Cardiac-based detection of seizures in children with epilepsy.

INTRODUCTION: We evaluated a multi-parametric approach to seizure detection using cardiac and activity features to detect a wide range of seizures across different people using the same model. METHODS: Electrocardiogram (ECG) and accelerometer data were collected from a chest-worn sensor from 62 children aged 2-17 years undergoing video-electroencephalogram monitoring for clinical care. ECG data from 5 adults aged 31-48 years who experienced focal seizures were also analyzed from the PhysioNet database. A detection algorithm was developed based on a combination of multiple heart rhythm and motion parameters. RESULTS: Excluding patients with multiple seizures per hour and myoclonic jerks, 25 seizures were captured from 18 children. Using cardiac parameters only, 11/12 generalized seizures with clonic or tonic activity were detected as well as 7/13 focal seizures without generalization. Separately, cardiac parameters were evaluated using electrocardiogram data from 10 complex partial seizures in the PhysioNet database of which 7 were detected. False alarms averaged one per day. Movement-based parameters did not identify any seizures missed by cardiac parameters, but did improve detection time for 4 of the generalized seizures. CONCLUSION: Our data suggest that cardiac measures can detect seizures with bilateral motor features with high sensitivity, while detection of focal seizures depends on seizure duration and localization and may require customization of parameter thresholds.

Automated respiratory sinus arrhythmia measurement: Demonstration using executive function assessment.

Respiratory sinus arrhythmia (RSA) is a quantitative metric that reflects autonomic nervous system regulation and provides a physiological marker of attentional engagement that supports cognitive and affective regulatory processes. RSA can be added to executive function (EF) assessments with minimal participant burden because of the commercial availability of lightweight, wearable electrocardiogram (ECG) sensors. However, the inclusion of RSA data in large data collection efforts has been hindered by the time-intensive processing of RSA. In this study we evaluated the performance of an automated RSA-scoring method in the context of an EF study in preschool-aged children. The absolute differences in RSA across both scoring methods were small (mean RSA differences = -0.02-0.10), with little to no evidence of bias for the automated relative to the hand-scoring approach. Moreover, the relative rank-ordering of RSA across both scoring methods was strong (rs = .96-.99). Reliable changes in RSA from baseline to the EF task were highly similar across both scoring methods (96%-100% absolute agreement; Kappa = .83-1.0). On the basis of these findings, the automated RSA algorithm appears to be a suitable substitute for hand-scoring in the context of EF assessment.