# Real-Time Computing Strategies for Automatic Detection of EEG Seizures in ICU
## 4 Discussion  
The model we have developed offers a novel perspective on the diagnosis of epilepsy, enhancing accuracy by focusing on the primary medical conditions underlying the seizures. By tailoring machine learning techniques to the unique attributes of each patient subgroup, our approach significantly improves the diagnostic precision for seizure detection. On one hand, [PERSON] et al. proposed the Empirical Mode Decomposition application as a machine learning algorithm and a classifier method based on entropy using discrete wavelet transforms. They classified up to four types of seizures, non-convulsive focal seizure, simple partial seizure, tonic seizure, and non-specific generalised seizure, and they reached an accuracy of 95% [17]. On the other hand, [PERSON] et al. proposed to extract the entropy from the samples using SVM-LS (least squared), KNN, and Bayes Naive as classifiers. This model achieved a mean accuracy of 99.5%, a sensitivity of 99.01%, and a specificity of 100%. The database had high variability, though, with recordings at extra and intracranial sites [18]. As other authors described in the literature, [PERSON] et al. applied different classification models from a database containing extra- and intracranial recordings. They obtained an accuracy greater than 92% using KNN and extracranial recording, with a higher proportion of accuracy regarding intracranial recording, having an accuracy of 99% (_FRNN: Fancy-Rough Nearest Neighbourhood_). In our study, we have used numerical methods for digital signal extraction, considering the signal mean during a time interval, including interictal and ictal periods, and the peak value along with this time. For that, we have processed electroencephal-physical signals from extracranial recordings using several mathematical transforms (wavelet transforms (biorthogonal 1.5, 3.3, and 4.4; reverse biorthogonal 6.8; Db 3, 4, 5, and 8; Meyer transform; Symlets 4 and 6; Coiflets 3 and 4), Haar transforms with order 3, Hilbert transform, Empirical Mode Decomposition, and first and second intrinsic mode functions), choosing the features with high diagnostics performance. After analysing different mathematical transforms on each signal, our research applied the biorthogonal 1.5 and 3.3 transforms; Hilbert; Db 3, 4, 5, and 8; and Symlets 4 and 6 transforms. Similarly to our approach, other researchers have also employed classification algorithms to study epilepsy. For instance, [PERSON] et al. developed a method to extract quantitative EEG characteristics using discrete wavelet transform coupled with an algorithm based on eight eigenvalues. This technique was applied to differentiate between normal control recordings, recordings not indicative of epilepsy, and recordings suggestive of epilepsy from epileptic patients. Their method achieved an impressive accuracy rate of up to 97% [46]. In another study, [PERSON] et al. utilised the major Lyapunov exponent derived from discrete wavelet transforms, processed through a Support Vector Machine (SVM) classifier. This method was capable of detecting 100% of seizures. However, effective signal change observation required a significantly longer pre-seizure time range (from 90 to 10 min before the seizure). Additionally, the technique occasionally misidentified non-epileptic events as seizures [47; 48]. Other published reports presented high accuracy in seizure detection, higher than 99% [2]. However, these researchers with such high precision are based on public datasets, mainly Bonn, Freiburg, and CHB-MIT EEG datasets. The first one has five dataset subtypes, one corresponding to seizures. These 100 recordings are not thoroughly described (focal, generalised, convulsive, non-convulsive, and other relevant information); the second one has only 21 patients, and the third one includes 32 patients from the paediatric population. After applying various transforms to the EEG signals, our study examined different classifiers based on the ten most optimal features.