FPGA-based fast bin-ratio spiking ensemble network for radioisotope identification.

In this work, we demonstrate the training, conversion, and implementation flow of an FPGA-based bin-ratio ensemble spiking neural network applied for radioisotope identification. The combination of techniques including learned step quantisation (LSQ) and pruning facilitated the implementation by compressing the network's parameters down to 30% yet retaining the accuracy of 97.04% with an accuracy loss of less than 1%. Meanwhile, the proposed ensemble network of 20 3-layer spiking neural networks (SNNs), which incorporates 1160 spiking neurons, only needs 334 µs for a single inference with the given clock frequency of 100 MHz. Under such optimisation, this FPGA implementation in an Artix-7 board consumes 157 µJ per inference by estimation.

High Accuracy Localization for Miniature Ingestible Devices using Mutual Inductance.

This paper demonstrates an inductively coupled high-accuracy localization system for miniature ingestible devices. It utilizes an inductance double capacitances-series capacitance (LCC-S) compensation architecture that enables mutual inductance measurement at primary side that is positioned outside the human body and less constrained by power budget and size than the miniature ingestible. Depending on the secondary circuit architecture, only limited and simple cooperative measurements are needed from the ingestible secondary side, which saves power and area in the miniature device. The errors in the system are modeled thoroughly, providing insights about system require-ments for a particular localization accuracy target for efficient design and to identify key building blocks with large influence on overall performance. The model shows that sub-centimeter localization root-mean-square error (RMSE) can be achieved with a modest external ADC (18bit) using three primary coils and three secondary coils. The localization is verified along a complete small intestine tract with realistic dimensions. The proposed model is verified by simulation and experiment showing that at the selected frequency range up to 5 MHz the body has no influence on the accuracy. The use of 0.9% saline as phantom is proposed which guarantees the analysis validity for all body types.

Multi-Modal Portable Respiratory Rate Monitoring Device for Childhood Pneumonia Detection.

Accurate assessment of Respiratory Rate (RR) is the most important mechanism in detecting pneumonia in low-resource settings. Pneumonia is a disease with one of the highest mortality rates among young children under five. However, the diagnosis of pneumonia for infants remains challenging, especially in low- and middle-income countries (LMIC). In such situations, RR is most often measured manually with visual inspection. Accurate RR measurement requires the child to remain calm without any stress for a few minutes. The difficulty in achieving this with a sick child in a clinical environment can result in errors and misdiagnosis, even more so when the child is crying and non-cooperating around unfamiliar adults. Therefore, we propose an automated novel RR monitoring device built with textile glove and dry electrodes which can make use of the relaxed posture when the child is resting on the carer's lap. This portable system is non-invasive and made with affordable instrumentation integrated on customized textile glove. The glove has multi-modal automated RR detection mechanism that simultaneously uses bio-impedance and accelerometer data. This novel textile glove with dry electrodes can easily be worn by a parent/carer and is washable. The real-time display on a mobile app shows the raw data and the RR value, allowing a healthcare professional to monitor the results from afar. The prototype device has been tested on 10 volunteers with age variation of 3 years to 33 years, including male and female. The maximum variation of measured RR with the proposed system is ±2 compared to the traditional manual counting method. It does not create any discomfort for either the child or the carer and can be used up to 60 to 70 sessions/day before recharging.

A Novel In-Home Sleep Monitoring System Based on Fully Integrated Multichannel Front-End Chip and Its Multilevel Analyses.

OBJECTIVE: A novel in-home sleep monitoring system with an 8-channel biopotential acquisition front-end chip is presented and validated via multilevel data analyses and comparision with advanced polysomnography. METHODS AND PROCEDURES: The chip includes a cascaded low-noise programmable gain amplifier (PGA) and 24-bit [Formula: see text]-[Formula: see text] analog-to-digital converter (ADC). The PGA is based on three op-amp structure while the ADC adopts cascade of integrator feedforward and feedback (CIFF-B) architecture. An innovative chopper-modulated input-scaling-down technique enhances the dynamic range. The proposed system and commercial polysomnography were used for in-home sleep monitoring of 20 healthy participants. The consistency and significance of the two groups' data were analyzed. RESULTS: Fabricated in 180 nm BCD technology, the input-referred noise, input impedance, common-mode rejection ratio, and dynamic range of the acquisition front-end chip were [Formula: see text]Vpp, 1.25 GN), 113.9 dB, and 119.8 dB. The kappa coefficients between the sleep stage labels of the three scorers were 0.80, 0.76, and 0.79. The consistency of the slowing index, multiscale entropy, and percentile features between the two devices reached 0.958, 0.885, and 0.834. The macro sleep architecture characteristics of the two devices were not significantly different (all p [Formula: see text] 0.05). CONCLUSION: The proposed chip was applied to develop an in-home sleep monitoring system with significantly reduced size, power, and cost. Multilevel analyses demonstrated that this system collects stable and accurate in-home sleep data. CLINICAL IMPACT: The proposed system can be applied for long-term in-home sleep monitoring outside of laboratory environments and sleep disorders screening that with low cost.

Single Photon Kilohertz Frame Rate Imaging of Neural Activity.

Establishing the biological basis of cognition and its disorders will require high precision spatiotemporal measurements of neural activity. Recently developed genetically encoded voltage indicators (GEVIs) report both spiking and subthreshold activity of identified neurons. However, maximally capitalizing on the potential of GEVIs will require imaging at millisecond time scales, which remains challenging with standard camera systems. Here, application of single photon avalanche diode (SPAD) sensors is reported to image neural activity at kilohertz frame rates. SPADs are electronic devices that when activated by a single photon cause an avalanche of electrons and a large electric current. An array of SPAD sensors is used to image individual neurons expressing the GEVI Voltron-JF525-HTL. It is shown that subthreshold and spiking activity can be resolved with shot noise limited signals at frame rates of up to 10 kHz. SPAD imaging is able to reveal millisecond scale synchronization of neural activity in an ex vivo seizure model. SPAD sensors may have widespread applications for investigation of millisecond timescale neural dynamics.

An Impedance Sensor for Pathologically Relevant Detection of In-Stent Restenosis In Vitro.

Cardiovascular disease (CVD) is the biggest cause of death globally. CVD is caused by atherosclerosis which is the accumulation of fatty deposits, often within the fine arteries of the heart or brain. These blockages reduce blood flow and lead to oxygen starvation (ischemia) which can lead to heart attacks and strokes. To treat blocked arteries an implantable device called a stent re-opens the artery to reinstate blood flow to the organ. The stent itself can become blocked over time by cell growth (intimal hyperplasia) which is characterised by excessive smooth muscle cell proliferation. Sensors based on electrical impedance spectroscopy (EIS) embedded in a stent could detect this re-blocking to allow for early intervention. Using platinum interdigitated electrodes on silicon sensor wafers we were able to co-culture different ratios of mouse smooth muscle cells and mouse endothelial cells on these sensors. This mimics the complex, multicellular environment which a stent is found in vivo when undergoing neo-intimal hyperplasia. Trends in the cell impedances were then characterised using the detection frequency and the gradient of change between populations over time which we termed 'Peak Cumulative Gradients (PCG). PCGs were calculated to successfully discriminate each cell type. This work moves towards a sensor that may help guide clinician's decision-making in a disease that is historically silent and difficult to detect. Clinical Relevance-This moves towards an early warning system for the detection of neo intimal hyperplasia ultimately leading to a reduction in stent complications.

Commercial Off-the-Shelf Components (COTS) in Realizing Miniature Implantable Wireless Medical Devices: A Review.

Over the past decade, there has been exponential growth in the per capita rate of medical patients around the world, and this is significantly straining the resources of healthcare institutes. Therefore, the reliance on smart commercial off-the-shelf (COTS) implantable wireless medical devices (IWMDs) is increasing among healthcare institutions to provide routine medical services, such as monitoring patients' physiological signals and the remote delivery of therapeutic drugs. These smart COTS IWMDs reduce the necessity of recurring visits of patients to healthcare institutions and also mitigate physical contact, which can minimize the possibility of any potential spread of contagious diseases. Furthermore, the devices provide patients with the benefit of recuperating in familiar surroundings. As such, low-cost, ubiquitous COTS IWMDs have engendered the proliferation of telemedicine in healthcare to provide routine medical services. In this paper, a review work on COTS IWMDs is presented at a macro level to discuss the history of IWMDs, different networked COTS IWMDs, health and safety regulations of COTS IWMDs and the importance of organized procurement. Furthermore, we discuss the basic building blocks of IWMDs and how COTS components can contribute to build these blocks over widely researched custom-built application-specific integrated circuits.

Predicting Cardiovascular Stent Complications Using Self-Reporting Biosensors for Noninvasive Detection of Disease.

Self-reporting implantable medical devices are the future of cardiovascular healthcare. Cardiovascular complications such as blocked arteries that lead to the majority of heart attacks and strokes are frequently treated with inert metal stents that reopen affected vessels. Stents frequently re-block after deployment due to a wound response called in-stent restenosis (ISR). Herein, an implantable miniaturized sensor and telemetry system are developed that can detect this process, discern the different cell types associated with ISR, distinguish sub plaque components as demonstrated with ex vivo samples, and differentiate blood from blood clot, all on a silicon substrate making it suitable for integration onto a vascular stent. This work shows that microfabricated sensors can provide clinically relevant information in settings closer to physiological conditions than previous work with cultured cells.

Challenges to the Development of the Next Generation of Self-Reporting Cardiovascular Implantable Medical Devices.

Cardiovascular disease (CVD) is a group of heart and vasculature conditions which are the leading form of mortality worldwide. Blood vessels can become narrowed, restricting blood flow, and drive the majority of hearts attacks and strokes. Reactive surgical interventions are frequently required; including percutaneous coronary intervention (PCI) and coronary artery bypass grafting (CABG). Despite successful opening of vessels and restoration of blood flow, often in-stent restenosis (ISR) and graft failure can still occur, resulting in subsequent patient morbidity and mortality. A new generation of cardiovascular implants that have sensors and real-time monitoring capabilities are being developed to combat ISR and graft failure. Self-reporting stent/graft technology could enable precision medicine-based and predictive healthcare by detecting the earliest features of disease, even before symptoms occur. Bringing an implantable medical device with wireless electronic sensing capabilities to market is complex and often obstructive undertaking. This critical review analyses the obstacles that need to be overcome for self-reporting stents/grafts to be developed and provide a precision-medicine based healthcare for cardiovascular patients. Here we assess the latest research and technological advancement in the field, the current devices; including smart cardiovascular implantable biosensors and associated wireless data and power transfer solutions. We include an evaluation of devices that have reached clinical trials and the market potential for their end-user implementation.

Graphene Wrapping of Electrospun Nanofibers for Enhanced Electrochemical Sensing.

This paper presents a scalable method of developing ultrasensitive electrochemical biosensors. This is achieved by maximizing sensor conductivity through graphene wrapping of carbonized electrospun nanofibers. The effectiveness of the graphene wrap was determined visually by scanning electron microscopy and chemically by Fourier transform infrared spectroscopy, Raman spectroscopy, and X-ray diffraction. The sensing performance of different electrode samples was electrochemically characterized using cyclic voltammetry and electrochemical impedance spectroscopy, with the graphene-wrapped carbonized nanofiber electrode showing significantly improved performance. The graphene-wrapped carbonized nanofibers exhibited a relative conductivity of ∼14 times and an electroactive surface area of ∼2 times greater compared to the bare screen-printed carbon electrode despite experiencing inhibitive effects from the carbon glue used to bind the samples to the electrode. The results indicate potential for a highly conductive, inert sensing platform.

Impedimetric Detection and Electromediated Apoptosis of Vascular Smooth Muscle Using Microfabricated Biosensors for Diagnosis and Therapeutic Intervention in Cardiovascular Diseases.

Cardiovascular diseases remain a significant global burden with 1-in-3 of all deaths attributable to the consequences of the disease. The main cause is blocked arteries which often remain undetected. Implantable medical devices (IMDs) such as stents and grafts are often used to reopen vessels but over time these too will re-block. A vascular biosensor is developed that can report on cellularity and is amenable to being mounted on a stent or graft for remote reporting. Moreover, the device is designed to also receive currents that can induce a controlled form of cell death, apoptosis. A combined diagnostic and therapeutic biosensor would be transformational for the treatment of vascular diseases such as atherosclerosis and central line access. In this work, a cell sensing and cell apoptosing system based on the same interdigitated electrodes (IDEs) is developed. It is shown that the device is scalable and that by miniaturizing the IDEs, the detection sensitivity is increased. Apoptosis of vascular smooth muscle cells is monitored using continuous impedance measurements at a frequency of 10 kHz and rates of cell death are tracked using fluorescent dyes and live cell imaging.

Multimodal Integrated Sensor Platform for Rapid Biomarker Detection.

Precision metabolomics and quantification for cost-effective rapid diagnosis of disease are the key goals in personalized medicine and point-of-care testing. At present, patients are subjected to multiple test procedures requiring large laboratory equipment. Microelectronics has already made modern computing and communications possible by integration of complex functions within a single chip. As More than Moore technology increases in importance, integrated circuits for densely patterned sensor chips have grown in significance. Here, we present a versatile single complementary metal-oxide-semiconductor chip forming a platform to address personalized needs through on-chip multimodal optical and electrochemical detection that will reduce the number of tests that patients must take. The chip integrates interleaved sensing subsystems for quadruple-mode colorimetric, chemiluminescent, surface plasmon resonance, and hydrogen ion measurements. These subsystems include a photodiode array and a single photon avalanche diode array with some elements functionalized to introduce a surface plasmon resonance mode. The chip also includes an array of ion sensitive field-effect transistors. The sensor arrays are distributed uniformly over an active area on the chip surface in a scalable and modular design. Bio-functionalization of the physical sensors yields a highly selective simultaneous multiple-assay platform in a disposable format. We demonstrate its versatile capabilities through quantified bio-assays performed on-chip for glucose, cholesterol, urea, and urate, each within their naturally occurring physiological range.

Time Multiplexed Active Neural Probe with 1356 Parallel Recording Sites.

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).

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 closed-loop compressive-sensing-based neural recording system.

OBJECTIVE: This paper describes a low power closed-loop compressive sensing (CS) based neural recording system. This system provides an efficient method to reduce data transmission bandwidth for implantable neural recording devices. By doing so, this technique reduces a majority of system power consumption which is dissipated at data readout interface. The design of the system is scalable and is a viable option for large scale integration of electrodes or recording sites onto a single device. APPROACH: The entire system consists of an application-specific integrated circuit (ASIC) with 4 recording readout channels with CS circuits, a real time off-chip CS recovery block and a recovery quality evaluation block that provides a closed feedback to adaptively adjust compression rate. Since CS performance is strongly signal dependent, the ASIC has been tested in vivo and with standard public neural databases. MAIN RESULTS: Implemented using efficient digital circuit, this system is able to achieve >10 times data compression on the entire neural spike band (500-6KHz) while consuming only 0.83uW (0.53 V voltage supply) additional digital power per electrode. When only the spikes are desired, the system is able to further compress the detected spikes by around 16 times. Unlike other similar systems, the characteristic spikes and inter-spike data can both be recovered which guarantes a >95% spike classification success rate. The compression circuit occupied 0.11mm(2)/electrode in a 180nm CMOS process. The complete signal processing circuit consumes <16uW/electrode. SIGNIFICANCE: Power and area efficiency demonstrated by the system make it an ideal candidate for integration into large recording arrays containing thousands of electrode. Closed-loop recording and reconstruction performance evaluation further improves the robustness of the compression method, thus making the system more practical for long term recording.

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 .

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.

Ultra-high-density in-vivo neural probes.

The past decade has witnessed an explosive growth in our ability to observe and measure brain activity. Among different functional brain imaging techniques, the electrical measurement of neural activity using neural probes provides highest temporal resolution. Yet, the electrode density and the observability of currently available neural probe technologies fall short of the density of neurons in the brain by several orders of magnitude. This paper presents opportunities for neural probes to utilize advances in CMOS technology for increasing electrode density and observability of neural activity, while minimizing the tissue damage. The authors present opportunities for neural probes to adapt advanced CMOS technologies and discuss challenges in terms of maintaining the signal integrity and implementing data communication.