Heart rate and epileptic seizure detection algorithms for low-power platforms

Ravindran, Sourabh; Cole, Randy

doi:10.1016/j.bspc.2011.11.002

Cited by 2 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…When it comes to designing algorithms for low power platforms, which is constrained by memory and computational limitations [6], simulators play an important role in optimization.First, a dedicated systemforthisintension isbuilttoimprove with respect to the current system limitations, associated to approach of hefty processing, realtime healthcare facility accessibility andproperanalysis.In the simulator, the peak positions are not invariable, but may change from one region to another [7]. This paper presents the procedure to simulate the EEG signal in LabVIEW environment and to detect the presence of healthy EEGvs.…”

Section: Methods and Designmentioning

confidence: 99%

Hardware Simulator for Seizure, Preseizure and Normal Mode Signal Generation in LabVIEW Environment for Research

Generator¹,

Malik²,

Shakir³

et al. 2013

IJBBB

View full text Add to dashboard Cite

Abstract-Currently numerous theories and model have been developed to associate various findings or in relating EEG patterns to develop a software simulator. Here we develop a hardware simulator of the EEG model or to simulator any EEG data set in either .edf or .tdmsot .txtformat from any patient or database depository. The proposed hardware simulator will enhance researchers and hardware validators to simulate, validate and test their detection algorithms forehand, before actual testing the algorithm in the actual standalone hardware. This system make use of signal generator block and then pass this data to the external hardware data acquisition system like the NI-DAQ with an external option to transfer the data wirelessly(Bluetooth, Zigbee, Wi-Fi) or wired (analog port, serial bus etc). This simulator can simulate or generate seizure, pre-seizure and normal EEG waveform. The paired cost effective Arduino microcontroller (in case of wireless system) will be having the algorithm in built in order to classify the type of signal received. This can help in developing wearable EEG Seizure monitoring system(WBAN-HL7). Thispaper will enhance the purpose of developing a system which can alert locally in a form of wearable gadget, whenever a pre-seizure occurs. This can help the epileptic patient or the user to take precautionary action to save themselves from accidents or injury, just before the occurrence of the seizure. Useable of this embedded wearable version can ensure a better everyday activities and the psychological stress can be reduces to leverage the social interaction.

show abstract

Section: Methods and Designmentioning

confidence: 99%