Background: The brain, made up of billions of neurons and synapses, is the marvelous core of human thought, action and memory. However, if neuronal activity manifests into abnormal electrical activity across the brain, neural behavior may exhibit synchronous neural firings known as seizures. If unprovoked seizures occur repeatedly, a patient may be diagnosed with epilepsy. Purpose: The scope of this project is to develop an ambulatory seizure monitoring system that can be used away from a hospital, making it possible for the user to stay at home, and primary care personnel to monitor a patient's seizure activity in order to provide deeper analysis of the patient's condition and apply personalized intervention techniques. Methods: The ambulatory seizure monitoring device is a research device that has been developed with the objective of acquiring a portable, clean electroencephalography (EEG) signal and transmitting it wirelessly to a handheld device for processing and notification. Result: This device is comprised of 4 phases: acquisition, transmission, processing and notification. During the acquisition stage, the EEG signal is detected using EEG electrodes; these signals are filtered and amplified before being transmitted in the second stage. The processing stage encompasses the signal processing and seizure prediction. A notification is sent to the patient and designated contacts, given an impending seizure. Each of these phases is comprised of various design components, hardware and software. The experimental findings illustrate that there may be a triggering mechanism through the phase lock value method that enables seizure prediction. Conclusion: The device addresses the need for long-term monitoring of the patient's seizure condition in order to provide the clinician a better understanding of the seizure's duration and frequency and ultimately provide the best remedy for the patient.
[1]
Tzyy-Ping Jung,et al.
Noninvasive Neural Prostheses Using Mobile and Wireless EEG
,
2008,
Proceedings of the IEEE.
[2]
R. Matthews,et al.
Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes
,
2008,
2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.
[3]
Mark H. Myers,et al.
Amplitude suppression and chaos control in epileptic EEG signals.
,
2006,
Computational and mathematical methods in medicine.
[4]
Chwan-Lu Tseng,et al.
DESIGN AND IMPLEMENTATION OF WIRELESS MULTI-CHANNEL EEG RECORDING SYSTEM AND STUDY OF EEG CLUSTERING METHOD
,
2006
.
[5]
Gahangir Hossain,et al.
Seizure Prediction and Detection via Phase and Amplitude Lock Values
,
2016,
Front. Hum. Neurosci..
[6]
Robert Kozma,et al.
Modeling Brain Electrical Activity Involving Vagus Nerve Stimulation
,
2007
.
[7]
Chin-Teng Lin,et al.
Brain Computer Interface-Based Smart Living Environmental Auto-Adjustment Control System in UPnP Home Networking
,
2014,
IEEE Systems Journal.
[8]
T. Jung,et al.
Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces
,
2012,
IEEE Transactions on Neural Systems and Rehabilitation Engineering.
[9]
B. Litt,et al.
High-frequency oscillations and seizure generation in neocortical epilepsy.
,
2004,
Brain : a journal of neurology.
[10]
J. Gotman,et al.
High‐frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery
,
2010,
Annals of neurology.