Abstract
Anxiety is common in youth with autism spectrum disorder (ASD), causing unique lifelong challenges that severely limit everyday opportunities and reduce quality of life. Given the detrimental consequences and long-term effects of pervasive anxiety for childhood development and the covert nature of mental states, brain-computer interfaces (BCIs) represent a promising method to identify maladaptive states and allow for individualized and real-time mitigatory action to alleviate anxiety. Here we investigated the effects of slow paced breathing entrainment during stress induction on the perceived levels of anxiety in neurotypical adolescents and adolescents with autism, and propose a multi-class long short-term recurrent neural net (LSTM RNN) deep learning classifier capable of identifying anxious states from ongoing electroencephalography (EEG) signals. The deep learning classifier used was able to discriminate between anxious and non-anxious classes with an accuracy of 90.82% and yielded an average accuracy of 93.27% across all classes. Our study is the first to successfully apply an LSTM RNN classifier to identify anxious states from EEG. This LSTM RNN classifier holds promise for the development of neuroadaptive systems and individualized intervention methods capable of detecting and alleviating anxious states in both neurotypical adolescents and adolescents with autism.
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References
Bennett, K., et al.: Preventing child and adolescent anxiety disorders: overview of systematic reviews. Depress. Anxiety 32, 909–918 (2015)
Zaboski, B.A., Storch, E.A.: Comorbid autism spectrum disorder and anxiety disorders: a brief review. Future Neurol. 13, 31–37 (2018)
Kendall, P.C., et al.: Clinical characteristics of anxiety disordered youth. J. Anxiety Disord. 24, 360–365 (2010)
Maddox, B.B., White, S.W.: Comorbid social anxiety disorder in adults with autism spectrum disorder. J. Autism Dev. Disord. 45(12), 3949–3960 (2015). https://doi.org/10.1007/s10803-015-2531-5
Hofvander, B., et al.: Psychiatric and psychosocial problems in adults with normal-intelligence autism spectrum disorders. BMC Psychiatry 9, 35 (2009)
Hepburn, S.L., Stern, J.A., Blakeley-Smith, A., Kimel, L.K., Reaven, J.A.: Complex psychiatric comorbidity of treatment-seeking youth with autism spectrum disorder and anxiety symptoms. J. Mental Health Res. Intellect. Disabil. 7, 359–378 (2014)
van Steensel, F.J.A., Bögels, S.M., Perrin, S.: Anxiety disorders in children and adolescents with autistic spectrum disorders: a meta-analysis. Clin. Child Family Psychol. Rev. 14, 302–317 (2011)
Kerns, C.M., Kendall, P.C., Zickgraf, H., Franklin, M.E., Miller, J., Herrington, J.: Not to be overshadowed or overlooked: functional impairments associated with comorbid anxiety disorders in youth with ASD. Behav. Ther. 46, 29–39 (2015)
Antshel, K.M., et al.: Comorbid ADHD and anxiety affect social skills group intervention treatment efficacy in children with autism spectrum disorders. J. Dev. Behav. Pediatr. 32, 439–446 (2011)
Ikeda, E., Hinckson, E., Krägeloh, C.: Assessment of quality of life in children and youth with autism spectrum disorder: a critical review. Qual. Life Res. 23(4), 1069–1085 (2013). https://doi.org/10.1007/s11136-013-0591-6
Mazzone, L., Ducci, F., Scoto, M.C., Passaniti, E., D’Arrigo, V.G., Vitiello, B.: The role of anxiety symptoms in school performance in a community sample of children and adolescents. BMC Public Health 7 (2007)
Preece, D., Howley, M.: An approach to supporting young people with autism spectrum disorder and high anxiety to re-engage with formal education - the impact on young people and their families. Int. J. Adolesc. Youth 23, 468–481 (2018)
Wallace, S.: One in a hundred: putting families at the heart of autism research. https://www.basw.co.uk/resources/one-hundred-putting-families-heart-autism-research
Pavlenko, V.B., Chernyi, S.V., Goubkina, D.G.: EEG correlates of anxiety and emotional stability in adult healthy subjects. Neurophysiology 41, 337–345 (2009)
Lewis, R.S., Weekes, N.Y., Wang, T.H.: The effect of a naturalistic stressor on frontal EEG asymmetry, stress, and health. Biol. Psychol. 75, 239–247 (2007)
Blackhart, G.C., Minnix, J.A., Kline, J.P.: Can EEG asymmetry patterns predict future development of anxiety and depression? A preliminary study. Biol. Psychol. 72, 46–50 (2006)
Oathes, D.J., et al.: Worry, generalized anxiety disorder, and emotion: evidence from the EEG gamma band. Biol. Psychol. 79, 165–170 (2008)
Newson, J.J., Thiagarajan, T.C.: EEG frequency bands in psychiatric disorders: a review of resting state studies. Front. Hum. Neurosci. 12, 521 (2018)
Thibodeau, R., Jorgensen, R.S., Kim, S.: Depression, anxiety, and resting frontal EEG asymmetry: a meta-analytic review. J. Abnorm. Psychol. 115, 715–729 (2006)
Lotte, F., et al.: A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update. J. Neural Eng. 15, 031005 (2018)
Gaikwad, P., Paithane, A.N.: Novel approach for stress recognition using EEG signal by SVM classifier. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), pp. 967–971 (2017)
Al-shargie, F., Tang, T.B., Badruddin, N., Kiguchi, M.: Towards multilevel mental stress assessment using SVM with ECOC: an EEG approach. Med. Biol. Eng. Comput. 56(1), 125–136 (2017). https://doi.org/10.1007/s11517-017-1733-8
Saeed, S.M.U., Anwar, S.M., Khalid, H., Majid, M., Bagci, A.U.: EEG based classification of long-term stress using psychological labeling. Sensors 20 (2020). https://doi.org/10.3390/s20071886
Faust, O., Hagiwara, Y., Hong, T.J., Lih, O.S., Acharya, U.R.: Deep learning for healthcare applications based on physiological signals: a review. Comput. Methods Programs Biomed. 161, 1–13 (2018)
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16, 051001 (2019)
Schmidhuber, J.: Deep learning in neural networks: an overview. Neural Netw. 61, 85–117 (2015)
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16, 031001 (2019)
Salama, E.S., El-Khoribi, R.A., Shoman, M.E., Wahby, M.A.: EEG-based emotion recognition using 3D Convolutional Neural Networks. IJACSA 9 (2018). https://doi.org/10.14569/IJACSA.2018.090843
Hwang, S., Hong, K., Son, G., Byun, H.: Learning CNN features from DE features for EEG-based emotion recognition. Pattern Anal. Appl. 23(3), 1323–1335 (2019). https://doi.org/10.1007/s10044-019-00860-w
Wang, Y., McCane, B., McNaughton, N., Huang, Z., Shadli, H., Neo, P.: AnxietyDecoder: an EEG-based anxiety predictor using a 3-D convolutional neural network. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1–8 (2019)
Zeng, H., Yang, C., Dai, G., Qin, F., Zhang, J., Kong, W.: EEG classification of driver mental states by deep learning. Cogn. Neurodyn. 12(6), 597–606 (2018). https://doi.org/10.1007/s11571-018-9496-y
Schirrmeister, R.T., et al.: Deep learning with convolutional neural networks for EEG decoding and visualization. Hum. Brain Mapp. 38, 5391–5420 (2017)
Lawhern, V.J., Solon, A.J., Waytowich, N.R., Gordon, S.M., Hung, C.P., Lance, B.J.: EEGNet: a compact convolutional neural network for EEG-based brain-computer interfaces. J. Neural Eng. 15, 056013 (2018)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997)
Xing, X., Li, Z., Xu, T., Shu, L., Hu, B., Xu, X.: SAE+LSTM: a new framework for emotion recognition from multi-channel EEG. Front. Neurorobot. 13, 37 (2019)
Alhagry, S., Fahmy, A.A., El-Khoribi, R.A.: Emotion recognition based on EEG using LSTM recurrent neural network. IJACSA 8 (2017). https://doi.org/10.14569/IJACSA.2017.081046
Borthakur, D., Grace, V., Batchelor, P., Dubey, H., Mankodiya, K.: Fuzzy C-means clustering and sonification of HRV features. In: 2019 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 53–57 (2019)
Spielberger, C.D.: Manual for the State-Trait Inventory for Children. Consulting Psychologists Press, Palo Alto (1973)
Simon, D.M., Corbett, B.A.: Examining associations between anxiety and cortisol in high functioning male children with autism. J. Neurodev. Disord. 5, 32 (2013)
Dedovic, K., Renwick, R., Mahani, N.K., Engert, V., Lupien, S.J., Pruessner, J.C.: The Montreal Imaging Stress Task: using functional imaging to investigate the effects of perceiving and processing psychosocial stress in the human brain. J. Psychiatry Neurosci. 30, 319–325 (2005)
Shilton, A.L., Laycock, R., Crewther, S.G.: The Maastricht Acute Stress Test (MAST): physiological and subjective responses in anticipation, and post-stress. Front. Psychol. 8, 567 (2017)
Szulczewski, M.T.: Training of paced breathing at 0.1 Hz improves CO2 homeostasis and relaxation during a paced breathing task. PLoS One 14, e0218550 (2019)
Gramfort, A., et al.: MNE software for processing MEG and EEG data. Neuroimage 86, 446–460 (2014)
Vanhatalo, S., Voipio, J., Kaila, K.: Full-band EEG (FbEEG): an emerging standard in electroencephalography. Clin. Neurophysiol. 116, 1–8 (2005)
Wang, P., Jiang, A., Liu, X., Shang, J., Zhang, L.: LSTM-based EEG classification in motor imagery tasks. IEEE Trans. Neural Syst. Rehabil. Eng. 26, 2086–2095 (2018)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2014). http://arxiv.org/abs/1412.6980
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Penchina, B., Sundaresan, A., Cheong, S., Martel, A. (2020). Deep LSTM Recurrent Neural Network for Anxiety Classification from EEG in Adolescents with Autism. In: Mahmud, M., Vassanelli, S., Kaiser, M.S., Zhong, N. (eds) Brain Informatics. BI 2020. Lecture Notes in Computer Science(), vol 12241. Springer, Cham. https://doi.org/10.1007/978-3-030-59277-6_21
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DOI: https://doi.org/10.1007/978-3-030-59277-6_21
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