TY  - JOUR
  - Greene, BR,de Chazal, P,Boylan, GB,Connolly, S,Reilly, RB
  - 2007
  - April
  - IEEE Transactions On Biomedical Engineering
  - Electrocardiogram based neonatal seizure detection
  - Validated
  - ()
  - ECG linear discriminant neonatal seizure detection EPILEPTIC SEIZURES EEG HEART CLASSIFICATION NEWBORN ABNORMALITIES RECORDINGS SIGNALS
  - 54
  - 673
  - 682
  - A method for the detection of seizures in the newborn using the electrocardiogram (ECG) signal is presented. Using a database of eight recordings, a method was developed for automatically annotating each 1-min epoch as "nonseizure" or "seizure." The system uses a linear discriminant classifier to process 41 heartbeat timing interval features. Performance assessment of the method showed that on a patient-specific basis an average accuracy of 70.5% was achieved in detecting seizures with associated sensitivity of 62.2% and specificity of 71.8%. On a patient-independent basis the average accuracy was 68.3% with sensitivity of 54.6% and specificity of 77.3%. Shifting the decision threshold for the patient-independent classifier allowed an increase in sensitivity to 78.4% at the expense of decreased specificity (51.6%), leading to increased false detections. The results of our ECG-based method are comparable with those reported for EEG-based neonatal seizure detection systems and offer the benefit of an easier acquisition methodology for seizure detection.
  - DOI 10.1109/TBME.2006.890137
DA  - 2007/04
ER  - 

@article{V43336150,
   = {Greene,  BR and de Chazal,  P and Boylan,  GB and Connolly,  S and Reilly,  RB },
   = {2007},
   = {April},
   = {IEEE Transactions On Biomedical Engineering},
   = {Electrocardiogram based neonatal seizure detection},
   = {Validated},
   = {()},
   = {ECG linear discriminant neonatal seizure detection EPILEPTIC SEIZURES EEG HEART CLASSIFICATION NEWBORN ABNORMALITIES RECORDINGS SIGNALS},
   = {54},
  pages = {673--682},
   = {{A method for the detection of seizures in the newborn using the electrocardiogram (ECG) signal is presented. Using a database of eight recordings, a method was developed for automatically annotating each 1-min epoch as "nonseizure" or "seizure." The system uses a linear discriminant classifier to process 41 heartbeat timing interval features. Performance assessment of the method showed that on a patient-specific basis an average accuracy of 70.5% was achieved in detecting seizures with associated sensitivity of 62.2% and specificity of 71.8%. On a patient-independent basis the average accuracy was 68.3% with sensitivity of 54.6% and specificity of 77.3%. Shifting the decision threshold for the patient-independent classifier allowed an increase in sensitivity to 78.4% at the expense of decreased specificity (51.6%), leading to increased false detections. The results of our ECG-based method are comparable with those reported for EEG-based neonatal seizure detection systems and offer the benefit of an easier acquisition methodology for seizure detection.}},
   = {DOI 10.1109/TBME.2006.890137},
  source = {IRIS}
}