A model of heart rate changes to detect seizures in severe epilepsy

W.J.C. Elmpt, van; T.M.E. Nijsen; P.A.M. Griep; J.B.A.M. Arends

doi:10.1016/j.seizure.2006.03.005

A model of heart rate changes to detect seizures in severe epilepsy

W.J.C. Elmpt, van, T.M.E. Nijsen, P.A.M. Griep, J.B.A.M. Arends

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Abstract

Heart rate changes were monitored in 10 patients with 104 seizures, mostly tonic and myoclonic, to assess the value of various modalities for the detection of seizures based on heart rate. EEG/video monitoring served as the golden standard. Two algorithms were developed. First, a curve-fitting algorithm was used to characterize the heart rate patterns. A second algorithm based on a moving median filter was developed for automatic detection of the heart rate change onset. For varying model parameters the sensitivity (SENS) and positive predictive values (PPV) were determined

Original language	English
Pages (from-to)	366-375
Number of pages	10
Journal	Seizure
Volume	15
Issue number	6
DOIs	https://doi.org/10.1016/j.seizure.2006.03.005
Publication status	Published - 2006

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title = "A model of heart rate changes to detect seizures in severe epilepsy",

abstract = "Heart rate changes were monitored in 10 patients with 104 seizures, mostly tonic and myoclonic, to assess the value of various modalities for the detection of seizures based on heart rate. EEG/video monitoring served as the golden standard. Two algorithms were developed. First, a curve-fitting algorithm was used to characterize the heart rate patterns. A second algorithm based on a moving median filter was developed for automatic detection of the heart rate change onset. For varying model parameters the sensitivity (SENS) and positive predictive values (PPV) were determined",

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AB - Heart rate changes were monitored in 10 patients with 104 seizures, mostly tonic and myoclonic, to assess the value of various modalities for the detection of seizures based on heart rate. EEG/video monitoring served as the golden standard. Two algorithms were developed. First, a curve-fitting algorithm was used to characterize the heart rate patterns. A second algorithm based on a moving median filter was developed for automatic detection of the heart rate change onset. For varying model parameters the sensitivity (SENS) and positive predictive values (PPV) were determined

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A model of heart rate changes to detect seizures in severe epilepsy

Abstract

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