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.

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.

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.

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.

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.

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 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.

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.

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.

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.

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.

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.

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.

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 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.

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.

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.

An integrated circuit for wireless ambulatory arrhythmia monitoring systems.

An ECG signal processor (ESP) is proposed for the low energy wireless ambulatory arrhythmia monitoring system. The ECG processor mainly performs filtering, compression, classification and encryption. The data compression flow consisting of skeleton and modified Huffman coding is the essential function to reduce the transmission energy consumption and the memory capacity, which are the most energy consuming part.

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.