Compressed sensing based seizure detection for an ultra low power multi-core architecture

Extracting information from brain signals in advanced Brain Machine Interfaces (BMI) often requires computationally demanding processing. The complexity of the algorithms traditionally employed to process multi-channel neural data, such as Principal Component Analysis (PCA), dramatically increases while scaling-up the number of channels and requires more power-hungry computational platforms. This could hinder the development of low-cost and low-power interfaces which can be used in wearable or implantable real-Time systems. This work proposes a new algorithm for the detection of epileptic seizure based on compressively sensed EEG information, and its optimization on a low-power multi-core SoC for near-sensor data analytics: Mr. Wolf. With respect to traditional algorithms based on PCA, the proposed approach reduces the computational complexity by 4.4x in ARM Cortex M4-based MCU. Implementing this algorithm on Mr.Wolf platform allows to detect a seizure with 1 ms of latency after acquiring the EEG data for 1 s, within an energy budget of 18.4 μJ. A comparison with the same algorithm on a commercial MCU shows an improvement of 6.9x in performance and up to 18.4x in terms of energy efficiency

Multigigabit programmable comb decimator implemented in GaAs/AlGaAs HEMT technology

The architecture and design of a GaAs multi-GHz two-stage programmable decimator are presented. A transistor-level realisation of the first stage( the comb decimator) and the cell count of the second stage decimator in a 0.3 um GaAs/AlGaAs HEMT E/D process are considered. The performance has been calculated through measurements made on two 12-bit adders using SDCFL and DCFL gates. An alternating carry state technique allows a speed of 2GHz to be obtained with 2.2W power dissipation from the comb decimator; the transistor count is 4525

Investigation of ECG Changes in Absence Epilepsy on WAG/ Rij Rats

Introduction: Seizures are symptoms associated with abnormal electrical activity in electroencephalogram (EEG). The present study was designed to determine the effect of absence seizure on heart rate (HR) changes in electrocardiogram (ECG). Methods: HR alterations were recorded simultaneous with spike and wave discharges (SWD) by EEG in 6 WAG/Rij rats as a well characterized and validated genetic animal epilepsy model. Moreover, 6 control rats were used to distinguish the differences of HR changes between various groups. Electrodes were placed on the skull and under the chest skin, minimizing time delay and signal attenuation. HR was calculated by an adaptable algorithm based on continues wavelet transform (CWT) particular for this study. Three main features of HR minimum, maximum, and mean values were estimated for pre-ictal and ictal intervals for all seizures. Results: ECG beats detected with sensitivity of 99.9% and positive predictability of 99.8% based on CWT. HR deceleration was found in 86% of the seizures. There were statistically significant (P<0.001) reductions of these values from pre-ictal to ictal intervals. Interictal HR acceleration and ictal deceleration were the major feature of alterations and in 23% of seizures, this decrease had priority to the onsets. Discussion: These findings may lead to design a seizure alarm system based on HR and to obtain new insights about sudden unexpected death in epilepsy (SUDEP) phenomenon and side-effects of antiepileptic drugs (AED)