Out-of-hospital multimodal seizure detection: a pilot study

Figures

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  Figure 1. Flow chart of the methods used to obtain and preprocess the data and for training and testing the seizure detection algorithm. The two approaches used for training and testing the algorithm is shown in the sections labelled 1 and 2. The in-patient data used in section 2 was obtained during epilepsy monitoring unit (EMU) recordings from the same individual which were recorded in a previous study.9 The recordings from the EMU study were preprocessed the same way as the out-patient recordings. ACM, combination of accelerometry; EEG, electroencephalography.

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  Figure 2. Time-domain comparison of an in-patient video-EEG validated seizure with an out-of-hospital electrographic seizure. Top panel shows an in-patient seizure as recorded with bteEEG. Middle panel shows the same seizure as recorded with EMU EEG. The start and stop times refer to the onset and end of the seizure, as reported by the clinical neurophysiologist based on both the ictal electrographic and behavioural changes seen in the in-patient EMU video-EEG recordings. Bottom panel shows an out-of-hospital electrographic seizure as recorded with the bteEEG. EEG, electroencephalography; EMU, epilepsy monitoring unit.

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  Figure 3. Receiver operating characteristic curves of the automatic seizure detection of the electrographic seizures identified in one patient. The classifier was trained and tested using only out-of-hospital recordings. ACM, combination of accelerometry; EEG, electroencephalography; FAR, false alarm rate (per 24 hours); MSD, minimum seizure duration.

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  Figure 4. Receiver operating characteristic curves of the automatic seizure detection of the electrographic seizures identified in one patient. In the left column, the classifier was trained on in-patient data from.9 In the middle column, the classifier was trained on in-patient data with the addition of one out-of-hospital fold. In the right column 5 out-of-hospital folds were added. *Number of out-of-hospital folds in the training set out of the total number of out-of-hospital folds. ACM, combination of accelerometry; EEG, electroencephalography; FAR, false alarm rate (per 24 hours); MSD, minimum seizure duration.

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  Figure 5. Visualisation of one channel from all the recordings. An RMS threshold range of physiological EEG was established from manually reviewed EEG epochs. Then RMS was calculated for all the EEG recordings in 2 s windows and each window was categorised and coloured according to the threshold. EEG, electroencephalography; RMS, electroencephalography.

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  Figure 6. A 20 s examples of EEG categorised according to the EEG thresholds. Note the different scale of the y-axis in example 3. Example 1: EEG signal with an RMS within the threshold range. visually subtle R-peak artefacts. Example 2: EEG signal with an RMS below the lower threshold limit, perhaps indicating loss of electrode skin contact. Example 3: EEG signal with an RMS above the upper threshold limit. perhaps movement artefacts at 1, 7 and 12 s. EEG, electroencephalography; RMS, electroencephalography.