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Table 2 Classification outcomes

From: A review of epileptic seizure detection using machine learning classifiers

Acronym Detection type Real-world scenario
TP True-positive If a person suffers to ‘seizure’ and also correctly detected as a ‘seizure’
TN True-negative The person is actually normal and the classifier also detected as a ‘non-seizure’
FP False-positive Incorrect detection, when the classifier detects the normal patient as a ‘seizure’ case
FN False-negative Incorrect detection, when the classifier detects the person with ‘seizure(s)’ as a normal person. This is a severe problem in health informatics research
  1. This table describes each parameter metric considering seizure and non-seizure case