Non-Invasive Bio-Signal Data Classification Of Psychiatric Mood Disorders Using Modified CNN and VGG16

Year 2023, Volume: 15 Issue: 1, 323 - 332, 31.01.2023

Ali Berkan Ural

https://doi.org/10.29137/umagd.1232222

Abstract

In this study, the aim is to develop an ensemble machine learning (ML) based deep learning (DL) model classifiers to detect and compare one type of major psychiatric disorders of mood disorders (Depressive and Bipolar disorders) using Electroencephalography (EEG). The diverse and multiple non-invasive biosignals were collected retrospectively according to the granted ethical permission. The experimental part is consisted from three main parts. First part is the data collection&development, the second part is data transformation and augmentation via Spectrogram image conversion process and online Keras data augmentation part, respectively. The third and final part is to fed these image dataset into modified Convolutional Neural Network (CNN) and VGG16 models for training and testing parts to detect, compare and discriminate mood disorders types in detail with a specific healthy group. As the performance evaluation background of the mood disorder classification models, confusion matrices and receiver operating characteristics (ROC) curves were used and finally, the accuracy achieved by CNN model was 88% and VGG16 model was %90, which is an improvement of 10% compared to the previous studies in literature. Therefore, our system can help clinicians and researchers to manage, diagnose and prognosis of the mental health of people.

Keywords

Psychiatric disorders, Mood disorder, Depressive disorder, Bipolar Disorder, Deep Learning, Pretrained model classification

Modifiye CNN ve VGG16 Kullanarak Psikiyatrik Duygudurum Bozukluklarının İnvazif Olmayan Biyo-Sinyal Verilerle Sınıflandırılması

Abstract

Bu çalışmada amaç, Elektroensefalografi (EEG) kullanarak duygudurum bozukluklarının (Depresif ve Bipolar bozukluklar) bir tür majör psikiyatrik bozukluğunu saptamak ve karşılaştırmak için topluluk makine öğrenimi (ML) tabanlı derin öğrenme (DL) model sınıflandırıcıları geliştirmektir. Çeşitli ve çoklu non-invaziv biyosinyaller, verilen etik izne göre geriye dönük olarak toplandı. Deneysel kısım üç ana bölümden oluşmaktadır. Birinci bölüm veri toplama ve geliştirme, ikinci bölüm ise sırasıyla Spektrogram görüntü dönüştürme işlemi ve çevrimiçi Keras veri artırma bölümü aracılığıyla veri dönüştürme ve artırmadır. Üçüncü ve son bölüm, duygudurum bozuklukları türlerini belirli bir sağlıklı grupla ayrıntılı olarak saptamak, karşılaştırmak ve ayırt etmek için eğitim ve test bölümleri için bu görüntü veri setini değiştirilmiş Konvolüsyonel Sinir Ağı (KSA) ve VGG16 modellerine beslemektir. Duygudurum bozukluğu sınıflama modellerinin performans değerlendirme arka planı olarak karışıklık matrisleri ve alıcı işletim karakteristikleri (ROC) eğrileri kullanılmış ve son olarak KSA modelinin doğruluk oranı %88 ve VGG16 modelinin sağladığı doğruluk %90, yani %10'luk bir iyileşme olmuştur. literatürdeki önceki çalışmalarla karşılaştırılmıştır. Bu nedenle sistemimiz klinisyenlere ve araştırmacılara insanların ruh sağlığını yönetme, teşhis etme ve tahmin etme konusunda yardımcı olabilir.

Keywords

Psychiatric disorders, Mood disorder, Depressive disorder, Bipolar Disorder, Deep Learning, Pretrained model classification

References

• Acharya, UR, Oh, SL, Hagiwara, Y, Tan, JH, Adeli, H, & Subha, DP, (2018), Automated EEG- based screening of depression using deep convolutional neural network, Computer Methods and Programs in Biomedicine, 161, 103–113. https://doi. org/10.1016/j.cmpb.2018.04.012
• Aristizabal, A, Fernando, D, Denman, T, Robinson, S, Sridharan, JE, Johnston, S, Fookes, C., (2021), Identification of children at risk of schizophrenia via deep learning and EEG responses, IEEE Journal of Biomedical and Health Informatics, 25(1), 69–76. https://doi.org/10.1109/JBHI.2020.2984238
• B˘alan, O, Moise, G, Moldoveanu, A, Leordeanu, M, & Moldoveanu, F, (2019), Fear level classification based on emotional dimensions and machine learning techniques, Sensors (Basel, Switzerland), 19(7). https://doi.org/10.3390/s19071738
• B˘alan, O, Moise, G, Moldoveanu, A, Leordeanu, M, & Moldoveanu, F, (2020), An investigation of various machine and deep learning techniques applied in automatic fear level detection and acrophobia virtual therapy, Sensors (Basel, Switzerland), 20 (2). https://doi.org/10.3390/s20020496
• Biship, CM, (2007), Pattern Recognition and Machine Learning (Information Science and Statistics) (Springer-Verlag, Berlin).
• Boudouh, SS, and Bouakkaz, M, (2022), Breast Cancer: Using Deep Transfer Learning Techniques AlexNet Convolutional Neural Network For Breast Tumor Detection in Mammography Images, 2022 7th International Conference on Image and Signal Processing and their Applications (ISPA), pp. 1-7, doi: 10.1109/ISPA54004.2022.9786351.
• Dubreuil-Vall, L, Ruffini, G, & Camprodon, JA, (2020), Deep learning convolutional neural networks discriminate adult ADHD from healthy individuals on the basis of event-related spectral EEG, Frontiers in Neuroscience, 14, 251. https://doi.org/ 10.3389/fnins.2020.00251
• Garcia, CI, Grasso, F, Luchetta, A, Piccirilli, MC, Paolucci, L, and Talluri, G, (2020), A comparison of power quality disturbance detection and classification methods using CNN, LSTM and CNN-LSTM, Applied Sciences, vol. 10, no. 19, pp. 6755–6757.
• Giannakakis, G, Grigoriadis, D, Giannakaki, K, Simantiraki, O, Roniotis, A, and Tsiknakis, M, (2019), Review on psychological stress detection using biosignals, IEEE Transactions on Affective Computing, vol. 2019, Article ID 2927337, 1 page.
• Gisele, H, Barboni, M and Joaquim, CF (2015), Computer-aided diagnosis system based on fuzzy logic for breast cancer categorization, Computers in biology and medicine, 64:334–346.
• Khan, MS, Salsabil, N, Alam, MGR, Dewan, MAA, Uddin, MZ, (2022), CNN-XGBoost fusion based affective state recognition using EEG spectrogram image analysis. Sci Rep 12, 14122. https://doi.org/10.1038/s41598-022-18257-x
• Kim, D, Ramani S, and Fessler, JA, (2015), Combining Ordered Subsets and Momentum for Accelerated X-Ray CT Image Reconstruction, in IEEE Transactions on Medical Imaging, vol. 34, no. 1, pp. 167-178, doi: 10.1109/TMI.2014.2350962.
• Kuang, D, & He, L, (2014), Classification on ADHD with deep learning. In Proc. Int. Conference on Cloud Computing and Big Data 27–32 (Wuhan, China).
• Kuang, D, Guo, X, An, X, Zhao, Y, & He, L, (2014), Discrimination of ADHD based on fMRI data with deep belief network. In Proc. Int. Conference on Intelligent Computing 225–232 (Taiyuan, China).
• Kumar, S, (2021), StressNet: detecting stress in thermal videos, in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 999–1009, Waikola, HI, USA.
• Li, X, La, R, Wang, Y, Hu, B, & Zhang, X, (2020), A deep learning approach for mild depression recognition based on functional connectivity using electroencephalography, Frontiers in Neuroscience, 14. https://doi.org/10.3389/ fnins.2020.00192
• Md Manjurul, A, Md Tanvir, A, Farzana, AS, Shuva, P, Ananna, C, Shahana, AL, Md Shafwat Yazdan, M, Akhlaqur, R, Zahed, S, and Huebner, P, (2021), Detecting sars-cov-2 from chest x-ray using artificial intelligence, IEEE Access, 9:35501–35513.
• Miotto, R, Wang, F, Wang, , Jiang, X & Dudley, JT, (2017), Deep learning for healthcare: review, opportunities and challenges, Brief. Bioinformatics 19, 1236–1246.
• Mumtaz, W, & Qayyum, A, (2019), A deep learning framework for automatic diagnosis of unipolar depression, International Journal of Medical Informatics, 132, Article 103983. https://doi.org/10.1016/j.ijmedinf.2019.103983
• Murphy, KP, (2012), Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge).
• Najafabadi, MM, Villanustre, F, Khoshgoftaar, TM, Seliya, N, Wald, R, & Muharemagic, E, (2015), Deep learning applications and challenges in big data analytics. Journal of Big Data, 2(1), 1. https://doi.org/10.1186/s40537-014-0007-7
• Oh, SL, Vicnesh, J, Ciaccio, EJ, Yuvaraj, R, & Acharya, UR, (2019), Deep convolutional neural network model for automated diagnosis of schizophrenia using EEG signals, Applied Sciences, 9(14), 2870. https://doi.org/10.3390/app9142870
• Rafiei, A, Zahedifar, R, Sitaula, C, Marzbanrad, F, (2022), Automated Detection of Major Depressive Disorder With EEG Signals: A Time Series Classification Using Deep Learning, in IEEE Access, vol. 10, pp. 73804-73817, doi: 10.1109/ACCESS.2022.3190502.
• Saeed, SMU, Anwar, SM, and Majid, M, (2015), Psychological stress measurement using low cost single channel EEG headset, in Proceedings of the 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), pp. 581–585, IEEE, Abu Dhabi, UAE.
• Saeed, SMU, Anwar, SM, Khalid, H, Majid, M, and Bagci, U, (2020), EEG based classification of long-term stress using psychological labeling, Sensors, vol. 7, pp. 886-887.
• Schnack, HG, Nieuwenhuis, M, van Haren, NE, Abramovic, L, Scheewe, TW, Brouwer, RM, Pol, HEH, Kahn, RS, (2014), Can structural MRI aid in clinical classification? A machine learning study in two independent samples of patients with schizophrenia, bipolar disorder and healthy subjects, Neuroimage 84, 299–306 (2014).
• Shafiei, SB, Zaeem, L, Ahmed, SE, Ahmed, AH, and Khurshid, AG, (2020), Identifying mental health status using deep neural network trained by visual metrics, Translational Psychiatry, vol. 10, pp. 1–8.
• Soroush, MZ, Maghooli, K, Setarehdan, SK, Nasrabadi, AM, (2018), Emotion Classification through Nonlinear EEG Analysis Using Machine Learning Methods, Internationa Clinical Neuroscience, 5(4): 135-149.
• Widge, AS, Bilge, MT, Montana, R, Chang, W, Rodriguez, CI, Deckersbach, T, Nemeroff, CB, (2019), Electroencephalographic biomarkers for treatment response prediction in major depressive illness: A meta-analysis, The American Journal of Psychiatry, 176(1), 44–56. https://doi.org/10.1176/appi.ajp.2018.17121358
• Xie, Y, Yang, B, Lu, X, Zheng, M, Fan, C, Bi, X, Li, Y, (2020), Anxiety and depression diagnosis method based on brain networks and convolutional neural networks, Annual international conference of the IEEE engineering in medicine and biology society. IEEE engineering in medicine and biology society, Annual international conference, 2020, 1503–1506. https://doi.org/10.1109/EMBC44109.2020.9176471
• Zeng, H, Wu, Z, Zhang, J, Yang, C, Zhang, H, Dai, G, Kong, W, (2019). EEG Emotion Classification Using an Improved SincNet-Based Deep Learning Model, Brain Sciences, 9(11):326. https://doi.org/10.3390/brainsci9110326
• Zhang, X, Li, J, Hou, K, Hu, B, Shen, J, Pan, J, & Hu, B, (2020), EEG-based depression detection using convolutional neural network with demographic attention mechanism, 2020 42nd Annual international conference of the IEEE engineering in medicine biology society (EMBC), 128–133. https://doi.org/10.1109/ EMBC44109.2020.9175956
• Zhang, H, Silva, FHS, Ohata, EF, Medeiros, AG, & Rebouças Filho, PP, (2020), Bi-dimensional approach based on transfer learning for alcoholism pre-disposition classification via EEG signals, Frontiers in Human Neuroscience, 14, 365. https://doi. org/10.3389/fnhum.2020.00365