Cardiac-based detection of seizures in children with epilepsy
Meghan Hegarty-Craver1, Barbara L Kroner2, Adrian Bumbut3
1RTI International, Technology Advancement and Commercialization, United States.
Insights
This study shows that using heart rhythm and activity data can detect many seizures in children and adults. Cardiac measures are sensitive for generalized seizures, but focal seizure detection may need personalized settings.
Area of Science:
- Biomedical Engineering
- Neurology
- Cardiology
Background:
- Seizure detection is crucial for patient care and research.
- Existing methods may have limitations in detecting diverse seizure types.
- A multi-parametric approach integrating physiological data shows promise.
Purpose of the Study:
- To evaluate a multi-parametric seizure detection model using cardiac and activity data.
- To assess the model's effectiveness across different seizure types and patient demographics.
- To develop a unified seizure detection model.
Main Methods:
- Collected electrocardiogram (ECG) and accelerometer data from a chest-worn sensor in 62 children (2-17 years).
- Analyzed ECG data from 5 adults (31-48 years) with focal seizures from PhysioNet.
- Developed a detection algorithm combining heart rhythm and motion parameters.
Main Results:
- Cardiac parameters detected 11/12 generalized seizures and 7/13 focal seizures in children.
- In adults, 7/10 complex partial seizures were detected using cardiac data.
- Movement parameters improved detection time for generalized seizures but did not detect missed seizures.
Conclusions:
- Cardiac measures demonstrate high sensitivity for detecting seizures with bilateral motor features.
- Detection of focal seizures is influenced by duration and localization, potentially requiring customized thresholds.
- A multi-parametric approach offers a promising avenue for comprehensive seizure detection.
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.
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