EPILEPSY PATIENT MONITORING SYSTEM
Abstract : Epilepsy is a chronic neurological disorder with several different types of seizures, some of them characterized by involuntary recurrent convulsions, which have a great impact on the everyday life of the patients. Several solutions have been proposed in the literature to detect this type of seizures and to monitor the patient; however, these approaches lack in ergonomic issues and in the suitable integration with the health system. This research makes an in-depth analysis of the main factors that an epileptic detection and monitoring tool should accomplish. Furthermore, we introduce the architecture for a specific epilepsy detection and monitoring platform, fulfilling these factors. Special attention has been given to the part of the system the patient should wear, providing details of this part of the platform. Finally, a partial implementation has been deployed and several tests have been proposed and carried out in order to make some design decisions.
Different biometric signals have been Exisitng for detecting the epileptic seizures.
The main data source to do so is the EEG, measuring the electrical activity of the brain to detect the epileptic seizures.
But in Existing System There is no data maintain in server and monitor.
As an alternative and sometimes supplement to EEG there exist many sensors embedded in clothing or worn on the body to obtain bio-signals such as gyroscopes, accelerometers, pulse rate, temperature sensors, magnetometers, galvanic skin response sensors (GSR), implanted advisory system, electromyography, video detection systems, mattress sensor, and audio systems.
The data was analysed to discover if values for each of the attributes are different for each patient, together with the investigation of epilepsy ‘terminology’ and existing seizure type classifications/categories were analysed so that an ‘individual’ seizure type patient profile could be formed.
There are very few experiments with sensor-based IoT devices that have been endorsed by the hospitals and a large problem is poor information when caring for people with epilepsy or doing epilepsy clinical trials.
There is great potential to vastly increase the efficacy of epilepsy management using biomedical devices that can improve the quality of information.
The greatest challenge of all to help solve these problems is the enabling collaboration between people with differences in expertise
The negative impact of uncontrolled seizures spreads beyond the individual to affect their family, friends, and society.
Insufficient knowledge about epilepsy, which is a very common disorder, has a great and negative impact on people with epilepsy, their families and communities, and the healthcare systems.
In Proposed system we use Accelerometer, GSR sensor to detect brain waves activity .
We will monitor accelerometer and GSR value of patient and store it to Database for feature prediction purpose.
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.
The task of acquiring and sending the data from the different epilepsy devices involved in capturing seizure data, to the proposed cloud platform.
The proposed cloud platform provides all the necessary services for the clinician to manage, process and visualise the seizure data.
The devices have already been validated, and connectivity options identified, and since they are beneficial for epilepsy they can be proposed in the IoT based Epilepsy monitoring model.
The IoT based Epilepsy monitoring model has been proposed and can be adopted by the PMP framework in future developments.
It is important to understand the performance of iSeiz if the bracelet is placed on different locations on the body to capture various movements.
Our proposed approach in utilization of a secure cloud-based database to record seizure data from various epilepsy patients appears to be practical and efficient, particularly for collaborative diagnosis.
In, a three-layer cognitive ring is proposed to achieve a good performance and high intelligence and merges human cognition with the system design.
The majority of the deep learning methods discussed previously exhibit a good performance but are incomparable to the deep learning models in other fields, such as computer vision.
An automatic and advanced method for EEG-based seizure detection and monitoring to be used as a component of the proposed cognitive healthcare IoT (CHIoT) framework.
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