In 2015, stroke was the number one cause of death in Indonesia. The majority type of stroke is ischemic. The standard tool for diagnosing stroke is CT-Scan. For developing countries like Indonesia, the availability of CT-Scan is very limited and still relatively expensive. Because of the availability, another device that potential to diagnose stroke in Indonesia is EEG. Ischemic stroke occurs because of obstruction that can make the cerebral blood flow (CBF) on a person with stroke has become lower than CBF on a normal person (control) so that the EEG signal have a deceleration. On this study, we perform the ability of 1D Convolutional Neural Network (1DCNN) to construct classification model that can distinguish the EEG and EOG stroke data from EEG and EOG control data. To accelerate training process our model we use Batch Normalization. Involving 62 person data object and from leave one out the scenario with five times repetition of measurement we obtain the average of accuracy 0.86 (F-Score 0.861) only at 200 epoch. This result is better than all over shallow and popular classifiers as the comparator (the best result of accuracy 0.69 and F-Score 0.72 ). The feature used in our study were only 24 'handcrafted' feature with simple feature extraction process.
until now, designing a reliable image segmentation algorithm is still an open problem. Research related to this matter is still underway, but in one occasion we may be faced with the problem for selection image segmentation algorithms that will we use? To get the solution of this problem we need a good technical evaluation of image segmentation algorithms. With the technique, it is expected we can finally choose and use the right image segmentation algorithm. Pixel matching (Pm) an image segmentation algorithm evaluation techniques are common but considered less complete and does not support the refinement aspects evaluation. In this study we present two techniques for the evaluation of the segmentation algorithm: Local Consistency Error (LCE) and boundary matching. Furthermore both of techniques will use for evaluate segmentation algorithms based on Support Vector Machine (SVM) with a variety of simple features. In addition, as a comparison, k-mean will used as the base segmentation technique. From the experimental result showed that in general segmentation algorithm using SVM produces a better accuracy than k-means. The highest accuracy is obtained when the value is used as the SVM as classifier and Hue Saturate Value (HSV) as a feature. Sequentially evaluation value obtained was 90. 614% (Pm), 0.106 (LCE), and the highest value of precision and recall for matching boundary are 0.419 and 0.721 (when radius = 5 pixels).
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.