map-Based Interpretable Transfer Learning Model for Automated Detection of ADHD from fMRI Data. Clin EEG Neurosci 2023; 54:151-159. [PMID: 36052402 DOI: 10.1177/15500594221122699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
Automatic detection of Attention Deficit Hyperactivity Disorder (ADHD) based on the functional Magnetic Resonance Imaging (fMRI) through Deep Learning (DL) is becoming a quite useful methodology due to the curse of-dimensionality problem of the data is solved. Also, this method proposes an invasive and robust solution to the variances in data acquisition and class distribution imbalances. In this paper, a transfer learning approach, specifically ResNet-50 type pre-trained 2D-Convolutional Neural Network (CNN) was used to automatically classify ADHD and healthy children. The results demonstrated that ResNet-50 architecture with 10-k cross-validation (CV) achieves an overall classification accuracy of 93.45%. The interpretation of the results was done via the Class Activation Map (CAM) analysis which showed that children with ADHD differed from controls in a wide range of brain areas including frontal, parietal and temporal lobes.
Collapse
Affiliation(s)
- Caglar Uyulan
- Department of Mechanical Engineering, Faculty of Engineering and Architecture, İzmir Katip Çelebi University, İzmir, Turkey
| | | | - Omer Turk
- Department of Computer Programming, Vocational School, Mardin Artuklu University, Mardin, Turkey
| | - Shams Farhad
- Department of Neuroscience, 232990Uskudar University, Istanbul, Turkey
| | - Baris Metin
- Department of Neuroscience, 232990Uskudar University, Istanbul, Turkey
| | - Nevzat Tarhan
- Department of Psychiatry, NPIstanbul Brain Hospital, Istanbul, Turkey
| |
Collapse
|
2
|
Adamson M, Hadipour A, Turkin T, Uyulan C, Kazemi R, Phillips A, Tarhan N. O001 / #892 A DEEP LEARNING APPROACH TO EVALUATING SEX DIFFERENCES IN ANTIDEPRESSANT RESPONSE TO NEUROMODULATION USING EEG IN MAJOR DEPRESSIVE DISORDER. Neuromodulation 2022. [DOI: 10.1016/j.neurom.2022.08.038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
|
3
|
Adamson M, Hadipour AL, Uyulan C, Erguzel T, Cerezci O, Kazemi R, Phillips A, Seenivasan S, Shah S, Tarhan N. Sex differences in rTMS treatment response: A deep learning-based EEG investigation. Brain Behav 2022; 12:e2696. [PMID: 35879921 PMCID: PMC9392544 DOI: 10.1002/brb3.2696] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Revised: 06/01/2022] [Accepted: 06/08/2022] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION The present study aimed to investigate sex differences in response to repetitive transcranial magnetic stimulation (rTMS) in Major Depressive Disorder (MDD) patients. Identifying the factors that mediate treatment response to rTMS in MDD patients can guide clinicians to administer more appropriate, reliable, and personalized interventions. METHODS In this paper, we developed a novel pipeline based on convolutional LSTM-based deep learning (DL) to classify 25 female and 25 male patients based on their rTMS treatment response. RESULTS Five different classification models were generated, namely pre-/post-rTMS female (model 1), pre-/post-rTMS male (model 2), pre-rTMS female responder versus pre-rTMS female nonresponders (model 3), pre-rTMS male responder vs. pre-rTMS male nonresponder (model 4), and pre-rTMS responder versus nonresponder of both sexes (model 5), achieving 93.3%, 98%, 95.2%, 99.2%, and 96.6% overall test accuracy, respectively. CONCLUSION These results indicate the potential of our approach to be used as a response predictor especially regarding sex-specific antidepressant effects of rTMS in MDD patients.
Collapse
Affiliation(s)
- M Adamson
- Department of Neurosurgery, Stanford University School of Medicine, Stanford, California.,Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - A L Hadipour
- Department of Cognitive Sciences, University of Messina, Messina, Italy
| | - C Uyulan
- Department of Mechanical Engineering, İzmir Katip Çelebi University, İzmir, Turkey
| | - T Erguzel
- Faculty of Engineering and Natural Sciences, Üsküdar University, Istanbul, Turkey
| | - O Cerezci
- Faculty of Health Sciences, Üsküdar University, Istanbul, Turkey
| | - R Kazemi
- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran
| | - A Phillips
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - S Seenivasan
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - S Shah
- Department of Rehabilitation Service, VA Palo Alto Healthcare System, Palo Alto, California
| | - N Tarhan
- Faculty of Humanities and Social Sciences, Üsküdar University, Istanbul, Turkey
| |
Collapse
|
4
|
Uyulan C, de la Salle S, Erguzel TT, Lynn E, Blier P, Knott V, Adamson MM, Zelka M, Tarhan N. Depression Diagnosis Modeling With Advanced Computational Methods: Frequency-Domain eMVAR and Deep Learning. Clin EEG Neurosci 2022; 53:24-36. [PMID: 34080925 DOI: 10.1177/15500594211018545] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Electroencephalogram (EEG)-based automated depression diagnosis systems have been suggested for early and accurate detection of mood disorders. EEG signals are highly irregular, nonlinear, and nonstationary in nature and are traditionally studied from a linear viewpoint by means of statistical and frequency features. Since, linear metrics present certain limitations and nonlinear methods have proven to be an efficient tool in understanding the complexities of the brain in the identification of underlying behavior of biological signals, such as electrocardiogram, EEG and magnetoencephalogram and thus, can be applied to all nonstationary signals. Various nonlinear algorithms can be used in the analysis of EEG signals. In this research paper, we aim to develop a novel methodology for EEG-based depression diagnosis utilizing 2 advanced computational techniques: frequency-domain extended multivariate autoregressive (eMVAR) and deep learning (DL). We proposed a hybrid method comprising a pretrained ResNet-50 and long-short term memory (LSTM) to capture depression-specific information and compared with a strong conventional machine learning (ML) framework having eMVAR connectivity features. The following 8 causality measures, which interpret the interaction mechanisms among spectrally decomposed oscillations, were used to extract features from multivariate EEG time series: directed coherence (DC), directed transfer function (DTF), partial DC (PDC), generalized PDC (gPDC), extended DC (eDC), delayed DC (dDC), extended PDC (ePDC), and delayed PDC (dPDC). The classification accuracies were 84% with DC, 85% with DTF, 95.3% with PDC, 95.1% with gPDC, 84.8% with eDC, 84.6% with dDC, 84.2% with ePDC, and 95.9% with dPDC for the eMVAR framework. Through a DL framework (ResNet-50 + LSTM), the classification accuracy was achieved as 90.22%. The results demonstrate that our DL methodology is a competitive alternative to the strong feature extraction-based ML methods in depression classification.
Collapse
Affiliation(s)
| | - Sara de la Salle
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | | | - Emma Lynn
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | - Pierre Blier
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | - Verner Knott
- Institute of Mental Health Research, 6363University of Ottawa, Ottawa, ON, Canada.,6363University of Ottawa, Ottawa, ON, Canada
| | | | | | - Nevzat Tarhan
- 232990Uskudar University, Istanbul, Turkey.,NPIstanbul Hospital, Istanbul, Turkey
| |
Collapse
|
5
|
Uyulan C, Gokten E. Prediction of Long-term Prognosis of Children with Attention-deficit/Hyperactivity Disorder in Conjunction with Deep Neural Network Regression. PBS 2022. [DOI: 10.5455/pbs.20220602052257] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Background: Although attention-deficit/hyperactivity disorder (ADHD) symptoms decrease with the factors such as age, many individuals keep suffering from the disorder in adolescence and adulthood.
Objective: In this paper, a deep learning algorithm was built to forecast the prognosis of ADHD, using the patient's clinical features, the measurement of their response to treatment, and the degree of improvement seen after six years of follow-up.
Participants and Settings: The clinical findings such as socio-demographic data of 433 patients followed by the child and adolescent psychiatry department for an average of 6 years with diagnoses of ADHD, and ADHD sub-type, accompanying oppositional/conduct disorders, other psychiatric and organic disorders, the effectiveness of psychotherapy and medication on attention, academic status, and behavioral problems were used to help predict prognosis.
Methods: A deep neural network (DNN) learning-based regressor was used to make a prediction model.
Results: The results obtained from the DNN regression model achieved accurate predictions for all outputs. The mean error for all outputs was evaluated as mean-squared error (mse) and 0.0068 mean-absolute error (mae), respectively. The R-value was evaluated as 0.91316. It was proven that the model prediction power was adequate as tested with these metrics.
Conclusions: This methodology improves the prediction of ADHD prognosis and provides a robust predictive model. Studies with larger samples may support the results.
Collapse
|
6
|
Adamson M, Hadipour A, Turkin T, Uyulan C, Kazemi R, Phillips A, Seenivasan S, Eshghi E, Tarhan N. A deep learning approach to evaluating sex differences in antidepressant response to neuromodulation using EEG in major depressive disorder. Brain Stimul 2021. [DOI: 10.1016/j.brs.2021.10.273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
|
7
|
Gokten ES, Uyulan C. Prediction of the development of depression and post-traumatic stress disorder in sexually abused children using a random forest classifier. J Affect Disord 2021; 279:256-265. [PMID: 33074145 DOI: 10.1016/j.jad.2020.10.006] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 09/29/2020] [Accepted: 10/04/2020] [Indexed: 10/23/2022]
Abstract
BACKGROUND Depression and post-traumatic stress disorder (PTSD) are among the most common psychiatric disorders observed in children and adolescents exposed to sexual abuse. OBJECTIVE The present study aimed to investigate the effects of many factors such as the characteristics of a child, abuse, and the abuser, family type of the child, and the role of social support in the development of psychiatric disorders using machine learning techniques. PARTICIPANTS AND SETTINGS The records of 482 children and adolescents who were determined to have been sexually abused were examined to predict the development of depression and PTSD. METHODS Each child was evaluated by a child and adolescent psychiatrist in the psychiatric aspect according to the DSM-V. Through the data of both groups, a predictive model was established based on a random forest classifier. RESULTS The mean values and standard deviation of the 10-k cross-validated results were obtained as accuracy: 0.82% (+/- 0.19%), F1: 0.81% (+/- 0.19%), precision: 0.81% (+/- 0.19%), recall: 0.80% (+/- 0.19%) for children with depression; and accuracy: 0.72% (+/- 0.12%), F1: 0.71% (+/- 0.12%), precision: 0.72% (+/- 0.12%), recall: 0.71% (+/- 0.12%) for children with PTSD, respectively. ROC curves were drawn for both, and the AUC results were obtained as 0.88 for major depressive disorder and 0.76 for PTSD. CONCLUSIONS Machine learning techniques are powerful methods that can be used to predict disorders that may develop after sexual abuse. The results should be supported by studies with larger samples, which are repeated and applied to other risk groups.
Collapse
Affiliation(s)
- Emel Sari Gokten
- Assoc Prof of Child and Adolescent Psychiatry, Uskudar University Medical Faculty, Istanbul, Turkey.
| | - Caglar Uyulan
- Assist Prof of Mechatronics Engineering Department, Zonguldak Bulent Ecevit University Faculty of Engineering, Zonguldak, Turkey.
| |
Collapse
|
8
|
Uyulan C, Ergüzel TT, Unubol H, Cebi M, Sayar GH, Nezhad Asad M, Tarhan N. Major Depressive Disorder Classification Based on Different Convolutional Neural Network Models: Deep Learning Approach. Clin EEG Neurosci 2021; 52:38-51. [PMID: 32491928 DOI: 10.1177/1550059420916634] [Citation(s) in RCA: 36] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
The human brain is characterized by complex structural, functional connections that integrate unique cognitive characteristics. There is a fundamental hurdle for the evaluation of both structural and functional connections of the brain and the effects in the diagnosis and treatment of neurodegenerative diseases. Currently, there is no clinically specific diagnostic biomarker capable of confirming the diagnosis of major depressive disorder (MDD). Therefore, exploring translational biomarkers of mood disorders based on deep learning (DL) has valuable potential with its recently underlined promising outcomes. In this article, an electroencephalography (EEG)-based diagnosis model for MDD is built through advanced computational neuroscience methodology coupled with a deep convolutional neural network (CNN) approach. EEG recordings are analyzed by modeling 3 different deep CNN structure, namely, ResNet-50, MobileNet, Inception-v3, in order to dichotomize MDD patients and healthy controls. EEG data are collected for 4 main frequency bands (Δ, θ, α, and β, accompanying spatial resolution with location information by collecting data from 19 electrodes. Following the pre-processing step, different DL architectures were employed to underline discrimination performance by comparing classification accuracies. The classification performance of models based on location data, MobileNet architecture generated 89.33% and 92.66% classification accuracy. As to the frequency bands, delta frequency band outperformed compared to other bands with 90.22% predictive accuracy and area under curve (AUC) value of 0.9 for ResNet-50 architecture. The main contribution of the study is the delineation of distinctive spatial and temporal features using various DL architectures to dichotomize 46 MDD subjects from 46 healthy subjects. Exploring translational biomarkers of mood disorders based on DL perspective is the main focus of this study and, though it is challenging, with its promising potential to improve our understanding of the psychiatric disorders, computational methods are highly worthy for the diagnosis process and valuable in terms of both speed and accuracy compared with classical approaches.
Collapse
Affiliation(s)
- Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Ecevit University, Zonguldak, Turkey
| | - Türker Tekin Ergüzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Huseyin Unubol
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Merve Cebi
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | | | - Nevzat Tarhan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| |
Collapse
|
9
|
Erguzel TT, Uyulan C, Unsalver B, Evrensel A, Cebi M, Noyan CO, Metin B, Eryilmaz G, Sayar GH, Tarhan N. Entropy: A Promising EEG Biomarker Dichotomizing Subjects With Opioid Use Disorder and Healthy Controls. Clin EEG Neurosci 2020; 51:373-381. [PMID: 32043373 DOI: 10.1177/1550059420905724] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/29/2023]
Abstract
Electroencephalography (EEG) signals are known to be nonstationary and often multicomponential signals containing information about the condition of the brain. Since the EEG signal has complex, nonlinear, nonstationary, and highly random behaviour, numerous linear feature extraction methods related to the short-time windowing technique do not satisfy higher classification accuracy. Since biosignals are highly subjective, the symptoms may appear at random in the time scale and very small variations in EEG signals may depict a definite type of brain abnormality it is valuable and vital to extract and analyze the EEG signal parameters using computers. The challenge is to design and develop signal processing algorithms that extract this subtle information and use it for diagnosis, monitoring, and treatment of subjects suffering from psychiatric disorders. For this purpose, finite impulse response-based filtering process was employed rather than traditional time and frequency domain methods. Finite impulse response subbands were analyzed further to obtain feature vectors of different entropy markers and these features were fed into a classifier namely multilayer perceptron. The performances of the classifiers were finally compared considering overall classification accuracies, area under receiver operating characteristic curve scores. Our results underline the potential benefit of the introduced methodology is promising and is to be treated as a clinical interface in dichotomizing substance use disorders subjects and for other medical data analysis studies. The results also indicate that entropy estimators can distinguish normal and opioid use disorder subjects. EEG data and theta frequency band have distinctive capability for almost all types of entropies while nonextensive Tsallis entropy outperforms compared with other types of entropies.
Collapse
Affiliation(s)
- Turker Tekin Erguzel
- Department of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, Turkey
| | - Caglar Uyulan
- Department of Mechatronics, Faculty of Engineering, Bulent Evevit University, Zonguldak, Turkey
| | - Baris Unsalver
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Alper Evrensel
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Merve Cebi
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey
| | - Cemal Onur Noyan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Baris Metin
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gul Eryilmaz
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Gokben Hizli Sayar
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| | - Nevzat Tarhan
- Department of Psychology, Faculty of Humanities and Social Sciences, Uskudar University, Istanbul, Turkey.,NP Istanbul Brain Hospital, Istanbul, Turkey
| |
Collapse
|
10
|
Uyulan C, Ergüzel TT, Tarhan N. Entropy-based feature extraction technique in conjunction with wavelet packet transform for multi-mental task classification. BIOMED ENG-BIOMED TE 2019; 64:529-542. [PMID: 30849042 DOI: 10.1515/bmt-2018-0105] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2018] [Accepted: 12/05/2018] [Indexed: 11/15/2022]
Abstract
Event-related mental task information collected from electroencephalography (EEG) signals, which are functionally related to different brain areas, possesses complex and non-stationary signal features. It is essential to be able to classify mental task information through the use in brain-computer interface (BCI) applications. This paper proposes a wavelet packet transform (WPT) technique merged with a specific entropy biomarker as a feature extraction tool to classify six mental tasks. First, the data were collected from a healthy control group and the multi-signal information comprised six mental tasks which were decomposed into a number of subspaces spread over a wide frequency spectrum by projecting six different wavelet basis functions. Later, the decomposed subspaces were subjected to three entropy-type statistical measure functions to extract the feature vectors for each mental task to be fed into a backpropagation time-recurrent neural network (BPTT-RNN) model. Cross-validated classification results demonstrated that the model could classify with 85% accuracy through a discrete Meyer basis function coupled with a Renyi entropy biomarker. The classifier model was finally tested in the Simulink platform to demonstrate the Fourier series representation of periodic signals by tracking the harmonic pattern. In order to boost the model performance, ant colony optimization (ACO)-based feature selection method was employed. The overall accuracy increased to 88.98%. The results underlined that the WPT combined with an entropy uncertainty measure methodology is both effective and versatile to discriminate the features of the signal localized in a time-frequency domain.
Collapse
Affiliation(s)
- Caglar Uyulan
- Department of Mechatronics Engineering, Bulent Ecevit University, Zonguldak, Turkey
| | - Türker Tekin Ergüzel
- Department of Software Engineering, Uskudar University, Altunizade, Haluk Turksory Street, No: 14, 34662 Uskudar/Istanbul, Turkey
| | - Nevzat Tarhan
- Department of Psychology, Uskudar University, Istanbul, Turkey
| |
Collapse
|
11
|
|