A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy

@article{Iranmanesh2017A9N,
  title={A 950 nW Analog-Based Data Reduction Chip for Wearable EEG Systems in Epilepsy},
  author={Saam Iranmanesh and Esther Rodr{\'i}guez-Villegas},
  journal={IEEE Journal of Solid-State Circuits},
  year={2017},
  volume={52},
  pages={2362-2373}
}
Long-term electroencephalogram (EEG) monitoring is an important tool used for the diagnosis of epilepsy. Truly Wearable EEG can be considered as the future of ambulatory EEG units, which are the current standard for long-term EEG monitoring. Replacing these short lifetime, bulky units with long-lasting miniature and wearable devices which can be easily worn by patients will result in more EEG data being acquired for longer monitoring periods. This paper presents an analog-based data reduction… 
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References

SHOWING 1-10 OF 36 REFERENCES
Wearable Electroencephalography
TLDR
The requirements of portable EEG systems are investigated and the core applications of wearable EEG technology are linked, principally new electrode technology and lower power electronics, and the approach for dealing with the electronic power issues is outlined.
Toward Online Data Reduction for Portable Electroencephalography Systems in Epilepsy
TLDR
A real-time data reduction algorithm based upon the discontinuous recording of the EEG that is formulated to have a direct, low power, hardware implementation and similar data reduction strategies could be employed in a range of body-area-network-type applications.
An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor
An 8-channel scalable EEG acquisition SoC is presented to continuously detect and record patient-specific seizure onset activities from scalp EEG. The SoC integrates 8 high-dynamic range Analog
A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
TLDR
This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients and lowers system power by 14× by reducing the rate of wireless EEG data transmission.
Algorithm for AEEG data selection leading to wireless and long term epilepsy monitoring
TLDR
A brief review of spike detection algorithms is carried out and an algorithm is described in detail and its performance tested and it is found that over 90 % of expert marked spikes are identified whilst giving a 40 % reduction in the amount of data to be transmitted and analysed.
New horizons in ambulatory electroencephalography
  • E. Waterhouse
  • Medicine
    IEEE Engineering in Medicine and Biology Magazine
  • 2003
TLDR
With miniaturization of AEEG and seizure anticipation technology, and advancements in the ability to identify the transition from pre-ictal to ictal state, there is realistic hope that patients with refractory epilepsy may gain control over their seizures and enjoy significantly improved quality of life.
A 60 pW g$_{m}$C Continuous Wavelet Transform Circuit for Portable EEG Systems
This paper presents a low power, low voltage and low frequency bandpass filter implementation of a continuous wavelet transform (CWT) for use with physiological signals in the electroencephalogram
A multistage, multimethod approach for automatic detection and classification of epileptiform EEG
TLDR
A robust system that combines multiple signal-processing methods in a multistage scheme, integrating adaptive filtering, wavelet transform, an artificial neural network, and expert system is proposed that has good performance in detecting epileptiform activities and the multistages multimethod approach is an appropriate way of solving this problem.
A 1.83 µJ/Classification, 8-Channel, Patient-Specific Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine
A non-linear support vector machine (NLSVM) seizure classification SoC with 8-channel EEG data acquisition and storage for epileptic patients is presented. The proposed SoC is the first work in
A low-power, low-noise CMOS amplifier for neural recording applications
  • R. Harrison
  • Engineering
    2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353)
  • 2002
TLDR
A novel bioamplifier that uses a MOS-bipolar pseudo-resistor to amplify signals down to the mHz range while rejecting large dc offsets and it is demonstrated that the VLSI implementation approaches the theoretical noise-power tradeoff limit.
...
...