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Epileptic high-frequency oscillations: detection and classification

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Abstract

High-frequency oscillations (HFOs) in intracranial electroencephalograms of patients with epilepsy are regarded as promising biomarkers of epileptogenic zones. Their detection and classification can be achieved by visual assessment or automated approaches, although manual processing of large recordings can be laborious. As a result, an automated analysis scheme is indispensable to enable the clinical use of HFOs. In this paper, we present a two-stage strategy to detect and classify HFOs, which starts with a threshold-based approach to detect plausible HFO events followed by an event classification to discriminate different oscillations. Unlike existing approaches, the detection process in the proposed schemes starts by calculating various multi-channel features that allow interrelations among electrodes to be exploited for detection. On this basis, the detection thresholds are set epoch-by-epoch, relying on a two-component Gaussian mixture model to avoid threshold overestimation. The events deemed to be plausible HFOs are then subjected to classification. By simultaneously examining the raw data and time-frequency maps of these events, they are ultimately sorted into the following categories: HFOs, spikes, and spikes with HFOs, so that the oscillations solely caused by filtering sharp transients can be discriminated. Experimental results using simulated data and intracranial recordings from three epileptic patients demonstrate that our proposed schemes achieve promising sensitivity and precision, especially when the noise level is high.

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Acknowledgements

The authors would like to acknowledge the financial support of the Ministry of Science and Technology of Taiwan, R.O.C. for the projects under Contract Nos. MOST 104-2221-E-007-145 and MOST 105-2221-E-007-090.

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Authors and Affiliations

  1. Department of Engineering and System Science, National Tsing Hua University, Hsinchu, 30013, Taiwan

    Shun-Chi Wu, Chen-Wei Chou & Yung-Chih Su

  2. Department of Neurology, The Neurological Institute, Taipei Veterans General Hospital, Taipei, 11217, Taiwan

    Chien Chen & Shang-Yeong Kwan

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  1. Shun-Chi Wu
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  2. Chen-Wei Chou
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Correspondence to Shun-Chi Wu or Chien Chen.

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Wu, SC., Chou, CW., Chen, C. et al. Epileptic high-frequency oscillations: detection and classification. Multidim Syst Sign Process 31, 965–988 (2020). https://doi.org/10.1007/s11045-019-00693-0

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