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• W2142176585 abstract "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." @default.
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• W2142176585 date "2007-04-01" @default.
• W2142176585 modified "2023-10-05" @default.
• W2142176585 title "Electrocardiogram Based Neonatal Seizure Detection" @default.
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• W2142176585 doi "https://doi.org/10.1109/tbme.2006.890137" @default.
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