Splenic Nerve Neuromodulation Reduces Inflammation and Promotes Resolution in Chronically Implanted Pigs.

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+CD14high pro-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, via implanted electronics and clinically-relevant stimulation parameters, is well tolerated and is able to prime the immune system toward a less inflammatory, pro-resolving phenotype.

Quantification of clinically applicable stimulation parameters for precision near-organ neuromodulation of human splenic nerves.

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.

A 172 $\mu$W Compressively Sampled Photoplethysmographic (PPG) Readout ASIC With Heart Rate Estimation Directly From Compressively Sampled Data.

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.

A Neural Probe With Up to 966 Electrodes and Up to 384 Configurable Channels in 0.13 $\mu$m SOI CMOS.

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- µm 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 µm wide, 10 mm long, 20  µm thick), achieving a crosstalk of [Formula: see text] dB. The probe base (5 × 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 µ V rms and a total power consumption of 49.1  µW/channel.

Active Electrodes for Wearable EEG Acquisition: Review and Electronics Design Methodology.

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 680 nA ECG acquisition IC for leadless pacemaker applications.

A sub- µW ECG acquisition IC is presented for a single-chamber leadless pacemaker applications. It integrates a low-power, wide dynamic-range ECG readout front end together with an analog QRS-complex extractor. To save ASIC power, a current-multiplexed channel buffer is introduced to drive a 7 b-to-10 b self-synchronized SAR ADC which utilizes 4 fF/unit capacitors. The ASIC consumes only 680nA and achieves CMRR > 90 dB, PSRR > 80 dB, an input-referred noise of 4.9 µVrms in a 130 Hz bandwidth, and has rail-to-rail DC offset rejection. Low-power heartbeat detections are evaluated with the help of the ASIC acquiring nearly 20,000 beats across 10 different records from the MIT-BIH arrhythmia database. In the presence of muscle noise, both the average Sensitivity (Se) and Positive Predictivity (PP) show more than 90% when the input SNR > 6 dB.

Noncontact ECG recording system with real time capacitance measurement for motion artifact reduction.

A system for noncontact ECG recording is proposed that measures the real time electrode-body capacitance concurrently with the ECG as a reference signal for motion artifact reduction. Simultaneous recordings of these two signals from the human body in the presence of electrode motion artifacts are shown and an adaptive least-mean-squares (LMS) filtering algorithm run on these signals is demonstrated to be able to reduce the severity of certain types of electrode motion artifacts.

An efficient and compact compressed sensing microsystem for implantable neural recordings.

Multi-Electrode Arrays (MEA) have been widely used in neuroscience experiments. However, the reduction of their wireless transmission power consumption remains a major challenge. To resolve this challenge, an efficient on-chip signal compression method is essential. In this paper, we first introduce a signal-dependent Compressed Sensing (CS) approach that outperforms previous works in terms of compression rate and reconstruction quality. Using a publicly available database, our simulation results show that the proposed system is able to achieve a signal compression rate of 8 to 16 while guaranteeing almost perfect spike classification rate. Finally, we demonstrate power consumption measurements and area estimation of a test structure implemented using TSMC 0.18 µm process. We estimate the proposed system would occupy an area of around 200 µm ×300 µm per recording channel, and consumes 0.27 µ W operating at 20 KHz .

A configurable and low-power mixed signal SoC for portable ECG monitoring applications.

This paper describes a mixed-signal ECG System-on-Chip (SoC) that is capable of implementing configurable functionality with low-power consumption for portable ECG monitoring applications. A low-voltage and high performance analog front-end extracts 3-channel ECG signals and single channel electrode-tissue-impedance (ETI) measurement with high signal quality. This can be used to evaluate the quality of the ECG measurement and to filter motion artifacts. A custom digital signal processor consisting of 4-way SIMD processor provides the configurability and advanced functionality like motion artifact removal and R peak detection. A built-in 12-bit analog-to-digital converter (ADC) is capable of adaptive sampling achieving a compression ratio of up to 7, and loop buffer integration reduces the power consumption for on-chip memory access. The SoC is implemented in 0.18 µm CMOS process and consumes 32 µ W from a 1.2 V while heart beat detection application is running, and integrated in a wireless ECG monitoring system with Bluetooth protocol. Thanks to the ECG SoC, the overall system power consumption can be reduced significantly.

Error correction algorithm for high accuracy bio-impedance measurement in wearable healthcare applications.

Implantable and ambulatory measurement of physiological signals such as Bio-impedance using miniature biomedical devices needs careful tradeoff between limited power budget, measurement accuracy and complexity of implementation. This paper addresses this tradeoff through an extensive analysis of different stimulation and demodulation techniques for accurate Bio-impedance measurement. Three cases are considered for rigorous analysis of a generic impedance model, with multiple poles, which is stimulated using a square/sinusoidal current and demodulated using square/sinusoidal clock. For each case, the error in determining pole parameters (resistance and capacitance) is derived and compared. An error correction algorithm is proposed for square wave demodulation which reduces the peak estimation error from 9.3% to 1.3% for a simple tissue model. Simulation results in Matlab using ideal RC values show an average accuracy of for single pole and for two pole RC networks. Measurements using ideal components for a single pole model gives an overall and readings from saline phantom solution (primarily resistive) gives an . A Figure of Merit is derived based on ability to accurately resolve multiple poles in unknown impedance with minimal measurement points per decade, for given frequency range and supply current budget. This analysis is used to arrive at an optimal tradeoff between accuracy and power. Results indicate that the algorithm is generic and can be used for any application that involves resolving poles of an unknown impedance. It can be implemented as a post-processing technique for error correction or even incorporated into wearable signal monitoring ICs.

High density wireless EEG prototype: Design and evaluation against reference equipment.

A high density wireless electroencephalographic (EEG) platform has been designed. It is able to record up to 64 EEG channels with electrode to tissue impedance (ETI) monitoring. The analog front-end is based on two kinds of low power ASICs implementing the active electrodes and the amplifier. A power efficient compression algorithm enables the use of continuous wireless transmission of data through Bluetooth for real-time monitoring with an overall power consumption of about 350 mW. EEG acquisitions on five subjects (one healthy subject and four patients suffering from epilepsy) have been recorded in parallel with a reference system commonly used in clinical practice and data of the wireless prototype and reference system have been processed with an automatic tool for seizure detection and localization. The false alarm rates (0.1-0.5 events per hour) are comparable between the two system and wireless prototype also detected the seizure correctly and allowed its localization.

A 155 µ W 88-dB DR discrete-time ∆Σ modulator for digital hearing aids exploiting a summing SAR ADC quantizer.

This paper presents a low-power switched-capacitor ∆Σ modulator for digital hearing-aid applications that features a novel summing successive approximation (SAR). The summing SAR performs multi-bit quantization together with the analog addition required in feed-forward (FF) ∆Σ modulator (∆ΣM) topologies, with no attenuation of the input signals and no need for amplifiers. The prototype is implemented in a 0.18- µ m CMOS technology and its measurements demonstrate a dynamic range of 88 dB in 10 kHz bandwidth while consuming 155 µW from a 1.8 V supply. The combined use of passive addition and SAR quantization reduces the complexity and power consumption of the modulator. The summing SAR ADC quantizer results in a calculated power saving of 40% when compared to a multi-bit FF ∆ΣM using active addition and flash quantization.

A 13 µA analog signal processing IC for accurate recognition of multiple intra-cardiac signals.

A low-power analog signal processing IC is presented for the low-power heart rhythm analysis. The ASIC features 3 identical, but independent intra-ECG readout channels each equipping an analog QRS feature extractor for low-power consumption and fast diagnosis of the fatal case. A 16-level digitized sine-wave synthesizer together with a synchronous readout circuit can measure bio-impedance in the range of 0.1-4.4 kΩ with 33 mΩ(rms) resolution and higher than 97% accuracy. The proposed 25 mm² ASIC consumes only 13 µA from 2.2 V. It is a highly integrated solution offering all the functionality of acquiring multiple high quality intra-cardiac signals, requiring only a few limited numbers of external passives.

Real time digitally assisted analog motion artifact reduction in ambulatory ECG monitoring system.

This paper proposes a real time digitally assisted analog motion artifact reduction ASIC with ECG measurement simultaneously. It features one ECG monitoring and in- and quad-phase electrode-skin impedance measurement, which are used to estimate motion artifacts. The implemented ASIC is capable of actual motion artifact reduction in the analog domain before final amplification.

A 160 µA biopotential acquisition IC with fully integrated IA and motion artifact suppression.

This paper proposes a 3-channel biopotential monitoring ASIC with simultaneous electrode-tissue impedance measurements which allows real-time estimation of motion artifacts on each channel using an an external µC. The ASIC features a high performance instrumentation amplifier with fully integrated sub-Hz HPF rejecting rail-to-rail electrode-offset voltages. Each readout channel further has a programmable gain amplifier and programmable 4th order low-pass filter. Time-multiplexed 12 b SAR-ADCs are used to convert all the analog data to digital. The ASIC achieves >; 115 dB of CMRR (at 50/60 Hz), a high input impedance of >; 1 GΩ and low noise (1.3 µVrms in 100 Hz). Unlike traditional methods, the ASIC is capable of actual motion artifact suppression in the analog domain before final amplification. The complete ASIC core operates from 1.2 V with 2 V digital IOs and consumes 200 µW when all 3 channels are active.

Closing the loop for Deep Brain Stimulation implants enables personalized healthcare for Parkinson's disease patients.

DEEP brain stimulation implants have improved life quality for more than 70,000 patients world-wide with diseases like Parkinson's, essential tremor, or obsessive-compulsive disorder where pharmaceutical therapies alone could not offer sufficient relief. Still, optimization and monitoring relies heavily on regular clinical visits, putting a burden on patient's comfort and clinicians. Permanent monitoring and combination with other patient health signals could ultimately lead to a personalized closed-loop therapy with remote quality monitoring. This requires technological improvements on the DBS implants such as integration of recording capabilities for brain activity monitoring, active low-power electronics, rechargeable battery technology, and body sensor networks for integration with e.g. gait, speech, and other vital information sensors on the patient's body and a link to a telemedicine platform using mobile technologies.

A mixed signal ECG processing platform with an adaptive sampling ADC for portable monitoring applications.

This paper describes a mixed-signal ECG processing platform with an 12-bit ADC architecture that can adapt its sampling rate according to the input signals rate of change. This enables the sampling of ECG signals with significantly reduced data rate without loss of information. The presented adaptive sampling scheme reduces the ADC power consumption, enables the processing of ECG signals with lower power consumption, and reduces the power consumption of the radio while streaming the ECG signals. The test results show that running a CWT-based R peak detection algorithm using the adaptively sampled ECG signals consumes only 45.6 µW and it leads to 36% less overall system power consumption.

A low-power, wireless, 8-channel EEG monitoring headset.

Micro- and nano-technology has enabled development of smaller and smarter wearable devices for medical and lifestyle related applications. In particular, recent advances in EEG monitoring technologies pave the way for wearable, wireless EEG monitoring devices. Here, a low-power wireless EEG sensor platform that measures 8-channels of EEG, is described. The platform is integrated into a wearable headset for ambulatory monitoring of EEG. While using standard EEG electrodes without conductive gel, a first evaluation shows the wireless headset is comparable to the reference system when looking at alpha wave discrimination. This device combines low-noise, and low-power functionality into an easy-to-use wireless headset, providing a first step towards a fully integrated, fully functional wearable wireless EEG monitoring system.

ECG signal compression and classification algorithm with quad level vector for ECG holter system.

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.