Prediction of epileptic seizures with convolutional neural networks and functional near-infrared spectroscopy signals

https://doi.org/10.1016/j.compbiomed.2019.103355Get rights and content

Highlights

  • Epileptic seizure prediction based on fNIRS and deep learning.

  • Accuracy of 96.9%–100%, sensitivity of 95.24%–100%, specificity of 98.57%–100%.

  • A CNN is suitable for this problem given the nature of fNIRS.

  • The results are superior to those based on EEG.

Abstract

There have been different efforts to predict epileptic seizures and most of them are based on the analysis of electroencephalography (EEG) signals; however, recent publications have suggested that functional Near-Infrared Spectroscopy (fNIRS), a relatively new technique, could be used to predict seizures. The objectives of this research are to show that the application of fNIRS to epileptic seizure detection yields results that are superior to those based on EEG and to demonstrate that the application of deep learning to this problem is suitable given the nature of fNIRS recordings. A Convolutional Neural Network (CNN) is applied to the prediction of epileptic seizures from fNIRS signals, an optical modality for recording brain waves. The implementation of the proposed method is presented in this work. Application of CNN to fNIRS recordings showed an accuracy ranging between 96.9% and 100%, sensitivity between 95.24% and 100%, specificity between 98.57% and 100%, a positive predictive value between 98.52% and 100%, and a negative predictive value between 95.39% and 100%. The most important aspect of this research is the combination of fNIRS signals with the particular CNN algorithm. The fNIRS modality has not been used in epileptic seizure prediction. A CNN is suitable for this application because fNIRS recordings are high dimensional data and they can be modeled as three-dimensional tensors for classification.

Introduction

Prediction of epileptic seizures refers to the analysis of brain signals, extracted from an epileptic patient, to detect the future occurrence of a seizure. There are four states within a brain signal, which is extracted from an epileptic patient. The ictal state is the signal fragment where an epileptic seizure occurs; the pre-ictal state is the segment that happens before the onset of a seizure; post-ictal is the state that follows a seizure, and inter-ictal is the fragment between the end of post-ictal and the start of pre-ictal. In this work, we merge post-ictal and inter-ictal states into one state, inter-ictal. Furthermore, we discard the ictal state since detecting an epileptic seizure, when it is happening, is of no interest for seizure prediction. Thus, we only consider two states, inter-ictal and pre-ictal. Epileptic seizure prediction is achieved when the system detects the pre-ictal state [1].Real-time epileptic seizure prediction mainly consists of two stages: (1) extraction of a tensor from the signal of interest, at any time position; and (2) classification of the tensor as inter-ictal or pre-ictal. Epileptic seizures are predicted when pre-ictal segments are correctly detected. Detection of the ictal state is of no interest since the goal is detecting a seizure before it happens by identifying the occurrence of the pre-ictal state. Thus, ictal fragments are of no use. There have been different efforts to predict seizures and most of them are based on the analysis of electroencephalography (EEG) signals [2]; however, recent publications have suggested that fNIRS signals, a relatively new modality, could be used to predict seizures [3,4]. fNIRS (functional near-infrared spectroscopy) is an optical technique, which conveys monitoring information about brain activity [5]; specifically, two physiological parameters or measurements, the relative level of oxygenated hemoglobin (HbO) and the relative level of deoxygenated hemoglobin (HbR). fNIRS signals are generated by injecting infrared lights of different wavelengths (650 nm–1000 nm) into the scalp, followed by registration of reflected light through optodes. Blood hemoglobin has different levels of light absorption and reflection depending on the level of blood oxygen due to brain activity [5,6]. fNIRS recordings arise from multiple channels. At each channel, two values are registered, HbO and HbR.fNIRS is a non-invasive neuroimaging technique that is effective for the monitoring of cortical hemodynamic changes. Studies have shown that fNIRS can be used to assess cortical hemodynamic changes associated with seizures, helping in the detection of seizures and the assessment of their impact on brain oxygenation [4]. Optical sensors (such as ratiometric nano quantum dot fluorescence resonance energy transfer sensors) were used to quantitatively measure oxygen dynamics from single-cell microdomains during oxygen-deprivation episodes (hypoxic episodes) and during induced seizure-like events in rats. A substantial decrease in the oxygenation of pyramidal cells was found through the concomitant measurement of blood oxygenation and electrical neural activity. This decrease in oxygen levels took place up to a few seconds before ictal activity could be revealed in electrophysiological recordings. Hence, an internally generated deficiency in oxygen is a crucial and decisive factor for hemodynamics related to epileptiform patterns [7]. The use of fNIRS in seizure prediction is based on scientific evidence that BOLD (Blood oxygen-level dependent) activity may result from neuronal changes occurring several seconds before a surface EEG event [8], and that vasoconstriction of blood vessels occurs a few seconds before the electrical onset [9]. A suitable format for fNIRS data consists of a two- or three-dimensional grid, which is the result of recording signals from multiple channels (rows), at multiple time positions (columns), by using two different measured values (feature maps or planes), HbO and HbR. Motivations for applying Convolutional Neural Networks (CNNs) to the problem of predicting epileptic seizures are (1) a large number of fNIRS channels, (2) the three-dimensional grid topology of fNIRS data, and (3) the straight application of raw fNIRS data to a CNN, where appropriate features are progressively extracted at convolutional layers for further classification. The use of fNIRS recordings for automated epileptic seizure detection requires more explorative studies. There are not public datasets related to fNIRS recordings on epileptic patients. This work is one of the firsts where fNIRS and deep learning are used to tackle the problem of seizure prediction. Detection of epileptic seizures in advance would allow epileptic patients or their caretakers to take precautions before the occurrence of an epileptic seizure, therefore mitigating risks and potential harm associated with the event, for instance in the intensive care unit, where electroencephalography (EEG) is typically not monitored. This document is organized as follows: Section 2 presents previous work. Section 3 provides the Convolutional Neural Network framework for the classification of fNIRS tensors, which includes a description of the architecture parameters and the implementation of the learning algorithm. Section 4 gives details of the methodology followed in this research. The results of this work are presented in Section 5 and discussed in Section 6. Finally, conclusions are given in Section 7.

Section snippets

Related work

Predicting epileptic seizures is the core of monitoring and warning systems so that precautions are taken before a seizure occurs [2]. One of the main problems for patients with epilepsy is the unpredictability of seizures [10]. Seizure detection and prediction have been mostly based on EEG recordings [1,2,[11], [12], [13], [14], [15]] given its capability to monitor electrical activity from the brain at a high temporal resolution [16]. EEG is the fastest and cheapest non-invasive method for

Implementation of a convolutional neural network

This section provides the framework for the complete implementation of a convolutional neural network (CNN) along with the corresponding learning algorithm. This section introduces the notation that is used to describe the selection of the most appropriate CNN model for seizure prediction. This notation is later used in the Results section, which shows the outcome of a set of experiments aimed at selecting the CNN model parameter values.

Dataset

There are not public datasets with fNIRS recordings from patients with epileptic seizures. The dataset for this work was the result of recording fNIRS signals from patients with focal refractory epilepsy. These recordings were performed and analyzed by epileptologists from Hôpital Notre-Dame du Centre Hospitalier de l'Université de Montréal. Forty-nine patients were subjects for the generation of the dataset; however, only five patients presented epileptic seizures while recording their brain

Results

Fig. 4 shows the ictal, inter-ictal and pre-ictal states within the recordings of (1) a single channel of EEG (specified in micro-volts [μV]); (2) three states within HbO and HbR recordings (specified in changes in hemoglobin concentration [C]). Finally, parts B), C), D), E) and F) show the specific placement of hundreds of optodes on five different patients, where blue lines represent sources and red lines represent detectors at different optode positions.

For each patient, experiments were

Discussion

According to Table 2, epileptic seizure prediction, based on the application of CNN to fNIRS recordings, is effective since performance metrics reach values above 95%. The performance of seizure prediction, when HbO and HbR are jointly used, is higher than that obtained by using only single reading, HbO or HbR. The HbO-HbR (fNIRS) integration allows higher prediction rates since this combination allows convolutional layers to extract more relevant features, which provides spatial and temporal

Conclusions

The problem of predicting epileptic seizures, using deep learning and fNIRS recordings, was studied. It is shown that the application of CNN to fNIRS data is suitable given the nature of fNIRS, which can be modeled as two- or three-dimensional tensors of high dimensionality. Evidence of the effectiveness of the proposed method is obtained through assessment of different performance metrics such as accuracy ranging between 96.9% and 100%, sensitivity between 95.24% and 100%, and specificity

Acknowledgement

This work was supported in part by a Mexico-Quebec Mobility program (project No. 265578). Data recordings were obtained from Hôpital Notre-Dame du Centre Hospitalier de l'Université de Montréal. Edgar Guevara acknowledges support from “Cátedras CONACYT” project 528.

References (42)

  • C.S. Hawco et al.

    BOLD changes occur prior to epileptic spikes seen on scalp EEG

    Neuroimage

    (2007)
  • D.N. Lenkov et al.

    Advantages and limitations of brain imaging methods in the research of absence epilepsy in humans and animal models

    J. Neurosci. Methods

    (2013)
  • N. Moghim et al.

    Predicting epileptic seizures in advance

    PLoS One

    (2014)
  • S. Ramgopal et al.

    Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy

    Epilepsy Behav.

    (2014)
  • D.K. Binder et al.

    Toward new paradigms of seizure detection

    Epilepsy Behav.

    (2013)
  • E. Guevara et al.

    Epileptic seizure detection in fNIRS signals using a supervised classifier

    Proc. fNIRS

    (2014)
  • H. Obrig

    NIRS in clinical neurology a promising tool?

    Neuroimage

    (2014)
  • P. Pouliot et al.

    Nonlinear hemodynamic responses in human epilepsy: a multimodal analysis with fNIRS-EEG and fMRI-EEG

    J. Neurosci. Methods

    (2012)
  • J. Ingram et al.

    Oxygen and seizure dynamics: I. experiments

    J. Neurophysiol.

    (2014)
  • M. Zhao et al.

    Preictal and ictal neurovascular and metabolic coupling surrounding a seizure focus

    J. Neurosci.

    (2011)
  • P. Camfield et al.

    Idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTC): a population-based cohort with more than 20 year follow up for medical and social outcome

    Epilepsy Behav.

    (2010)
  • P. Gmez-Gil et al.

    Identification of epilepsy seizures using multi-resolution analysis and artificial neural networks

    Recent Adv. Hybrid Approaches Des. Intell. Syst.

    (2014)
  • A. T. Tzallas, M. G. Tsipouras, D. G. Tsalikakis, E. C. Karvounis, L. Astrakas, S. Konitsiotis, M. Tzaphlidou,...
  • L. Orosco et al.

    Review: a survey of performance and techniques for automatic epilepsy detection

    J. Med. Biol. Eng.

    (2013)
  • F. Mormann et al.

    Seizure prediction: the long and winding road

    Brain J. Neurol.

    (2007)
  • U. Rajendra-Acharya et al.

    Automated EEG analysis of epilepsy: a review

    Knowl. Based Syst.

    (2013)
  • F. Wallois et al.

    EEG-NIRS in epilepsy in children and neonates

    Neurophysiol. Clin. Clin. Neurophysiol.

    (2010)
  • E. D. Kondylis, T. A. Wozny, W. J. Lipski, A. Popescu, V. J. DeStefino, B. Esmaeili, V. K. Raghu, A. Bagic, R. M....
  • S. Smith

    EEG in the diagnosis, classification, and management of patients with epilepsy

    J. Neurol. Neurosurg. Psychiatry

    (2005)
  • A. Gallagher et al.

    Non-invasive pre-surgical investigation of a 10 year-old epileptic boy using simultaneous EEG-NIRS

    Seizure J. Br. Epilepsy Assoc.

    (2008)
  • P. Pinti et al.

    Using fiberless, wearable fNIRS to monitor brain activity in real-world cognitive tasks

    J. Vis. Exp.

    (2015)
  • Cited by (44)

    • Assessment of flourishing levels of individuals by using resting-state fNIRS with different functional connectivity measures

      2021, Biomedical Signal Processing and Control
      Citation Excerpt :

      In contrast to the conventional approach using ΔHbO for FC analysis, we also used ΔHb due to previous evidence suggesting both hemoglobin concentrations should be analyzed for sparse networks [58]. Previous fNIRS-based ML studies focusing on classification of various psychiatric disorders, also use ΔHb in addition to ΔHbO [29,30,59–64]. In some cases, ΔHb based features may also show higher accuracy results compared to ΔHbO based features [59].

    • A systematic review on hybrid EEG/fNIRS in brain-computer interface

      2021, Biomedical Signal Processing and Control
      Citation Excerpt :

      Multimodal EEG-fNIRS data can provide superior performance metrics, and different classification procedures have been applied to combined EEG-fNIRS BCI [94,110–112,114–121,123–126,133–143]. Specifically, by employing support vector machine (SVM) [110–115] or linear discriminant analysis (LDA) [94,116–120] (which are as the classic machine learning methods), the BCI signals have been classified by EEG power spectral densities and fNIRS signal amplitudes. Furthermore, linear discriminant analysis ensemble classifiers have been employed in [127], to increase the bitrate as well as the classification accuracy for fNIRS-BCI datasets.

    View all citing articles on Scopus
    View full text