Biyolojik ve internet sensorlarının kullanımı ile epilepsi nöbetlerinin/ krizlerinin tahmin edilmesi

The biosensors became most important for monitoring patient status; the seizure epilepsy is taken into consideration to monitor the patients and predict their status before the seizure happen. The epilepsy is the 4th most common neurologic disorder affecting people of different ages which about 65 million people affected around the world, this disease is happen randomly and may be caused a sadden unexpected death. The standard monitoring epileptic seizures system involves video/EEG (electro-encephalography), which is bothersome for the patient, as EEG electrodes are attached to the patient head. Seriously, help and alert patient before the seizure is one of the issue that the researchers and designers attention. For that there are spectrums of portable seizure detection systems available in markets which are based on non-EEG signal. This study is conducted to use the combined a portable wrist-band, together with smartphone which can easily carried by the patients. The portable wrist-band integrated four sensors to read the signal of three physiological parameters such as: Electromyography (EMG), Heart rate (HR), oxygen level (SpO2) and accelerometer (ACM) biosensor; for facilitate in providing separable signal variation to recognize the status of patients. The study applied on the Iraqi's patients whom visit the Department of Neurology at Baghdad Hospital, almost all the patients had no-seizure during the EEG-Video recording which was one of the problems was faced during the trials of device, according to this problem the work change from real monitoring into virtual study. In this study was incorporated Arduino platform ‗wrist-band‖ as a component part of the system. From the applied test it showed that the fixed wrist-band is confortable to use by the patient hand. Also, the used sensors were reflected a good signals of the studied parameters. The proposed system provide difference services such as heart rata tracking and oxygen level , user tracking location and emergency notification. The Arduino-wear and android-smartphone side of the proposed system is implemented by the Android studio using java programming, while the portable written by Arduino programing language. The results of the proposed system response show a promising outcome that can depend on for predicting the seizure. Keyword: Biosensors, Wearable sensors, Epilepsy, Seizures, Non-EEG, EMG, Autonomic Alterations in Epilepsy