Why the study?
Automated seizure detection using EEG is often patient-specific, needing high accuracy and very low false-positive rates with unclear generalizability across patients.
Does an automated machine learning seizure detection method accurately identify seizures and minimize false alarms in EEG recordings of patients with epilepsy?
Does an automated machine learning seizure detection method accurately identify seizures and minimize false alarms in EEG recordings of patients with epilepsy?
A machine learning method for automated seizure detection showed high accuracy in a single patient but limited generalizability to other patients, suggesting the need for patient-specific or cluster-specific models.
Does not support clinical adoption of automated seizure detection; leaves open patient-specific modeling approaches.
BACKGROUND: The development of automated seizure detection methods using EEG signals could be of great importance for the diagnosis and the monitoring of patients with epilepsy. These methods are often patient-specific and require high accuracy in detecting seizures but also very low false-positive rates. The aim of this study is to evaluate the performance of a seizure detection method using EEG signals by investigating its performance in correctly identifying seizures and in minimizing false alarms and to determine if it is generalizable to different patients. METHODS: We tested the method on about two hours of preictal/ictal and about ten hours of interictal EEG recordings of one patient from the Freiburg Seizure Prediction EEG database using machine learning techniques for data mining. Then, we tested the obtained model on six other patients of the same database. RESULTS: The method achieved very high performance in detecting seizures (close to 100% of correctly classified positive elements) with a very low false-positive rate when tested on one patient. Furthermore, the model portability or transfer analysis revealed that the method achieved good performance in one out of six patients from the same dataset. CONCLUSIONS: This result suggests a strategy to discover clusters of similar patients, for which it would be possible to train a general-purpose model for seizure detection.
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Zazzaro et al. (2022) studied this question.
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