IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification

2019 ◽  
Author(s):  
Kuldeep Singh ◽  
Jyoteesh Malhotra
Keyword(s):  
Cloud Computing ◽  
Random Forest ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection ◽  
Random Forest Classification ◽  
Forest Classification

scholarly journals Patient-dependent epilepsy seizure detection using random forest classification over one-dimension transformed EEG data

2016 ◽  
Author(s):  
Marco A. Pinto-Orellana ◽  
Fábio R. Cerqueira
Keyword(s):  
Random Forest ◽  
False Positive Rate ◽  
Seizure Detection ◽  
Computational Method ◽  
One Dimension ◽  
Random Forest Classification ◽  
Envelope Detector ◽  
Forest Classification ◽  
Positive Rate ◽  
Pass Through

AbstractThis work presents a computational method for improving seizure detection for epilepsy diagnosis. Epilepsy isthe second most common neurological disease impacting between 40 and 50 million of patients in the world and it proper diagnosis using electroencephalographic signals implies a long and expensive process which involves medical specialists. The proposed system is a patient-dependent offline system which performs an automatic detection of seizures in brainwaves applying a random forest classifier. Features are extracted using one-dimension reduced information from a spectro-temporal transformation of the biosignals which pass through an envelope detector. The performance of this method reached 97.12% of specificity, 99.29% of sensitivity, and a 0.77 h−1 false positive rate. Thus, the method hereby proposed has great potential for diagnosis support in clinical environments.

Automatic epileptic seizure detection in EEGs using MF-DFA, SVM based on cloud computing

2017 ◽  
Vol 25 (2) ◽  
pp. 261-272 ◽  
Author(s):  
Zhongnan Zhang ◽  
Tingxi Wen ◽  
Wei Huang ◽  
Meihong Wang ◽  
Chunfeng Li
Keyword(s):  
Cloud Computing ◽  
Epileptic Seizure ◽  
Seizure Detection ◽  
Epileptic Seizure Detection

Early Seizure Detection Algorithm Based on Intracranial EEG and Random Forest Classification

2015 ◽  
Vol 25 (05) ◽  
pp. 1550023 ◽  
Author(s):  
Cristian Donos ◽  
Matthias Dümpelmann ◽  
Andreas Schulze-Bonhage
Keyword(s):  
Random Forest ◽  
Seizure Detection ◽  
Detection Algorithm ◽  
Seizure Onset ◽  
Intracranial Eeg ◽  
Random Forest Classification ◽  
Forest Classification ◽  
Seizure Onset Zone ◽  
Early Seizure

The goal of this study is to provide a seizure detection algorithm that is relatively simple to implement on a microcontroller, so it can be used for an implantable closed loop stimulation device. We propose a set of 11 simple time domain and power bands features, computed from one intracranial EEG contact located in the seizure onset zone. The classification of the features is performed using a random forest classifier. Depending on the training datasets and the optimization preferences, the performance of the algorithm were: 93.84% mean sensitivity (100% median sensitivity), 3.03 s mean (1.75 s median) detection delays and 0.33/h mean (0.07/h median) false detections per hour.

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