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The recording of biopotential signals using techniques such as electroencephalography (EEG) and electrocardiography (ECG) poses important challenges to the design of the front-end readout circuits in terms of noise, electrode DC offset cancellation and motion artifact tolerance. In this paper, we present a 2nd-order hybrid-CTDT Δ∑-∑ modulator front-end architecture that tackles these challenges by taking advantage of the over-sampling and noise-shaping characteristics of a traditional Δ∑ modulator, while employing an extra ∑-stage in the feedback loop to remove electrode DC offsets and accommodate motion artifacts. To meet the stringent noise requirements of this application, a capacitively-coupled chopper-stabilized amplifier located in the forward path of the modulator loop serves simultaneously as an input stage and an active adder. A prototype of this direct-to-digital front-end chip is fabricated in a standard 0.18-μm CMOS process and achieves a peak SNR of 105.6 dB and a dynamic range of 108.3 dB, for a maximum input range of 720 mVpp. The measured input-referred noise is 0.98 μVrms over a bandwidth of 0.5–100 Hz, and the measured CMRR is >100 dB. ECG and EEG measurements in human subjects demonstrate the capability of this architecture to acquire biopotential signals in the presence of large motion artifacts. [ABSTRACT FROM AUTHOR]

The imbalance of autonomic nervous system (ANS) has a close relationship to cardiac mortality. The noninvasive assessment of ANS is of great importance and remains challenging. A new method, characterizing the consistency of short-term nonlinear indexes of heart rate variability (HRV) and QT variability (QTV), is proposed and validated. Holter records from two databases in Telemetric and Holter ECG Warehouse were used, 43 records from one database (named ESRD) as typical subjects of ANS dysfunction and 118 records from the other database (named Normal) as normal controls. The consistency of HRV and QTV was characterized by estimating mutual information (MI) of paired short-term recurrence quantification analysis (RQA) indexes in resting state. The influence of physiological differences on MIs of paired RQA indexes in Normal was investigated as well. Results showed that there were significant differences in MI-DET (day: 0.283 ± 0.070 versus 0.133 ± 0.055 and night: 0.258 ± 0.061 versus 0.117 ± 0.055) and MI-LAM (day: 0.439 ± 0.053 versus 0.293 ± 0.073 and night: 0.361 ± 0.079 versus 0.241 ± 0.087) between Normal and ESRD, much reduced consistency in ESRD. For MI-DET in Normal, sex had no influence, and there was age related alternations by day but not at night. There was no influence of sex and age on MI-LAM in Normal. The sensitivity, specificity, and total accuracy for discriminating Normal and ESRD were 88.37%, 95.76%, and 93.79%, respectively. The proposed measures are shown to have the advantage in reducing the influence of physiological differences and highlighting the pathological influence, providing a promising method to find clinical application for noninvasive assessment of ANS state. [ABSTRACT FROM PUBLISHER]

The resistive random access memory (RRAM) crossbar array has been extensively studied as one of the most promising candidates for future high-density nonvolatile memory technology. However, some problems caused by circuit and device interaction, such as sneak leakage paths, result in limited array size and large power consumption, which degrade the array performance significantly. Thus, the analysis on circuit and device interaction issue is imperative. In this paper, a simulation method is developed to investigate the critical issues correlated with the interaction between devices and the circuit. The simulations show that a large off/on ratio of resistance states of RRAM is beneficial for large readout margin (i.e., array size). The existence of the selector connected in series with an RRAM device can eliminate the need for high Ron resistance, which is critical for the array consisted of only RRAM cells. The readout margin is more sensitive to the variation of Ron and is determined by the nonlinearity of the I–V characteristics of RRAM, whereas the nonlinear characteristics of the selector device are beneficial for a larger readout margin. An optimal design scheme for turn-on voltage and conductance of the selector is proposed based on the simulation. [ABSTRACT FROM PUBLISHER]

The recording of biopotential signals using techniques such as electroencephalography (EEG) and electrocardiography (ECG) poses important challenges to the design of the front-end readout circuits in terms of noise, electrode DC offset cancellation and motion artifact tolerance. In this paper, we present a 2 nd -order hybrid-CTDT Δ∑-∑ modulator front-end architecture that tackles these challenges by taking advantage of the over-sampling and noise-shaping characteristics of a traditional Δ∑ modulator, while employing an extra ∑-stage in the feedback loop to remove electrode DC offsets and accommodate motion artifacts. To meet the stringent noise requirements of this application, a capacitively-coupled chopper-stabilized amplifier located in the forward path of the modulator loop serves simultaneously as an input stage and an active adder. A prototype of this direct-to-digital front-end chip is fabricated in a standard 0.18-μm CMOS process and achieves a peak SNR of 105.6 dB and a dynamic range of 108.3 dB, for a maximum input range of 720 mV pp . The measured input-referred noise is 0.98 μV rms over a bandwidth of 0.5-100 Hz, and the measured CMRR is >100 dB. ECG and EEG measurements in human subjects demonstrate the capability of this architecture to acquire biopotential signals in the presence of large motion artifacts.