Literature DB >> 27647960

Ambulatory Seizure Monitoring: From Concept to Prototype Device.

Mark H Myers1, Madeline Threatt2, Karsten M Solies2, Brent M McFerrin2, Lindsey B Hopf2, J Douglas Birdwell2, Karl A Sillay3.   

Abstract

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.

Entities:  

Keywords:  Brain computer interface; Phase lock value; Seizure monitoring

Year:  2016        PMID: 27647960      PMCID: PMC5020388          DOI: 10.1159/000443567

Source DB:  PubMed          Journal:  Ann Neurosci        ISSN: 0972-7531


  6 in total

1.  Dry and noncontact EEG sensors for mobile brain-computer interfaces.

Authors:  Yu Mike Chi; Yu-Te Wang; Yijun Wang; Christoph Maier; Tzyy-Ping Jung; Gert Cauwenberghs
Journal:  IEEE Trans Neural Syst Rehabil Eng       Date:  2011-12-12       Impact factor: 3.802

2.  Real time workload classification from an ambulatory wireless EEG system using hybrid EEG electrodes.

Authors:  R Matthews; P J Turner; N J McDonald; K Ermolaev; T Manus; R A Shelby; M Steindorf
Journal:  Conf Proc IEEE Eng Med Biol Soc       Date:  2008

3.  Amplitude suppression and chaos control in epileptic EEG signals.

Authors:  Kaushik Majumdar; Mark H Myers
Journal:  Comput Math Methods Med       Date:  2006-03       Impact factor: 2.238

4.  High-frequency electroencephalographic oscillations correlate with outcome of epilepsy surgery.

Authors:  Julia Jacobs; Maeike Zijlmans; Rina Zelmann; Claude-Edouard Chatillon; Jeffrey Hall; André Olivier; François Dubeau; Jean Gotman
Journal:  Ann Neurol       Date:  2010-02       Impact factor: 10.422

5.  High-frequency oscillations and seizure generation in neocortical epilepsy.

Authors:  Greg A Worrell; Landi Parish; Stephen D Cranstoun; Rachel Jonas; Gordon Baltuch; Brian Litt
Journal:  Brain       Date:  2004-05-20       Impact factor: 13.501

6.  Seizure Prediction and Detection via Phase and Amplitude Lock Values.

Authors:  Mark H Myers; Akshay Padmanabha; Gahangir Hossain; Amy L de Jongh Curry; Charles D Blaha
Journal:  Front Hum Neurosci       Date:  2016-03-08       Impact factor: 3.169

  6 in total
  2 in total

1.  Remote and Long-Term Self-Monitoring of Electroencephalographic and Noninvasive Measurable Variables at Home in Patients With Epilepsy (EEG@HOME): Protocol for an Observational Study.

Authors:  Andrea Biondi; Petroula Laiou; Elisa Bruno; Pedro F Viana; Martijn Schreuder; William Hart; Ewan Nurse; Deb K Pal; Mark P Richardson
Journal:  JMIR Res Protoc       Date:  2021-03-19

2.  Dynamic Expression of CX36 Protein in Kainic Acid Kindling induced Epilepsy.

Authors:  Xue-Mei Wu; Guang-Liang Wang; Xiao-Sheng Hao; Jia-Chun Feng
Journal:  Transl Neurosci       Date:  2017-05-11       Impact factor: 1.757

  2 in total

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