Key result
An independent component analysis accelerator decreased the error of HRV extraction from 5.94% to 1.84% in a 259.6 μW chaos-processor for mental task monitoring.
Absolute Event Rate: 1.84% vs 5.94%
A novel low-power HRV-EEG processor with hardware acceleration for nonlinear chaotic analysis demonstrates improved accuracy for mental task monitoring.
May enable low-power wearable HRV monitoring; leaves open clinical validation in patients.
A system-on-chip (SoC) with nonlinear chaotic analysis (NCA) is presented for mental task monitoring. The proposed processor treats both heart rate variability (HRV) and electroencephalography (EEG). An independent component analysis (ICA) accelerator decreases the error of HRV extraction from 5.94% to 1.84% in the preprocessing step. Largest Lyapunov exponents (LLE), as well as linear features such as mean and standard variation and sub-band power, are calculated with NCA acceleration. Measurements with mental task protocols result in confidence level of 95%. Thanks to the hardware acceleration, the chaos-processor fabricated in 0.13 μm CMOS technology consumes only 259.6 μW.
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Roh et al. (2015) studied Mental task monitoring. System-on-chip (SoC) with nonlinear chaotic analysis (NCA) vs. Without independent component analysis (ICA) accelerator was evaluated on HRV extraction error. An independent component analysis accelerator decreased the error of HRV extraction from 5.94% to 1.84% in a 259.6 μW chaos-processor for mental task monitoring.
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