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Analysis of the Middle and Long Latency ERP Components in Schizophrenia

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Optimization, Learning Algorithms and Applications (OL2A 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1488))

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Abstract

Schizophrenia is a complex and disabling mental disorder estimated to affect 21 million people worldwide. Electroencephalography (EEG) has proven to be an excellent tool to improve and aid the current diagnosis of mental disorders such as schizophrenia. The illness is comprised of various disabilities associated with sensory processing and perception. In this work, the first 10−200 ms of brain activity after the self-generation via button presses (condition 1) and passive presentation (condition 2) of auditory stimuli was addressed. A time-domain analysis of the event-related potentials (ERPs), specifically the MLAEP, N1, and P2 components, was conducted on 49 schizophrenic patients (SZ) and 32 healthy controls (HC), provided by a public dataset. The amplitudes, latencies, and scalp distribution of the peaks were used to compare groups. Suppression, measured as the difference between both conditions’ neural activity, was also evaluated. With the exception of the N1 peak during condition (1), patients exhibited significantly reduced amplitudes in all waveforms analyzed in both conditions. The SZ group also demonstrated a peak delay in the MLAEP during condition (2) and a modestly earlier P2 peak during condition (1). Furthermore, patients exhibited less and more N1 and P2 suppression, respectively. Finally, the spatial distribution of activity in the scalp during the MLAEP peak in both conditions, N1 peak in condition (1) and N1 suppression differed considerably between groups. These findings and measurements will be used with the finality of developing an intelligent system capable of accurately diagnosing schizophrenia.

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Acknowledgements

This article is a result of the project “GreenHealth - Digital strategies in biological assets to improve well-being and promote green health” (Norte-01-0145-FEDER-000042), supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF).

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Authors and Affiliations

  1. Research Centre in Digitalization and Intelligent Robotics (CeDRI), Instituto Politécnico de Bragança, 5300-253, Bragança, Portugal

    Miguel Rocha e Costa, Felipe Teixeira & João Paulo Teixeira

  2. Universidade de Trás-os-Montes e Alto Douro, 5000-801, Vila Real, Portugal

    Felipe Teixeira

  3. Applied Management Research Unit (UNIAG), Instituto Politécnico de Bragança, Bragança, Portugal

    João Paulo Teixeira

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  1. Miguel Rocha e Costa
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  2. Felipe Teixeira
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  3. João Paulo Teixeira
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Correspondence to João Paulo Teixeira .

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Editors and Affiliations

  1. Instituto Politécnico de Bragança, Bragança, Portugal

    Ana I. Pereira

  2. Instituto Politécnico de Bragança, Bragança, Portugal

    Florbela P. Fernandes

  3. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Coelho

  4. Instituto Politécnico de Bragança, Bragança, Portugal

    João P. Teixeira

  5. Instituto Politécnico de Bragança, Bragança, Portugal

    Maria F. Pacheco

  6. Instituto Politécnico de Bragança, Bragança, Portugal

    Paulo Alves

  7. Instituto Politécnico de Bragança, Bragança, Portugal

    Rui P. Lopes

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Costa, M.R.e., Teixeira, F., Teixeira, J.P. (2021). Analysis of the Middle and Long Latency ERP Components in Schizophrenia. In: Pereira, A.I., et al. Optimization, Learning Algorithms and Applications. OL2A 2021. Communications in Computer and Information Science, vol 1488. Springer, Cham. https://doi.org/10.1007/978-3-030-91885-9_35

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  • DOI: https://doi.org/10.1007/978-3-030-91885-9_35

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