Table 2.

Classification outcomes

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

This table describes each parameter metric considering seizure and non-seizure case