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

TY - JOUR

T1 - Detection of seizures with ictal tachycardia, using heart rate variability and patient adaptive logistic regression machine learning methods

T2 - A hospital-based validation study

AU - Jeppesen, Jesper

AU - Lin, Katia

AU - Melo, Hiago Murilo

AU - Pavei, Jonatas

AU - Marques, Jefferson Luiz Brum

AU - Beniczky, Sándor

AU - Walz, Roger

N1 - Publisher Copyright: © 2024 The Authors. Epileptic Disorders published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.

PY - 2024/4

Y1 - 2024/4

N2 - Objective: Automated 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. Methods: Ictal 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. Results: The 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%). Significance: The 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.

AB - Objective: Automated 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. Methods: Ictal 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. Results: The 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%). Significance: The 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.

KW - electrocardiography

KW - epilepsy

KW - focal seizures

KW - seizure alarm

KW - Heart Rate/physiology

KW - Electroencephalography/methods

KW - Humans

KW - Logistic Models

KW - Epilepsies, Partial/complications

KW - Machine Learning

KW - Retrospective Studies

KW - Tachycardia/diagnosis

KW - Seizures

UR - http://www.scopus.com/inward/record.url?scp=85184859975&partnerID=8YFLogxK

U2 - 10.1002/epd2.20196

DO - 10.1002/epd2.20196

M3 - Journal article

C2 - 38334223

AN - SCOPUS:85184859975

SN - 1294-9361

VL - 26

SP - 199

EP - 208

JO - Epileptic Disorders

JF - Epileptic Disorders

IS - 2

ER -