Enhanced Epilepsy Seizure Detection and Smart Phone APP for Monitoring Seizures Based on EEG Classification

Automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. We developed a technique for epilepsy seizure detection using EEG signals. The signal will be pre-processed and filtered using multiple filters. Then, the filtered signal will be decomposed into sub-bands. Furthermore, feature extraction is applied; we developed a combined feature consists of combining three features into one. Finally, we used well-known classifiers such as Support Vector Machine (SVM), Artificial Neural Network (ANN), and K-Nears Neighbor (KNN) to differentiate between epileptic and no epileptic signals, and we achieved an accuracy of 98%. Furthermore, we developed an Android-based smartphone application for monitoring epilepsy detection based on the classification results of the EEG signal. A notification will be sent to the patient, doctors, and family members when an epilepsy seizure occurs. Once the EEG signal is classified as epileptic, the App will display a visual notification indicating that Epileptic Seizure has been detected. Moreover, it will trigger an alarm and send a message notification to all associated phone numbers. Although we are using an EEG signal from a dataset, we have generated both normal and epileptic EEG signals using a waveform generator, and we have displayed those signals on the spectrum analyzer for future real time detection using our Android App

An Efficient Automated Technique and Smartphone Application for Epilepsy Seizure Detection Using EEG signals

Epilepsy is a neurological disorder disease that affects the central nervous system of the human brain that can disrupt the activity of the nervous cells in the brain which will result in unusual behavior that can lead to loss of consciousness called epileptic seizure that can endanger the life of the patient. Therefore, automated epilepsy seizure detection is the solution to the limitation and time consuming of manual epilepsy monitoring and detection using EEG signals. Thus, using MATLAB R2014b, we developed a technique for epilepsy seizure detection using EEG signals, and we achieved an accuracy of 97%. For our main contribution, we developed an Android-based smartphone application for monitoring epilepsy detection based on the classification results of the EEG signal. A notification will be sent to the patient, doctors, and family members when an epilepsy seizure occurs. Once the EEG signal is classified as epileptic, the App will display a visual notification indicating that Epileptic Seizure has been detected. Moreover, it will trigger an alarm and send a message notification to all associated phone numbers. The main goal of our research is to develop an APP that will read the signal from the brain through a Bluetooth device, and process the signal on the APP to determine if it is normal or abnormal

Transparent authentication: Utilising heart rate for user authentication

There has been exponential growth in the use of wearable technologies in the last decade with smart watches having a large share of the market. Smart watches were primarily used for health and fitness purposes but recent years have seen a rise in their deployment in other areas. Recent smart watches are fitted with sensors with enhanced functionality and capabilities. For example, some function as standalone device with the ability to create activity logs and transmit data to a secondary device. The capability has contributed to their increased usage in recent years with researchers focusing on their potential. This paper explores the ability to extract physiological data from smart watch technology to achieve user authentication. The approach is suitable not only because of the capacity for data capture but also easy connectivity with other devices - principally the Smartphone. For the purpose of this study, heart rate data is captured and extracted from 30 subjects continually over an hour. While security is the ultimate goal, usability should also be key consideration. Most bioelectrical signals like heart rate are non-stationary time-dependent signals therefore Discrete Wavelet Transform (DWT) is employed. DWT decomposes the bioelectrical signal into n level sub-bands of detail coefficients and approximation coefficients. Biorthogonal Wavelet (bior 4.4) is applied to extract features from the four levels of detail coefficents. Ten statistical features are extracted from each level of the coffecient sub-band. Classification of each sub-band levels are done using a Feedforward neural Network (FF-NN). The 1 st , 2 nd , 3 rd and 4 th levels had an Equal Error Rate (EER) of 17.20%, 18.17%, 20.93% and 21.83% respectively. To improve the EER, fusion of the four level sub-band is applied at the feature level. The proposed fusion showed an improved result over the initial result with an EER of 11.25% As a one-off authentication decision, an 11% EER is not ideal, its use on a continuous basis makes this more than feasible in practice

Edge-based Compression and Classification for Smart Healthcare Systems: Concept, Implementation and Evaluation

Smart healthcare systems require recording, transmitting and processing large volumes of multimodal medical data generated from different types of sensors and medical devices, which is challenging and may turn some of the remote health monitoring applications impractical. Moving computational intelligence to the net- work edge is a promising approach for providing efficient and convenient ways for continuous-remote monitoring. Implementing efficient edge-based classification and data reduction techniques are of paramount importance to enable smart health- care systems with efficient real-time and cost-effective remote monitoring. Thus, we present our vision of leveraging edge computing to monitor, process, and make au- tonomous decisions for smart health applications. In particular, we present and im- plement an accurate and lightweight classification mechanism that, leveraging some time-domain features extracted from the vital signs, allows for a reliable seizures detection at the network edge with precise classification accuracy and low com- putational requirement. We then propose and implement a selective data transfer scheme, which opts for the most convenient way for data transmission depending on the detected patient’s conditions. In addition to that, we propose a reliable energy-efficient emergency notification system for epileptic seizure detection, based on conceptual learning and fuzzy classification. Our experimental results assess the performance of the proposed system in terms of data reduction, classification accuracy, battery lifetime, and transmission delay. We show the effectiveness of our system and its ability to outperform conventional remote monitoring systems that ignore data processing at the edge by: (i) achieving 98.3% classification accuracy for seizures detection, (ii) extending battery lifetime by 60%, and (iii) decreasing average transmission delay by 90%

Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients

This is the Accepted Manuscript version of the following article: I. Mporas, D. Triantafyllopoulos, V. Megalooikonomou, “Real-Time Management of Multimodal Streaming Data for Monitoring of Epileptic Patients”, Journal of Medical Systems, Vol. 40(45), December 2015. The final published versions is available at: https://link.springer.com/article/10.1007%2Fs10916-015-0403-3 © Springer Science+Business Media New York 2015.New generation of healthcare is represented by wearable health monitoring systems, which provide real-time monitoring of patient’s physiological parameters. It is expected that continuous ambulatory monitoring of vital signals will improve treatment of patients and enable proactive personal health management. In this paper, we present the implementation of a multimodal real-time system for epilepsy management. The proposed methodology is based on a data streaming architecture and efficient management of a big flow of physiological parameters. The performance of this architecture is examined for varying spatial resolution of the recorded data.Peer reviewedFinal Accepted Versio

A Model for real time monitoring of epileptic patients

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityEffective treatment and therapy in epileptic patients require thorough monitoring of seizures. Medical care givers require information on number of seizure occurrences, duration of seizure and magnitude. People suffering from epilepsy face tremendous problems in regards to epileptic seizure monitoring. The typical way to diagnose and monitor epileptic patients is by use of electroencephalography (EEG) which requires monitoring within the confines of the hospital. EEG equipment is available in very few hospitals in Kenya and that is an impediment to proper therapy and treatment for epileptic patients. The challenges faced in using the existing methods include; lack of flexibility for the patient as there is need for long term monitoring in a hospital setup, financial burden on the patients when they are hospitalized and obtrusive nature of the EEG monitoring making it not suitable for monitoring outdoors. This study applies agile methodology to design, develop and test a model for real time monitoring of patients with tonic-clonic epileptic seizures. This model is hardware based, with the capability to send alerts to a family member in the event of a seizure. The patient can also view their seizure history from a mobile application installed on their smartphones. The device was created using Arduino Uno, a tri-axis accelerometer for motion detection and a Global system for Mobile Communication (GSM) module for communication. This model promotes long term, flexible and inexpensive mode of epileptic seizure monitoring therefore contributing to effective treatment of epileptic patients