Dataset Information

An Ultra-Low Power Smart Headband for Real-Time Epileptic Seizure Detection.

ABSTRACT: In this paper, the design of a smart headband for epileptic seizure detection is presented. The proposed headband consists of four key components: 1) an analog front-end circuitry; 2) an epileptic seizure detection tag (ESDT); 3) a Bluetooth low-power chip; and 4) customized electrodes. All the above components are integrated into a fabric headband with only 50.3 g. The smart headband system dissipates 55.89 mW. The epileptic seizure detection algorithm inside ESDT is validated by using Boston Children's Hospital's CHB-MIT scalp EEG clinical database with the detection rate of 92.68% and the false alarm of 0.527/h. We develop a service APP connected to the cloud so that the patients' health condition can be recorded and then referenced by doctors for further diagnosis or research.

SUBMITTER: Lin SK

PROVIDER: S-EPMC6147694 | biostudies-other | 2018

REPOSITORIES: biostudies-other

ACCESS DATA

Publications

An Ultra-Low Power Smart Headband for Real-Time Epileptic Seizure Detection.

Lin Shih-Kai SK Istiqomah Wang Li-Chun LC Lin Chin-Yew CY Chiueh Herming H

IEEE journal of translational engineering in health and medicine 20180817

In this paper, the design of a smart headband for epileptic seizure detection is presented. The proposed headband consists of four key components: 1) an analog front-end circuitry; 2) an epileptic seizure detection tag (ESDT); 3) a Bluetooth low-power chip; and 4) customized electrodes. All the above components are integrated into a fabric headband with only 50.3 g. The smart headband system dissipates 55.89 mW. The epileptic seizure detection algorithm inside ESDT is validated by using Boston C ...[more]

PMID: 30245945

Similar Datasets

Project description:Epilepsy is a common neurological disorder which affects 0.5-1% of the world population. Its diagnosis relies both on Electroencephalogram (EEG) findings and characteristic seizure-induced body movements--called seizure semiology. Thus, synchronous EEG and (2D)video recording systems (known as Video-EEG) are the most accurate tools for epilepsy diagnosis. Despite the establishment of several quantitative methods for EEG analysis, seizure semiology is still analyzed by visual inspection, based on epileptologists' subjective interpretation of the movements of interest (MOIs) that occur during recorded seizures. In this contribution, we present NeuroKinect, a low-cost, easy to setup and operate solution for a novel 3Dvideo-EEG system. It is based on a RGB-D sensor (Microsoft Kinect camera) and performs 24/7 monitoring of an Epilepsy Monitoring Unit (EMU) bed. It does not require the attachment of any reflectors or sensors to the patient's body and has a very low maintenance load. To evaluate its performance and usability, we mounted a state-of-the-art 6-camera motion-capture system and our low-cost solution over the same EMU bed. A comparative study of seizure-simulated MOIs showed an average correlation of the resulting 3D motion trajectories of 84.2%. Then, we used our system on the routine of an EMU and collected 9 different seizures where we could perform 3D kinematic analysis of 42 MOIs arising from the temporal (TLE) (n = 19) and extratemporal (ETE) brain regions (n = 23). The obtained results showed that movement displacement and movement extent discriminated both seizure MOI groups with statistically significant levels (mean = 0.15 m vs. 0.44 m, p<0.001; mean = 0.068 m(3) vs. 0.14 m(3), p<0.05, respectively). Furthermore, TLE MOIs were significantly shorter than ETE (mean = 23 seconds vs 35 seconds, p<0.01) and presented higher jerking levels (mean = 345 ms(-3) vs 172 ms(-3), p<0.05). Our newly implemented 3D approach is faster by 87.5% in extracting body motion trajectories when compared to a 2D frame by frame tracking procedure. We conclude that this new approach provides a more comfortable (both for patients and clinical professionals), simpler, faster and lower-cost procedure than previous approaches, therefore providing a reliable tool to quantitatively analyze MOI patterns of epileptic seizures in the routine of EMUs around the world. We hope this study encourages other EMUs to adopt similar approaches so that more quantitative information is used to improve epilepsy diagnosis.

Dataset Information

An Ultra-Low Power Smart Headband for Real-Time Epileptic Seizure Detection.

Publications

An Ultra-Low Power Smart Headband for Real-Time Epileptic Seizure Detection.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets