Impedance spectroscopy is a low-cost sensing technique that is generating considerable interest in wearable and implantable biomedical applications since it can be efficiently integrated on a single microchip. In this paper, the fundamental characteristics of the most well-known system architectures are presented, and a more robust and hardware-efficient solution is proposed. An all-digital implementation based on adaptive filtering is used for identifying the impedance parameters of a sampleunder-test. The coefficients of an infinite-impulse-response (IIR) filter are tuned by an adaptive algorithm based on pseudo-linear regression and output-error formulation. A three-level pseudorandom noise generator with a concave power spectral density is employed without deteriorating the nominal performance. Proofof-concept has been verified with behavioral simulations.
This is the accepted version of a paper published in IEEE Transactions on Biomedical Circuits and Systems. This paper has been peer-reviewed but does not include the final publisher proof-corrections or journal pagination.
Abstract-This paper presents a delta-sigma based readout architecture targeting electrocortical recording in brain stimulation applications. The proposed architecture can accurately record a peak input signal up to 240 mV in a power-efficient manner without saturating or employing offset rejection techniques. The readout architecture consists of a delta-sigma modulator with an embedded analog front-end. The proposed architecture achieves a total harmonic distortion of -95 dB by employing a current-steering DAC and a multi-bit quantizer implemented as a tracking ADC. A system prototype is implemented in a 0.18 µm CMOS triple-well process and has a total power consumption of 54 µW. Measurement results, across ten packaged samples, show approximately 14-ENOB over a 300 Hz bandwidth with an input referred noise of 5.23 µVrms, power-supply/common-mode rejection ratio of 100 dB/98 dB and an input impedance larger than 94 MΩ.
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