Low-Energy Two-Stage Algorithm for High Efficacy Epileptic Seizure Detection

Markandeya, Himanshu S.; Irazoqui, Pedro P.; Roy, Kaushik

doi:10.1109/tvlsi.2014.2302798

Cited by 6 publications

(3 citation statements)

References 17 publications

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“…Epilepsy is characterized by recurrent temporary electrical disturbances in the brain known as seizures [2]. Approximately 30% of patients remain medicament refractory in spite of available therapies [3]. Using implantable and wearable seizure control devices are considered as a promising and effective alternative.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, a sophisticated algorithm is rather power hungry, which must be avoided in an implantable module due the expensive and risk-prone battery replacement issue. Furthermore, it is mandatory to maintain the power consumption of the implant within a minimum range to prevent an undesirable temperature elevation of the neural tissues which can cause detrimental tissue damages [3]. *Research supported by Swiss national science foundation (SNSF).…”

Section: Introductionmentioning

confidence: 99%

“…Three time-domain features used in this work are the coastline, energy and nonlinear energy features. These features have been widely used in the literature of epileptic seizure detection [3,7,9]. An 8-bit feature extractor engine calculates these three features in a two-stage architecture.…”

Section: Introductionmentioning

confidence: 99%

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Two-stage Hardware-Friendly Epileptic Seizure Detection Method with a Dynamic Feature Selection

Razi¹,

Schmid²

2021

2021 43rd Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

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A novel low-complexity method of detecting epileptic seizures from intracranial encephalography (iEEG) signals is presented. In the proposed algorithm, coastline, energy and nonlinear energy features of iEEG signals are extracted in a patient-specific two-stage seizure detection system. The detection stage of the proposed system, which extracts two times more features than the monitoring stage, is only powered on when the monitoring stage detects a seizure occurrence. A new metric is defined to demonstrate the significance of the two-stage architecture and show the time duration over which the detection stage is activated. The new parameter is called detection stage activation ratio (DAR) and it is equal to 0.272 in this work. In addition, the proposed seizure detector outperforms other algorithms which utilize a single feature or multiple features continuously in terms of sensitivity, specificity and DAR. Therefore, it is highly suitable for seizure detector implants in which reducing the power consumption is a critical factor to increase the lifetime of the implanted battery. The algorithm is implemented on a Cyclone V FPGA and has a low dynamic power of 1 𝛍W when tested on human iEEG signals of six patients from the Bern Inselspital dataset. It reaches a perfect sensitivity of 100% tested on 120 hours of iEEG data containing 24 seizure periods of six patients.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%