Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods: A hospital‐based validation study

Abstract

AbstractObjectiveAutomated seizure detection of focal epileptic seizures is needed for objective seizure quantification to optimize the treatment of patients with epilepsy. Heart rate variability (HRV)‐based seizure detection using patient‐adaptive threshold with logistic regression machine learning (LRML) methods has presented promising performance in a study with a Danish patient cohort. The objective of this study was to assess the generalizability of the novel LRML seizure detection algorithm by validating it in a dataset recorded from long‐term video‐EEG monitoring (LTM) in a Brazilian patient cohort.MethodsIctal and inter‐ictal ECG‐data epochs recorded during LTM were analyzed retrospectively. Thirty‐four patients had 107 seizures (79 focal, 28 generalized tonic–clonic [GTC] including focal‐to‐bilateral‐tonic–clonic seizures) eligible for analysis, with a total of 185.5 h recording. Because HRV‐based seizure detection is only suitable in patients with marked ictal autonomic change, patients with >50 beats/min change in heart rate during seizures were selected as responders. The patient‐adaptive LRML seizure detection algorithm was applied to all elected ECG data, and results were computed separately for responders and non‐responders.ResultsThe patient‐adaptive LRML seizure detection algorithm yielded a sensitivity of 84.8% (95% CI: 75.6–93.9) with a false alarm rate of .25/24 h in the responder group (22 patients, 59 seizures). Twenty‐five of the 26 GTC seizures were detected (96.2%), and 25 of the 33 focal seizures without bilateral convulsions were detected (75.8%).SignificanceThe study confirms in a new, independent external dataset the good performance of seizure detection from a previous study and suggests that the method is generalizable. This method seems useful for detecting both generalized and focal epileptic seizures. The algorithm can be embedded in a wearable seizure detection system to alert patients and caregivers of seizures and generate objective seizure counts helping to optimize the treatment of the patients.