An Ultralow-Power Sleep Spindle Detection System on Chip

@article{Iranmanesh2017AnUS,
  title={An Ultralow-Power Sleep Spindle Detection System on Chip},
  author={Saam Iranmanesh and Esther Rodr{\'i}guez-Villegas},
  journal={IEEE Transactions on Biomedical Circuits and Systems},
  year={2017},
  volume={11},
  pages={858-866}
}
This paper describes a full system-on-chip to automatically detect sleep spindle events from scalp EEG signals. These events, which are known to play an important role on memory consolidation during sleep, are also characteristic of a number of neurological diseases. The operation of the system is based on a previously reported algorithm, which used the Teager energy operator, together with the Spectral Edge Frequency (SEF50) achieving more than 70% sensitivity and 98% specificity. The… 
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References

SHOWING 1-10 OF 29 REFERENCES
An Ultralow Power System on Chip for Automatic Sleep Staging
This paper presents an ultralow power system on chip (SoC) for automatic sleep staging using a single electroencephalogram (EEG) channel. The system integrates an analog front end for EEG data
Evaluating the use of line length for automatic sleep spindle detection
TLDR
The overall detection performance, combined with the low power consumption show that line length could be a useful feature for detecting sleep spindles in wearable and resource-constrained systems.
Automatic detection of sleep spindles using Teager energy and spectral edge frequency
TLDR
A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input and it is shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.
A Low Computational Cost Algorithm for REM Sleep Detection Using Single Channel EEG
TLDR
A novel feature in sleep EEG that is based on spectral edge frequency in the 8–16 Hz frequency band is investigated and it is demonstrated that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.
An Automatic Sleep Spindle Detector based on WT, STFT and WMSD
TLDR
Three different approaches are used for the automatic detection of sleep spindles: Short Time Fourier Transform, Wavelet Transform and Wave Morphology for Spindle Detection and a combination of the three algorithms resulted in a sensitivity and specificity of 94% when compared to human expert scorers.
A new method for the quantitative analysis of sleep spindles during continuous overnight EEG recordings.
Sleep spindles detection using short time Fourier transform and neural networks
TLDR
A method that detects the sleep spindles in sleep EEG is proposed and Short time Fourier transform is used for feature extraction and the results obtained are quite satisfactory.
An Ultra Low-Power CMOS Automatic Action Potential Detector
  • B. Gosselin, M. Sawan
  • Computer Science
    IEEE Transactions on Neural Systems and Rehabilitation Engineering
  • 2009
TLDR
A low-power complementary metal-oxide semiconductor analog integrated biopotential detector intended for neural recording in wireless multichannel implants can achieve accurate automatic discrimination of action potential (APs) from the background activity by means of an energy-based preprocessor and a linear delay element.
Local field potential measurement with low-power analog integrated circuit
TLDR
A low-power, fully-integrated circuit that performs on-site data reduction by isolating LFPs and measuring their signal energy is presented and it is shown that the chip performs similarly to state-of-the-art signal processing algorithms.
A micro-power neural spike detector and feature extractor in .13μm CMOS
TLDR
A fully-integrated system for the detection and characterization of action potentials observed in extracellular neural recordings and an analog implementation of the nonlinear energy operator for spike detection is presented.
...
...