An innovative portable device for detecting and sending the occurrence of the seizure

Background:Epilepsy is the most common neurological diseases.Tonic-clonic seizures(Grand Mal)are a type of generalized seizure with fast spiactivity,gradually decreasing in frequency and increasing in amplitude(tonic phase)interrupted by slow waves(clonic phase),and followed by post-ictal EEG depression. The unpredictable nature of seizures poses risks for the patient like falling down and other dangerous situations. Objectives:we are going to present an innovative portable,Wireless EEG Monitoring with ability of real time detection of grand-mal seizure in the started point and sending alarm to patient’s family and doctor and asking for an ambulance with sending the GPS data to emergency unit . It can facilitate timely intervention and hence minimize risks. system can analyze recorded EEG waves from the Four main brain areas, then extract rapid increase spikes of EEG rhythms as feature of seizure-occurrence-time.The task of software is to analyze EEG waves and extracting features and Commanding hardware unit to call and report emergency situation to increase patients’safety. the patient’s EEG information can be viewed at any time by the doctor. Materials/Patients and Methods:The proposed system is an wireless and portable EEG including two innovative hardware and software parts.The main part is the hardware,processing and controlling unit using ARM chip and telecommunication unit.The first stage of the process is capturing raw EEG signals by placing two bipolar electrodes in each mentioned areas.then EEG goes to processing unit for extracting epileptic seizure features,which includes by rapid polyspike activity with increase in EEG rhythms using Fast Fourier Transform(FFT) as software unit.These features considered as occurrence time of seizure.using this features control unit commands telecommunication to call defined contacts. Results:This Methodology was applied on EEG data sets that belong to three subject groups(Healthy subjects,Epileptic subjects during Seizure-free interval,Epilepticsubjects during a seizure).An overall classification accuracy of 99%was achieved and the device could have an emergency call to the selected contacts for patient s emergency condition Conclusion:The results confirmed that the proposed algorithm and Device has a potential in classification of EEG signals and detection of Epileptic seizures that could help the doctors and the patients, even the patients’families for better detection,protection and care .