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Algorithm for Medical Diagnostic Support Using Machine and Deep Learning for Depressive Disorder Based on Electroencephalogram Readings

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Smart Technologies, Systems and Applications (SmartTech-IC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1705))

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Abstract

Depression is one of the most common mental disorders affecting 121 million people worldwide. Depression is more than a low mood and those who suffer from it can experience a lack of interest in daily activities, lack of concentration, low energy, feelings of worthlessness and in the worst cases, it can lead to suicide. For this reason, correct detection of the disorder is essential to reduce the number of cases of misdiagnosed people. In addition to psychological analysis, EEG signals are also one of the tools that help in the detection of mental disorders, such as depressive disorder. Therefore, the purpose of this study is to develop an algorithm for the detection of depressive disorder based on the classification of EEG signals. For this purpose, machine learning was used with the Welch method and four different classifiers, which are: LDA, LR, KNN and RFC. Also was used neural network that combines (IC-RNN) and (C-DRNN). Despite working with few data from only 26 depressed patients and 29 healthy patients, it could be obtained an accuracy of 57%.

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References

  1. Aalbers, G., McNally, R.J., Heeren, A., De Wit, S., Fried, E.I.: Social media and depression symptoms: a network perspective. J. Exp. Psychol. Gen. 148(8), 1454 (2019)

    Article  Google Scholar 

  2. Aguiar-Salazar, E., Villalba-Meneses, F., Tirado-Espín, A., Amaguaña-Marmol, D., Almeida-Galárraga, D.: Rapid detection of cardiac pathologies by neural networks using ECG signals (1D) and SECG images (3D). Computation 10(7), 112 (2022)

    Article  Google Scholar 

  3. Akbari, H., Sadiq, M.T., Payan, M., Esmaili, S.S., Baghri, H., Bagheri, H.: Depression detection based on geometrical features extracted from SODP shape of EEG signals and binary PSO. Traitement du Signal 38(1) (2021)

    Google Scholar 

  4. Balakrishnama, S., Ganapathiraju, A.: Linear discriminant analysis-a brief tutorial. Inst. Signal Inf. Process. 18(1998), 1–8 (1998)

    Google Scholar 

  5. Barbé, K., Pintelon, R., Schoukens, J.: Welch method revisited: nonparametric power spectrum estimation via circular overlap. IEEE Trans. Signal Process. 58(2), 553–565 (2009)

    Article  MathSciNet  MATH  Google Scholar 

  6. Branding, M.: Google colaboratory colab - guía completa español. Marketing branding (2020)

    Google Scholar 

  7. Budunova, K., Kravchenko, V., Churikov, D.: Application of the family of Kravchenko-Rvachev atomic weight functions (windows) in welch method EEG power spectral density estimation, pp. 500–506 (2019)

    Google Scholar 

  8. Cai, H., et al.: Modma dataset: a multi-modal open dataset for mental-disorder analysis. arXiv preprint arXiv:2002.09283 (2020)

  9. Caicho, J., et al.: Diabetic retinopathy: detection and classification using alexnet, googlenet and resnet50 convolutional neural networks, pp. 259–271 (2022)

    Google Scholar 

  10. Chaudhary, A., Kolhe, S., Kamal, R.: An improved random forest classifier for multi-class classification. Inf. Process. Agric. 3(4), 215–222 (2016)

    Google Scholar 

  11. De Aguiar Neto, F.S., Rosa, J.L.G.: Depression biomarkers using non-invasive EEG: a review. Neurosci. Biobehav. Rev. 105, 83–93 (2019)

    Google Scholar 

  12. Duan, L., et al.: Machine learning approaches for MDD detection and emotion decoding using EEG signals. Front. Hum. Neurosci. 14, 284 (2020)

    Article  Google Scholar 

  13. Ergin, T., Ozdemir, M.A., Akan, A.: Emotion recognition with multi-channel EEG signals using visual stimulus, pp. 1–4 (2019)

    Google Scholar 

  14. Forouzandeh, N., Saeedi, M., Maghooli, K.: Depression diagnosis based on KNN algorithm and EEG signals. Int. J. Smart Electr. Engi. 10(01), 17–22 (2021)

    Google Scholar 

  15. Gramfort, A., et al.: MEG and EEG data analysis with MNE-Python. Front. Neurosci. 7(267), 1–13 (2013). https://doi.org/10.3389/fnins.2013.00267

    Article  Google Scholar 

  16. Gualsaquí, M.G., et al.: Convolutional neural network for imagine movement classification for neurorehabilitation of upper extremities using low-frequency EEG signals for spinal cord injury, pp. 272–287 (2022)

    Google Scholar 

  17. Guevara, G.L.: Classification of egg signals for diagnosing depression. Departamento de Psiquiatria y Salud Mental, Facultad de Medicina Universidad Nacional Autonoma de Mexico (2016)

    Google Scholar 

  18. Herrera-Romero, B., Almeida-Galárraga, D., Salum, G.M., Villalba-Meneses, F., Gudiño-Gomezjurado, M.: Gusignal: an informatics tool to analyze glucuronidase gene expression in arabidopsis thaliana roots. IEEE/ACM Trans. Comput. Biol. Bioinform. 20(2), 1073–1080 (2022)

    Article  Google Scholar 

  19. Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying depression patients and normal subjects using machine learning techniques and nonlinear features from eeg signal. Comput. Methods Programs Biomed. 109(3), 339–345 (2013)

    Article  Google Scholar 

  20. Hu, R.: Diagnostic and statistical manual of mental disorders: DSM-IV. In: Encyclopedia of the Neurological Sciences, vol. 25, no. 2, pp. 4–8 (2003)

    Google Scholar 

  21. Kemp, A., et al.: Disorder specificity despite comorbidity: resting EEG alpha asymmetry in major depressive disorder and post-traumatic stress disorder. Biol. Psychol. 85(2), 350–354 (2010)

    Article  Google Scholar 

  22. Khosla, A., Khandnor, P., Chand, T.: Automated diagnosis of depression from EEG signals using traditional and deep learning approaches: a comparative analysis. Biocybern. Biomed. Eng. 42(1), 108–142 (2021)

    Article  Google Scholar 

  23. Köhler-Forsberg, O., et al.: Association between c-reactive protein (CRP) with depression symptom severity and specific depressive symptoms in major depression. Brain Behav. Immun. 62, 344–350 (2017)

    Article  Google Scholar 

  24. Lakshmi, M.R., Prasad, T., Prakash, D.V.C.: Survey on EEG signal processing methods. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 4(1) (2014)

    Google Scholar 

  25. Lu, L.H., et al.: Relationships between brain activation and brain structure in normally developing children. Cereb. Cortex 19(11), 2595–2604 (2009)

    Article  Google Scholar 

  26. Maalouf, M.: Logistic regression in data analysis: an overview. Int. J. Data Anal. Tech. Strat. 3(3), 281–299 (2011)

    Article  Google Scholar 

  27. Mahato, S., Paul, S.: Classification of depression patients and normal subjects based on electroencephalogram (EEG) signal using alpha power and theta asymmetry. J. Med. Syst. 44(1), 1–8 (2020)

    Article  Google Scholar 

  28. Mallikarjun, H., Suresh, H.: Depression level prediction using EEG signal processing, pp. 928–933 (2014)

    Google Scholar 

  29. Mantri, S., Patil, D., Agrawal, P., Wadhai, V.: Non invasive EEG signal processing framework for real time depression analysis, pp. 518–521 (2015)

    Google Scholar 

  30. Matamoros-Alcivar, E., et al.: Implementation of MPC and PID control algorithms to the artificial pancreas for diabetes mellitus type 1, pp. 1–6 (2021)

    Google Scholar 

  31. Mingote Adán, J.C., Gálvez Herrer, M., Pino Cuadrado, P.d., Gutiérrez García, M.: El paciente que padece un trastorno depresivo en el trabajo. Medicina y seguridad del trabajo 55(214), 41–63 (2009)

    Google Scholar 

  32. Mumtaz, W., Xia, L., Ali, S.S.A., Yasin, M.A.M., Hussain, M., Malik, A.S.: Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomed. Signal Process. Control 31, 108–115 (2017)

    Article  Google Scholar 

  33. Niles, D.N., et al.: COVID-19 pulmonary lesion classification using CNN software in chest X-ray with quadrant scoring severity parameters, pp. 370–382 (2022)

    Google Scholar 

  34. Peterson, L.E.: K-nearest neighbor. Scholarpedia 4(2), 1883 (2009)

    Article  Google Scholar 

  35. Piscoya Tenorio, J.L., Heredia Rioja, W.V.: Niveles de ansiedad y depresión en estudiantes de medicina de universidades de lambayeque-2018 (2018)

    Google Scholar 

  36. Rice, F., et al.: Adolescent and adult differences in major depression symptom profiles. J. Affect. Disord. 243, 175–181 (2019)

    Article  Google Scholar 

  37. Rodríguez Martínez, E.I.: Indicadores de maduración cerebral y su relación con la memoria de trabajo (2014)

    Google Scholar 

  38. Roy, S., Kiral-Kornek, I., Harrer, S.: Chrononet: a deep recurrent neural network for abnormal EEG identification, pp. 47–56 (2019)

    Google Scholar 

  39. Saeedi, M., Saeedi, A., Maghsoudi, A.: Major depressive disorder assessment via enhanced k-nearest neighbor method and EEG signals. Phys. Eng. Sci. Med. 43(3), 1007–1018 (2020)

    Article  Google Scholar 

  40. Shen, J., Zhao, S., Yao, Y., Wang, Y., Feng, L.: A novel depression detection method based on pervasive EEG and EEG splitting criterion, pp. 1879–1886 (2017)

    Google Scholar 

  41. Shi, Q., Liu, A., Chen, R., Shen, J., Zhao, Q., Hu, B.: Depression detection using resting state three-channel EEG signal. arXiv preprint arXiv:2002.09175 (2020)

  42. Supriya, S., Siuly, S., Wang, H., Zhang, Y.: Automated epilepsy detection techniques from electroencephalogram signals: a review study. Health Inf. Sci. Syst. 8(1), 1–15 (2020)

    Article  Google Scholar 

  43. Suquilanda-Pesántez, J., et al.: Prediction of Parkinson’s disease severity based on gait signals using a neural network and the fast fourier transform, pp. 3–18 (2020)

    Google Scholar 

  44. Suquilanda-Pesántez, J.D., Salazar, E.D.A., Almeida-Galárraga, D., Salum, G., Villalba-Meneses, F., Gomezjurado, M.E.G.: NIFtHool: an informatics program for identification of NifH proteins using deep neural networks. F1000Research 11 (2022)

    Google Scholar 

  45. Tene-Hurtado, D., et al.: Brain tumor segmentation based on 2D U-net using MRI multi-modalities brain images, pp. 345–359 (2022)

    Google Scholar 

  46. WHO: Depression. World Health Organization (2021)

    Google Scholar 

  47. Yanchatuña, O., et al.: Skin lesion detection and classification using convolutional neural network for deep feature extraction and support vector machine (2021)

    Google Scholar 

  48. Yasin, S., Hussain, S.A., Aslan, S., Raza, I., Muzammel, M., Othmani, A.: EEG based major depressive disorder and bipolar disorder detection using neural networks: a review. Comput. Methods Programs Biomed. 202, 106007 (2021)

    Article  Google Scholar 

  49. Zandvakili, A., Philip, N.S., Jones, S.R., Tyrka, A.R., Greenberg, B.D., Carpenter, L.L.: Use of machine learning in predicting clinical response to transcranial magnetic stimulation in comorbid posttraumatic stress disorder and major depression: a resting state electroencephalography study. J. Affect. Disord. 252, 47–54 (2019)

    Article  Google Scholar 

  50. Zhao, L., He, Y.: Power spectrum estimation of the welch method based on imagery EEG, vol. 278, pp. 1260–1264 (2013)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Escuela de Ciencias Biológicas e Ingeniería, Universidad Yachay Tech, Hacienda San José s/n, San Miguel de Urcuquí, 100119, Ecuador

    Lady L. González, Giovana F. Vaca, Marilyn E. Figueroa, Adriana E. Estrella, Evelyn G. González, Diego A. Almeida-Galárraga, Jonathan Cruz-Varela & Fernando Villalba-Meneses

  2. Escuela de Ciencias Matemáticas y Computacionales, Universidad Yachay Tech, Hacienda San José s/n, San Miguel de Urcuquí, 100119, Ecuador

    Carolina Cadena-Morejón & Andres Tirado-Espín

  3. Universidad Complutense de Madrid, 28040, Madrid, CP, Spain

    Andres Tirado-Espín

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  1. Lady L. González
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  2. Giovana F. Vaca
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  3. Marilyn E. Figueroa
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  10. Fernando Villalba-Meneses
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Correspondence to Fernando Villalba-Meneses .

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Editors and Affiliations

  1. Universidad Politécnica Salesiana, Quito, Ecuador

    Fabián R. Narváez

  2. Universidad Politécnica Salesiana, Cuenca, Ecuador

    Fernando Urgilés

  3. Universidade Federal do Espírito Santo, Vitória, Brazil

    Teodiano Freire Bastos-Filho

  4. Universidad Politécnica Salesiana, Cuenca, Ecuador

    Juan Pablo Salgado-Guerrero

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González, L.L. et al. (2023). Algorithm for Medical Diagnostic Support Using Machine and Deep Learning for Depressive Disorder Based on Electroencephalogram Readings. In: Narváez, F.R., Urgilés, F., Bastos-Filho, T.F., Salgado-Guerrero, J.P. (eds) Smart Technologies, Systems and Applications. SmartTech-IC 2022. Communications in Computer and Information Science, vol 1705. Springer, Cham. https://doi.org/10.1007/978-3-031-32213-6_23

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  • DOI: https://doi.org/10.1007/978-3-031-32213-6_23

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