Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information Advances in Nonstationary Electrophysiological Signal Analysis and Processing

Mostefa Mesbah, Malarvili Balakrishnan, Paul B. Colditz, Boualem Boashash

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

Original languageEnglish
Article number215
JournalEurasip Journal on Advances in Signal Processing
Volume2012
Issue number1
DOIs
Publication statusPublished - 2012

Fingerprint

Signal analysis
Electroencephalography
Signal processing
Classifiers
Fusion reactions
Electrocardiography
Information use
Detectors

Keywords

  • Classifier combination
  • EEG
  • Features fusion
  • Heart rate variability
  • IF
  • MBD
  • Newborn seizure
  • Seizure detection
  • TFD
  • Time-frequency representation

ASJC Scopus subject areas

  • Signal Processing
  • Hardware and Architecture
  • Electrical and Electronic Engineering

Cite this

@article{a58854bcc5514275852fabea967ce661,
title = "Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information Advances in Nonstationary Electrophysiological Signal Analysis and Processing",
abstract = "This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20{\%} sensitivity and 88.60{\%} specificity for the feature fusion case and 95.20{\%} sensitivity and 94.30{\%} specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90{\%}, 86.50{\%}) or HRV only (85.70{\%}, 84.60{\%}).",
keywords = "Classifier combination, EEG, Features fusion, Heart rate variability, IF, MBD, Newborn seizure, Seizure detection, TFD, Time-frequency representation",
author = "Mostefa Mesbah and Malarvili Balakrishnan and Colditz, {Paul B.} and Boualem Boashash",
year = "2012",
doi = "10.1186/1687-6180-2012-215",
language = "English",
volume = "2012",
journal = "Eurasip Journal on Advances in Signal Processing",
issn = "1687-6172",
publisher = "Springer Publishing Company",
number = "1",

}

TY - JOUR

T1 - Automatic seizure detection based on the combination of newborn multi-channel EEG and HRV information Advances in Nonstationary Electrophysiological Signal Analysis and Processing

AU - Mesbah, Mostefa

AU - Balakrishnan, Malarvili

AU - Colditz, Paul B.

AU - Boashash, Boualem

PY - 2012

Y1 - 2012

N2 - This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

AB - This article proposes a new method for newborn seizure detection that uses information extracted from both multi-channel electroencephalogram (EEG) and a single channel electrocardiogram (ECG). The aim of the study is to assess whether additional information extracted from ECG can improve the performance of seizure detectors based solely on EEG. Two different approaches were used to combine this extracted information. The first approach, known as feature fusion, involves combining features extracted from EEG and heart rate variability (HRV) into a single feature vector prior to feeding it to a classifier. The second approach, called classifier or decision fusion, is achieved by combining the independent decisions of the EEG and the HRV-based classifiers. Tested on recordings obtained from eight newborns with identified EEG seizures, the proposed neonatal seizure detection algorithms achieved 95.20% sensitivity and 88.60% specificity for the feature fusion case and 95.20% sensitivity and 94.30% specificity for the classifier fusion case. These results are considerably better than those involving classifiers using EEG only (80.90%, 86.50%) or HRV only (85.70%, 84.60%).

KW - Classifier combination

KW - EEG

KW - Features fusion

KW - Heart rate variability

KW - IF

KW - MBD

KW - Newborn seizure

KW - Seizure detection

KW - TFD

KW - Time-frequency representation

UR - http://www.scopus.com/inward/record.url?scp=84887074890&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84887074890&partnerID=8YFLogxK

U2 - 10.1186/1687-6180-2012-215

DO - 10.1186/1687-6180-2012-215

M3 - Article

VL - 2012

JO - Eurasip Journal on Advances in Signal Processing

JF - Eurasip Journal on Advances in Signal Processing

SN - 1687-6172

IS - 1

M1 - 215

ER -