Multimodal Epileptic Seizure Detection: Towards a Wearable Solution
Epilepsy is one of the most common neurological disorders, which affects almost 1% of the population worldwide. Anti-epileptic drugs provide adequate treatment for about 70% of epilepsy patients. The remaining 30% of the patients continue to have seizures, which drastically affects their quality of life. In order to obtain efficacy measures of therapeutic interventions for these patients, an objective way to count and document seizures is needed. However, in an outpatient setting, one of the major problems is that seizure diaries kept by patients are unreliable.
Automated seizure detection systems could help to objectively quantify seizures. Those detection systems are typically based on full scalp Electroencephalography (EEG). In an outpatient setting, full scalp EEG is of limited use because patients
will not tolerate wearing a full EEG cap for long time periods during daily life. There is a need for ambulatory seizure detection systems using wearable sensors. In this thesis, the focus lies on focal seizures, since for this group no existing non-invasive effective solutions for seizure detection in a daily life environment are on the market. The aim of this thesis is the development of an offline seizure detection algorithm to construct automatically a seizure diary. There were three major contributions: seizure detection using Electrocardiography (ECG)/ Photoplethysmography (PPG), seizure detection using behind-the-ear EEG and multimodal seizure detection using ECG and behind-the-ear EEG.
Focal seizures, especially temporal focal seizures, most common in the population of focal epilepsy, are associated with changes in the autonomic nervous system, in particular the cardiovascular system. It has been shown that temporal lobe seizures are often accompanied with a strong heart rate increase. Those heart rate increases can be measured using Electrocardiography (ECG) . Most of the published articles use ECG recorded with wired electrodes using hospital equipment. However, a wearable solution is preferred. Furthermore, ECG electrodes can be uncomfortable and can cause skin irritation after a few days. Another way to measure heart rate is by a photoplethysmography (PPG) sensor in a smartwatch. PPG makes use of reflected light to measure changes in light absorption, caused by changes in the blood volume due to heart beats. The seizure detection performance of a wearable PPG and ECG device were compared with that of ECG recorded with wired hospital equipment. The sensitivities of ECG measured with the hospital system, the wearable ECG device and the wearable PPG device were respectively 57%, 70% and 32%, with corresponding false alarms per hour of 1.92, 2.11 and 1.80. Whereas seizure detection performance using the wrist-worn PPG device was considerably lower, the performance using the wearable ECG was proven to be similar to that of the hospital ECG.
Seizure detection systems based on only the heart rate have around 2 false alarms per hour, which is too high for practical use. Behind-the-ear EEG channels can also be recorded with a wearable device. Firstly, the recognition of ictal patterns using only behind-the-ear EEG channels was investigated resulting in 65.7% sensitivity and 94.4% specificity. Secondly, an automated seizure detection algorithm was developed using only those behind-the-ear EEG channels. By
using the behind-the-ear seizure annotations, the automated algorithm obtained 64.1% sensitivity and 2.8 false alarms per 24 hours with the patient-independent model. The patient-specific model achieved 69.1% sensitivity and 0.49 false alarms per 24 hours.
In most cases, seizure detection algorithms in literature are developed using only one modality. However, combining different modalities can lead to a better performance. A multimodal automated seizure detection algorithm integrating behind-the-ear EEG and ECG was developed to detect focal seizures. In this framework, we quantified the added value of ECG compared to only behind-the-ear channels using a multicenter dataset. The multimodal algorithm outperformed the EEG-based seizure detection in two out of the three databases with an increase in sensitivity of 10% and 8% for the same false alarm rate.