EPILEPTIC SEIZURES PREDICTION USING IOT AND MACHINE LEARNING
ABSTARCT :
Epilepsy is a type of neurological disorder that causes abnormal brain activities and creates epileptic seizures.Traditionally epileptic seizure prediction is realized with a visual examination of Electroencephalogram (EEG) signals. But this technique needs a long time EEG monitoring. So, the automatic epileptic seizures prediction schemes become a requirement at this point. This study proposes a method to classify epileptic seizures and normal EEG data by utilizing the Intrinsic Time-scale Decomposition (ITD)-based features. The dataset has been supplied from the database of the Epileptology Department of Bonn University. It contains 5 data groups A, B, C, D, E. The study aims to classify healthy and epileptic data, so data of groups A and E are used to perform evaluations of proposed methods. The EEG data are decomposed into Proper Rotation Components (PRCs) by ITD. The feature extraction methods are applied to the first five PRCs of each EEG data from healthy and epileptic individuals. These features are classified using K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Naïve Bayes, Support Vector Machine (SVM) and Logistic Regression classifiers. The results demonstrated that the epileptic data is differentiated from normal data by applying the nonlinear ITD with outstanding classification performance.
EXISTING SYSTEM :
? In existing system classic spirometry and peak flow detectors are used but the output is a graphical representation to observe by the physician more complex.
? The combination of the normal and multi-channel EEG-based classifier system paves the way to promote the performance of seizure detection.
? The performance of the algorithm is measured by using a large dataset and the compound of the normal ECG and multi-channel EEG assist to bridge the caliber and time gap.
DISADVANTAGE :
? The proposed method treats all seizure vocalizations as a single target event class, and models the seizure detection problem in terms of detecting the target vs non-target classes.
? In particular, CNN is among state of the art methods for rare sound event detection, which represents a similar type of problem as the seizure detection task.
? The disease imposes huge physical, psychological and social burdens on individuals and their families. Besides, epilepsy has a dramatic impact on the health care systems’ annual budgets.
PROPOSED SYSTEM :
? The proposed system aims to build a compact real-time monitoring and managing device to detect and eliminate epileptic seizures.
? In Proposed system we use Blood Pressure, Temperature sensor and heart pulse sensor to detect brain waves activity and body temperature and heart rate and stores it in to cloud .
? Using Cloud Data we can use machine learning algorithm to predict epileptic seizures of patient .This system aims to protect life and also aids to live a healthy and normal life.
ADVANTAGE :
? The various feature extraction methods, such as temporal, spectral, statistical, and nonlinear features, are utilized to obtain high-performance evaluations.
? The performance of the algorithm in biomedical signal processing studies is investigated, and it has been observed that high success.
? The performance of the system is insufficient for reliable independent monitoring, but the system provides a tool that reduces significantly the monitoring effort.
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