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Neuromodulation of the immune system has been proposed as a novel therapeutic strategy for the treatment of inflammatory conditions. We recently demonstrated that stimulation of near-organ autonomic nerves to the spleen can be harnessed to modulate the inflammatory response in an anesthetized pig model. The development of neuromodulation therapy for the clinic requires chronic efficacy and safety testing in a large animal model. This manuscript describes the effects of longitudinal conscious splenic nerve neuromodulation in chronically-implanted pigs. Firstly, clinically-relevant stimulation parameters were refined to efficiently activate the splenic nerve while reducing changes in cardiovascular parameters. Subsequently, pigs were implanted with a circumferential cuff electrode around the splenic neurovascular bundle connected to an implantable pulse generator, using a minimally-invasive laparoscopic procedure. Tolerability of stimulation was demonstrated in freely-behaving pigs using the refined stimulation parameters. Longitudinal stimulation significantly reduced circulating tumor necrosis factor alpha levels induced by systemic endotoxemia. This effect was accompanied by reduced peripheral monocytopenia as well as a lower systemic accumulation of CD16+CD14highpro-inflammatory monocytes. Further, lipid mediator profiling analysis demonstrated an increased concentration of specialized pro-resolving mediators in peripheral plasma of stimulated animals, with a concomitant reduction of pro-inflammatory eicosanoids including prostaglandins. Terminal electrophysiological and physiological measurements and histopathological assessment demonstrated integrity of the splenic nerves up to 70 days post implantation. These chronic translational experiments demonstrate that daily splenic nerve neuromodulation,viaimplanted electronics and clinically-relevant stimulation parameters, is well tolerated and is able to prime the immune system toward a less inflammatory, pro-resolving phenotype.

Neuromodulation is a new therapeutic pathway to treat inflammatory conditions by modulating the electrical signalling pattern of the autonomic connections to the spleen. However, targeting this sub-division of the nervous system presents specific challenges in translating nerve stimulation parameters. Firstly, autonomic nerves are typically embedded non-uniformly among visceral and connective tissues with complex interfacing requirements. Secondly, these nerves contain axons with populations of varying phenotypes leading to complexities for axon engagement and activation. Thirdly, clinical translational of methodologies attained using preclinical animal models are limited due to heterogeneity of the intra- and inter-species comparative anatomy and physiology. Here we demonstrate how this can be accomplished by the use of in silico modelling of target anatomy, and validation of these estimations through ex vivo human tissue electrophysiology studies. Neuroelectrical models are developed to address the challenges in translation of parameters, which provides strong input criteria for device design and dose selection prior to a first-in-human trial., Communications Biology, 3 (1), ISSN:2399-3642

This paper presents a digital active electrode (DAE) system for multi-parameter biopotential signal acquisition in portable and wearable devices. It is built around an IC that performs analog signal processing and digitization with the help of on-chip instrumentation amplifiers, a 12 bit ADC and a digital interface. Via a standard ${\rm I}^{{2}}{\rm C}$ bus, up to 16 digital active electrodes (15-channels) can be connected to a commercially available microcontroller, thus significantly reducing system complexity and cost. In addition, the DAE utilizes an innovative functionally DC-coupled amplifier to preserve input DC signal, while still achieving state-of-the-art performance: 60 nV/sqrt(Hz) input-referred noise and ${\pm} $ 350 mV electrode-offset tolerance. A common-mode feedforward scheme improves the CMRR of an AE pair from 40 dB to maximum 102 dB.

A complete light weight 24 channel wireless neural recording system enabled by a custom IC is presented. By transmitting only the action potential (AP) data, the system power consumption is scaled based on the activity of the neural signal. By detecting an AP event prior to digitization, the ASIC can reduce the power consumed in all subsequent stages and yet conserves the entire AP shape without affecting the classification performance. The complete 2.5 cm3 head stage weighing below 7 g can operate for 3 h. The ASIC achieves 70 dB CMRR.

We present a high electrode density and high channel count CMOS (complementary metal-oxide-semiconductor) active neural probe containing 1344 neuron sized recording pixels (20 µm × 20 µm) and 12 reference pixels (20 µm × 80 µm), densely packed on a 50 µm thick, 100 µm wide, and 8 mm long shank. The active electrodes or pixels consist of dedicated in-situ circuits for signal source amplification, which are directly located under each electrode. The probe supports the simultaneous recording of all 1356 electrodes with sufficient signal to noise ratio for typical neuroscience applications. For enhanced performance, further noise reduction can be achieved while using half of the electrodes (678). Both of these numbers considerably surpass the state-of-the art active neural probes in both electrode count and number of recording channels. The measured input referred noise in the action potential band is 12.4 µVrms, while using 678 electrodes, with just 3 µW power dissipation per pixel and 45 µW per read-out channel (including data transmission). ispartof: Sensors vol:17 issue:10 ispartof: location:Switzerland status: published

In vivo recording of neural action-potential and local-field-potential signals requires the use of high-resolution penetrating probes. Several international initiatives to better understand the brain are driving technology efforts towards maximizing the number of recording sites while minimizing the neural probe dimensions. We designed and fabricated (0.13- $\mu$ m SOI Al CMOS) a 384-channel configurable neural probe for large-scale in vivo recording of neural signals. Up to 966 selectable active electrodes were integrated along an implantable shank (70 $\mu$ m wide, 10 mm long, 20 $\mu$ m thick), achieving a crosstalk of $-\text{64.4}$ dB. The probe base (5 $\times$ 9 mm $^2$ ) implements dual-band recording and a 171.6 Mbps digital interface. Measurement results show a total input-referred noise of 6.4 $\mu$ V $_{\mathrm{rms}}$ and a total power consumption of 49.1 $\mu$ W/channel.

Active electrodes (AEs), i.e., electrodes with built-in readout circuitry, are increasingly being implemented in wearable healthcare and lifestyle applications due to AEs' robustness to environmental interference. An AE locally amplifies and buffers μV-level EEG signals before driving any cabling. The low output impedance of an AE mitigates cable motion artifacts, thus enabling the use of high-impedance dry electrodes for greater user comfort. However, developing a wearable EEG system, with medical grade signal quality on noise, electrode offset tolerance, common-mode rejection ratio, input impedance, and power dissipation, remains a challenging task. This paper reviews state-of-the-art bio-amplifier architectures and low-power analog circuits design techniques intended for wearable EEG acquisition, with a special focus on an AE system interfaced with dry electrodes.

A compressive sampling (CS) photoplethysmographic (PPG) readout with embedded feature extraction to estimate heart rate (HR) directly from compressively sampled data is presented. It integrates a low-power analog front end together with a digital back end to perform feature extraction to estimate the average HR over a 4 s interval directly from compressively sampled PPG data. The application-specified integrated circuit (ASIC) supports uniform sampling mode (1x compression) as well as CS modes with compression ratios of 8x, 10x, and 30x. CS is performed through nonuniformly subsampling the PPG signal, while feature extraction is performed using least square spectral fitting through Lomb-Scargle periodogram. The ASIC consumes 172 μ W of power from a 1.2 V supply while reducing the relative LED driver power consumption by up to 30 times without significant loss of relevant information for accurate HR estimation. ispartof: IEEE Transactions on Biomedical Circuits and Systems vol:11 issue:3 pages:487-496 ispartof: location:CA, San Francisco status: published

Summary This paper presents a methodology to design reconfigurable switched-capacitor delta-sigma modulators (ΔΣMs) capable of keeping their corresponding power efficiency figures constant and optimal for a set of resolutions and signal bandwidths. This method is especially suitable for low-bandwidth, medium-to-high-resolution specifications, which are common in biomedical application range. The presented methodology is based on an analytic model of all different contributions to the power dissipation of the ΔΣM. In particular, a novel way to predict the static power dissipated by integrators based on class A and class AB operational transconductance amplifier is presented. The power-optimal solution is found in terms of filter order, quantizer resolution, oversampling ratio, and capacitor dimensions for a targeted resolution and bandwidth. As the size of the sampling capacitors is crucial to determine power consumption, three approaches to achieve reconfigurability are compared: sizing the sampling capacitors to achieve the highest resolution and keep them constant, change only the first sampling capacitor according to the targeted resolution, or program all sampling capacitors to the required resolution. The second approach results in the best trade-off between power efficiency and simplicity. A reconfigurable ΔΣM for biomedical applications is designed at transistor level in a 0.18-µm complementary metal–oxide–semiconductor process following the methodology discussed. A comparison between the power estimated by the proposed analytic model and the transistor implementation shows a maximum difference of 17%, validating thus the proposed approach. Copyright © 2014 John Wiley & Sons, Ltd.

We present a high density CMOS neural probe with\ud active electrodes (pixels), consisting of dedicated in-situ circuits\ud for signal source amplification. The complete probe contains 1356\ud neuron size (20x20 μm2) pixels densely packed on a 50 μm thick,\ud 100 μm wide and 8 mm long shank. It allows simultaneous highperformance\ud recording from 678 electrodes and a possibility to\ud simultaneously observe all of the 1356 electrodes with increased\ud noise. This considerably surpasses the state of the art active neural\ud probes in electrode count and flexibility. The measured action\ud potential band noise is 12.4 μVrms, with just 3 μW power\ud dissipation per electrode amplifier and 45 μW per channel\ud (including data transmission).

A time-domain-based amplifier concept is proposed to obtain high-voltage gains with low power consumption and at an ultra-low supply voltage of 0.35 V. A prototype instrumentation amplifier designed using the proposed technique in the 180 nm technology consumes 210 nW of power and 0.1 mm 2 of the active area.

An ECG signal processing method with quad level vector (QLV) is proposed for the ECG holter system. The ECG processing consists of the compression flow and the classification flow, and the QLV is proposed for both flows to achieve better performance with low-computation complexity. The compression algorithm is performed by using ECG skeleton and the Huffman coding. Unit block size optimization, adaptive threshold adjustment, and 4-bit-wise Huffman coding methods are applied to reduce the processing cost while maintaining the signal quality. The heartbeat segmentation and the R-peak detection methods are employed for the classification algorithm. The performance is evaluated by using the Massachusetts Institute of Technology-Boston's Beth Israel Hospital Arrhythmia Database, and the noise robust test is also performed for the reliability of the algorithm. Its average compression ratio is 16.9:1 with 0.641% percentage root mean square difference value and the encoding rate is 6.4 kbps. The accuracy performance of the R-peak detection is 100% without noise and 95.63% at the worst case with -10-dB SNR noise. The overall processing cost is reduced by 45.3% with the proposed compression techniques.

Thermoelectric generators worn on a person’s body have demonstrated their capability to power a variety of wireless sensor nodes that are to improve his/her health or comfort. In this article, the design, fabrication and performance of two prototypes of a battery-free wireless 2-channel electroencephalography (EEG) system are presented. The first system is powered solely by a thermoelectric generator that produces 2-2.5mW of power and is worn as a headband. The second system resembles a diadem or headphones and uses a hybrid power supply that combines a thermoelectric generator and photovoltaic cells in one device. This portable EEG headset considerably improves the comfort of patients in clinical as well as in non-clinical environments and opens perspectives for a new range of non-clinical applications.

There is a growing demand for low-power, small-size and ambulatory biopotential acquisition systems. A crucial and important block of this acquisition system is the analog readout front-end. We have implemented a low-power and low-noise readout front-end with configurable characteristics for Electroencephalogram (EEG), Electrocardiogram (ECG), and Electromyogram (EMG) signals. Key to its performance is the new AC-coupled chopped instrumentation amplifier (ACCIA), which uses a low power current feedback instrumentation amplifier (U). Thus, while chopping filters the 1/f noise of CMOS transistors and increases the CMRR, AC coupling is capable of rejecting differential electrode offset (DEO) up to +/- 50 mV from conventional Ag/AgCl electrodes. The ACCIA achieves 120 dB CMRR and 57 nV/root Hz input-referred voltage noise density, while consuming 11.1 mu A from a 3 V supply. The chopping spike filter (CSF) stage filters the chopping spikes generated by the input chopper of ACCIA and the digitally controllable variable gain stage is used to set the gain and the bandwidth of the front-end. The front-end is implemented in a 0.5 mu m CMOS process. Total current consumption is 20 mu A from 3V. ispartof: IEEE Journal of Solid-State Circuits vol:42 issue:5 pages:1100-1110 status: published

This paper describes an 8-channel gel-free EEG/electrode-tissue impedance (ETI) acquisition system, consisting of nine active electrodes (AEs) and one back-end (BE) analog signal processor. The AEs amplify the weak EEG signals, while their low output impedance suppresses cable-motion artifacts and 50/60 Hz mains interference. A common-mode feed-forward (CMFF) scheme boosts the CMRR of the AE pairs by 25 dB. The BE post-processes and digitizes the analog outputs of the AEs, it also can configure them via a single-wire pulse width modulation (PWM) protocol. Together, the AEs and BE are capable of recording 8-channel EEG and ETI signals. With EEG recording enabled, ETIs of up to 60 kΩ can be measured, which increases to 550 kΩ when EEG recording is disabled. Each EEG channel has a 1.2 GΩ input impedance (at 20 Hz), 1.75 μVrms (0.5-100 Hz) input-referred noise, 84 dB CMRR and ±250 mV electrode offset rejection capability. The EEG acquisition system was implemented in a standard 0.18 μm CMOS process, and dissipates less than 700 μW from a 1.8 V supply.

This paper presents multi-electrode arrays for in vivo neural recording applications incorporating the principle of electronic depth control (EDC), i.e., the electronic selection of recording sites along slender probe shafts independently for multiple channels. Two-dimensional (2D) arrays were realized using a commercial 0.5- μm complementary-metal-oxide-semiconductor (CMOS) process for the EDC circuits combined with post-CMOS micromachining to pattern the comb-like probes and the corresponding electrode metallization. A dedicated CMOS integrated front-end circuit was developed for pre-amplification and multiplexing of the neural signals recorded using these probes.

This paper presents an active electrode system for gel-free biopotential EEG signal acquisition. The system consists of front-end chopper amplifiers and a back-end common-mode feedback (CMFB) circuit. The front-end AC-coupled chopper amplifier employs input impedance boosting and digitally-assisted offset trimming. The former increases the input impedance of the active electrode to $2~{rm G}Omega$ at 1 Hz and the latter limits the chopping induced output ripple and residual offset to 2 mV and 20 mV, respectively. Thanks to chopper stabilization, the active electrode achieves $0.8~mu {rm Vrms}~(0.5-100~{rm Hz})$ input referred noise. The use of a back-end CMFB circuit further improves the CMRR of the active electrode readout to 82 dB at 50 Hz. Both front-end and back-end circuits are implemented in a $0.18~mu{rm m}$ CMOS process and the total current consumption of an 8-channel readout system is $88~mu {rm A}$ from 1.8 V supply. EEG measurements using the proposed active electrode system demonstrate its benefits compared to passive electrode systems, namely reduced sensitivity to cable motion artifacts and mains interference.

Multi-electrode arrays for in vivo neural recording are presented incorporating the principle of electronic depth control, i.e. an electronic selection of electrode locations along the probe shaft independently for multiple channels. Two-dimensional (2D) arrays are realized using a commercial CMOS process for the electronic circuits combined with post-CMOS micromachining for shaping the probes and electrode metallization. These 2D arrays can be further assembled into 3D arrays. Two-dimensional arrays with IrO x metal finish show electrode impedances between 100 KΩ and 1 MΩ. In vivo tests demonstrate the capability to simultaneously record multi-unit activity in addition to local field potentials on all 32 available output channels of the probe combs. Electronic steering enabled some of the electrodes to record from cortical and others to record from thalamic sites in the rat. This new device significantly increases the amount of useful information that can be obtained from a single experiment.

This paper gives an overview of results of the Human++ research program [1]. This research aims to achieve highly miniaturized and autonomous transducer systems that assist our health and comfort. It combines expertise in wireless ultra-low power communications, 3D integration technologies, MEMS energy scavenging techniques and low-power design techniques.

In this paper, we present a new bio-impedance monitor for wearable and continuous monitoring applications. The system consumes less than 14.4mW when measuring impedance, and 0.9mW when idling. Its compact size (4.8cm × 3cm × 2cm) makes it suitable for portable and wearable use. The proposed system has an accuracy of 0.5Ω and resolution of 0.2Ω on both resistance (R) and reactance (X) measurements, for impedance ranging between (j0.7)Ω to (54+j5)Ω with 2.9<

The first integrated CMOS multichannel electroencephalogram (EEG) readout front-end, capable of extracting EEG signals from conventional AgCl electrodes without the use of any trimmed components, is presented. Each channel of the ASIC achieves better than 90 dB common-mode rejection ratio (CMRR) at 50 Hz with 50 mV DC offset between EEG electrodes. This is consistent with the presented formula describing the CMRR behaviour of the preamplifier under DC electrode offset. The front-end includes 24 channels and consumes 10.5 mW from a 3 V supply.