This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14x by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 J per feature vector.Index Terms-1/f noise, algorithm design and analysis, amplifiers, biomedical equipment, brain, choppers, digital signal processing, electroencephalography, low-noise amplifiers, low-power electronics.
A 350µW MSK direct modulation transmitter and a 400µW OOK super-regenerative receiver (SRR) are implemented in 90nm CMOS technology. The transceiver tunes 24MHz in frequency steps smaller than 2kHz and is designed to meet the specifications of the Medical Implant Communications Service (MICS) standard in the 402-405MHz band. The transmitter meets MICS mask specifications with data rates up to 120kbps, and the receiver has a sensitivity better than -99dBm with a data rate of 40kbps or -93dBm with a data rate of 120kbps.
Recent advances in the medical field are spurring the need for ultra-low power transceivers for wireless communication with medical implants. To deal with the growing demand for medical telemetry, the FCC commissioned the Medical Implant Communications Services (MICS) standard in 1999 in the 402-405 MHz band. This paper presents a 350 W FSK/MSK direct modulation transmitter and a 400 W OOK super-regenerative receiver (SRR) specifically optimized for medical implant communications. The transceiver is implemented in 90 nm CMOS and digitally tunes 24 MHz in frequency steps smaller than 2 kHz. The transmitter meets MICS mask specifications with data rates up to 120 kb/s consuming only 2.9 nJ/bit; the receiver has a sensitivity better than 99 dBm with a data rate of 40 kb/s or 93 dBm with a data rate of 120 kb/s consuming 3.3 nJ/bit. A frequency correction loop incorporating the base-station is prototyped to eliminate the need for a frequency synthesizer in the implant while still achieving frequency stability of less than 3 ppm.
EIM is a reliable and valid measure of disease severity in DMD. Longitudinal studies comparing EIM with other assessments over time in DMD are warranted.
Objective:
In this longitudinal multicenter cohort study, we evaluated the potential of a dedicated electrical impedance myography (EIM) device to assess ALS progression and the system’s basic reproducibility and diagnostic accuracy.
Methods:
Forty-six ALS patients underwent up to 5 sequential measurements of multiple muscles over a period of 8 months at 2-month intervals using the mView EIM device (Myolex, Inc). Standard measures of disease status were also obtained. A group of 30 healthy volunteers and 30 ALS-mimics were evaluated once to determine if the technique could assist with initial diagnosis. Several electrode arrays and EIM outcomes were assessed.
Results:
EIM tracked ALS progression; power analyses suggested a 5.2-fold reduction in sample size requirements compared to ALSFRS-R by utilizing 50 kHz phase value from the muscle with the greatest EIM decline in each subject. This progression rate correlated to total ALSFRS-R progression, with R=0.371, p=0.021. Reproducibility was high, with both intra- and inter-rater intraclass correlation coefficients for individual muscles mostly greater than 0.90. The mean 50 kHz phase distinguished between ALS patients and healthy controls (area-under-curve 0.78, 95% confidence intervals (CIs) 0.68,0.89), but not between mimics and ALS patients (area-under-curve 0.60, 95% CIs 0.47,0.73).
Conclusions:
While limited in its specificity to identify ALS versus disease mimics, these results support the hypothesis that single-muscle EIM can serve as a convenient, repeatable, and powerful outcome measure in ALS clinical trials.
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