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Patient-Specific Epilepsy Seizure Detection Using Random Forest Classification over One-Dimension Transformed EEG Data

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Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

Abstract

This work presents a computational method for improving seizure detection for epilepsy diagnosis. Epilepsy is the second most common neurological disease. It impacts between 40 and 50 million of patients in the world. However, epilepsy diagnosis using electroencephalographic signals implies a long and expensive process involving medical specialists. The proposed system is a patient-specific system which performs an automatic detection of seizures from brainwaves applying a random forest classifier. Features are extracted using one-dimension reduced information from a spectro-temporal transformation of biosignals that pass through an envelope detector. The performance of the present method reached \(97.12\%\) of specificity, \(99.29\%\) of sensitivity, and a \(0.77h^{-1}\) false positive rate. Therefore, the method hereby proposed has great potential for diagnosis support in clinical environments.

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Acknowledgement

This work is supported by grants from Fundação de Amparo à Pesquisa de Minas Gerais (FAPEMIG) and the PAEC agreement between the Organization of American States (OAS) and Universidade Federal de Viçosa (UFV).

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Authors and Affiliations

  1. Departamento de Informática, Universidade Federal de Viçosa, Viçosa, Brazil

    Marco A. Pinto-Orellana & Fabio R. Cerqueira

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  1. Marco A. Pinto-Orellana
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  2. Fabio R. Cerqueira
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Correspondence to Marco A. Pinto-Orellana .

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Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Pinto-Orellana, M.A., Cerqueira, F.R. (2017). Patient-Specific Epilepsy Seizure Detection Using Random Forest Classification over One-Dimension Transformed EEG Data. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_51

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_51

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