Automated seizure detection: Unrecognized challenges, unexpected insights
نویسندگان
چکیده
منابع مشابه
Automated seizure detection: unrecognized challenges, unexpected insights.
One of epileptology's fundamental aims is the formulation of a universal, internally consistent seizure definition. To assess this aim's feasibility three signal analysis methods were applied to a seizure time series and performance comparisons were undertaken among them and with respect to a validated algorithm. One of the methods uses a Fisher's matrix weighted measure of the rate of paramete...
متن کاملAdvances in automated neonatal seizure detection
This chapter highlights the current approaches in automated neonatal seizure detection and in particular focuses on classifier based methods. Automated detection of neonatal seizures has the potential to greatly improve the outcome of patients in the neonatal intensive care unit. The electroencephalogram (EEG) is the only signal on which 100% of electrographic seizures are visible and thus is c...
متن کاملAutomated Rapid Seizure Detection in the Human ECoG
Automated seizure detection with high specificity and sensitivity is a highly desirable but elusive goal. The failure to develop a reliable system despite decades of effort is due in part to the non-stationary and noise in the EEG/ECoG signals, as well as to the rudimentary mathematical treatment it has received. Another important limitation of present methods is their inability to perform seiz...
متن کاملAn evaluation of automated neonatal seizure detection methods.
OBJECTIVE To evaluate 3 published automated algorithms for detecting seizures in neonatal EEG. METHODS One-minute, artifact-free EEG segments consisting of either EEG seizure activity or non-seizure EEG activity were extracted from EEG recordings of 13 neonates. Three published neonatal seizure detection algorithms were tested on each EEG recording. In an attempt to obtain improved detection ...
متن کاملDeep Architectures for Automated Seizure Detection in Scalp EEGs
Automated seizure detection using clinical electroencephalograms is a challenging machine learning problem because the multichannel signal often has an extremely low signal to noise ratio. Events of interest such as seizures are easily confused with signal artifacts (e.g, eye movements) or benign variants (e.g., slowing). Commercially available systems suffer from unacceptably high false alarm ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Epilepsy & Behavior
سال: 2011
ISSN: 1525-5050
DOI: 10.1016/j.yebeh.2011.09.011