Adaptive heart rate-based epileptic seizure detection using real-time user feedback

Abstract

Objective: Automated seizure detection in the home environment has attracted increasing interest in recent decades. Heart rate-based seizure detection is a way to detect temporal lobe epilepsy seizures at home, but patient-independent classifiers have been shown to be insufficiently accurate. This is due to the high patient-dependence of heart rate features, whereas this method does not use patient-specific data. Patient-specific classifiers take into account patient-specific data, but often not enough patient data are available for a fully robust patient-specific classifier. Therefore a real-time adaptive seizure detection algorithm is proposed here. Approach: The algorithm starts with a patient-independent classifier, but gradually adapts to the patient-specific characteristics while they are obtained ‘on the go’. This is done by using real-time user feedback to annotate previously generated alarms, causing an immediate update to the used support vector machine classifier. Data annotated as seizures are automatically removed from the updating procedure if their detection would lead to too many false alarms. This is done in order to cope with potentially incorrect feedback. Main results: The adaptive classifier resulted in an overall sensitivity of 77.12% and 1.24 false alarms per hour when applied to over 2833 h of heart rate data from 19 patients with 153 clinical seizures. This is around 30% fewer false alarms compared to a patient-independent classifier with a similar sensitivity. Significance: This low-complexity adaptive algorithm is able to deal well with incorrect feedback, making it ideal for a seizure warning system, and in the future it will also include complementary modalities to improve its performance.

Seizure detection based on heart rate variability using a wearable electrocardiography device

“…In two phase 1 studies, De Cooman et al 29,30 used adaptive seizure-detection algorithms that started out as patient-independent classifiers, and gradually adapted to patient-specific characteristics by using feedback from the users to update the classifier. This resulted in an improvement of FAR, and yet it remained at an unsatisfactorily high level of 29.8/day (sensitivity 77.1%) 29 and 2.56/night (sensitivity for nocturnal seizures 77.6%). 30 A recent comprehensive review of seizure detection using ictal autonomic changes (mostly HR) concluded that the quality of studies so far had been low and that the FAR was too high.…”

Personalizing Heart Rate-Based Seizure Detection Using Supervised SVM Transfer Learning

“…In practice, these annotations can be made in the hospital during, for example, presurgical evaluation, but they could also be made by the patient or their caregivers/family. Extra procedures should then be added to avoid a too big impact of incorrectly annotated data as patients are not always aware about whether they actually had a seizure or not (3,14). Ideally, an unsupervised approach could be used (9,25,26), which indicates that epileptic heart rate activity can be seen as an outlier to normal heart rate activity.…”

“…The proposed transfer learning approach is also compared to two different alternatives for personalization. The first alternative includes a fully PS approach which is trained with only PS data using the SVM classifier defined by (1) as in De Cooman et al (14). The other alternative is a so-called mixed model, in which both PI and PS data are used for training an SVM classifier defined by (1), but adapting the values of c i in (2) into c MIX i :…”

“…such that misclassification of PS data is more critical during training than misclassification of non-PS data. The value 4 is chosen as recommended in De Cooman et al (14).…”

“…However, due to the large inter-patient differences in heart rate characteristics, performance is too low for practical use. In order to increase the performance, models can be adapted to the patient heart rate characteristics (14,15). Different options are possible.…”