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During the past two decades, a number of two-terminal switching devices have been demonstrated in the literature. They typically exhibit hysteric behavior in the current-to-voltage characteristics. These devices have often been also referred to as memristive devices. Their capacity to switch and exhibit electrical hysteresis has made them well-suited for applications such as data storage, in-memory computing, and in-sensor computing or in-memory sensing. The aim of this perspective paper is to is twofold. Firstly, it seeks to provide a comprehensive examination of the existing research findings in the field and engage in a critical discussion regarding the potential for the development of new non-Von-Neumann computing machines that can seamlessly integrate sensing and computing within memory units. Secondly, this paper aims to demonstrate the practical application of such an innovative approach in the realm of cancer medicine. Specifically, it explores the modern concept of employing multiple cancer markers simultaneously to enhance the efficiency of diagnostic processes in cancer medicine.

This paper addresses two key challenges toward an integrated forward error-correcting biosensor based on our previously reported self-assembled quick-response (QR) code. The first challenge involves the choice of the paper substrate for printing and self-assembling the QR code. We have compared four different substrates that includes regular printing paper, Whatman filter paper, nitrocellulose membrane and lab synthesized bacterial cellulose. We report that out of the four substrates bacterial cellulose outperforms the others in terms of probe (gold nanorods) and ink retention capability. The second challenge involves remote activation of the analyte sampling and the QR code self-assembly process. In this paper, we use light as a trigger signal and a graphite layer as a light-absorbing material. The resulting change in temperature due to infrared absorption leads to a temperature gradient that then exerts a diffusive force driving the analyte toward the regions of self-assembly. The working principle has been verified in this paper using assembled biosensor prototypes where we demonstrate higher sample flow rate due to light induced thermal gradients.

This paper extends our previous work on silver-enhancement based self-assembling structures for designing reliable, self-powered biosensors with forward error correcting (FEC) capability. At the core of the proposed approach is the integration of paper-based microfluidics with quick response (QR) codes that can be optically scanned using a smart-phone. The scanned information is first decoded to obtain the location of a web-server which further processes the self-assembled QR image to determine the concentration of target analytes. The integration substrate for the proposed FEC biosensor is polyethylene and the patterning of the QR code on the substrate has been achieved using a combination of low-cost ink-jet printing and a regular ballpoint dispensing pen. A paper-based microfluidics channel has been integrated underneath the substrate for acquiring, mixing and flowing the sample to areas on the substrate where different parts of the code can self-assemble in presence of immobilized gold nanorods. In this paper we demonstrate the proof-of-concept detection using prototypes of QR encoded FEC biosensors.

This paper presents a wireless, self-powered, affinity-based biosensor based on the integration of paper-based microfluidics with our previously reported method for self-assembling radio-frequency (RF) antennas. At the core of the proposed approach is a silver-enhancement technique that grows portions of a RF antenna in regions where target antigens hybridize with target specific affinity probes. The hybridization regions are defined by a network of nitrocellulose based microfluidic channels which implement a self-powered approach to sample the reagent and control its flow and mixing. The integration substrate for the biosensor has been constructed using polyethylene and the patterning of the antenna on the substrate has been achieved using a low-cost ink-jet printing technique. The substrate has been integrated with passive radio-frequency identification (RFID) tags to demonstrate that the resulting sensor-tag can be used for continuous monitoring in a food supply-chain where direct measurement of analytes is typically considered to be impractical. We validate the proof-of-concept operation of the proposed sensor-tag using IgG as a model analyte and using a 915 MHz Ultra-high-frequency (UHF) RFID tagging technology.

Sensitivity is an essential consideration for microfluidic paper-based analytical devices (μPADs) when these devices are used for low concentration sample detection. Very recently, ion concentration polarization (ICP)-based μPADs are emerging as novel tools for bio-sample preconcentration. In this study, we develop an enclosed paper-based microfluidic platform as a preconcentrator based on the ICP effect. This paper chip is fabricated by a Parafilm embedding technique and holds many advantages over traditional open-channel μPADs, which usually suffer from sample contamination and evaporation problems. The enclosed structure minimizes sample evaporation, reduces contamination risk, and increases the mechanical strength of the paper channel. The experiment results show that more than 100-fold concentration enhancement can be achieved in the enclosed paper device, which is comparable with most reported paper-based ICP devices. Additionally, other improved ICP performance has been observed on the enclosed device, including better concentration plug profile, increased concentration durability.

Facial palsy evaluation (FPE) aims to assess facial palsy severity of patients, which plays a vital role in facial functional treatment and rehabilitation. The traditional manners of FPE are based on subjective judgment by clinicians, which may ultimately depend on individual experience. Compared with subjective and manual evaluation, objective and automated evaluation using artificial intelligence (AI) has shown great promise in improving traditional manners and recently received significant attention. The motivation of this survey paper is mainly to provide a systemic review that would guide researchers in conducting their future research work and thus make automatic FPE applicable in real-life situations. In this survey, we comprehensively review the state-of-the-art development of AI-based FPE. First, we summarize the general pipeline of FPE systems with the related background introduction. Following this pipeline, we introduce the existing public databases and give the widely used objective evaluation metrics of FPE. In addition, the preprocessing methods in FPE are described. Then, we provide an overview of selected key publications from 2008 and summarize the state-of-the-art methods of FPE that are designed based on AI techniques. Finally, we extensively discuss the current research challenges faced by FPE and provide insights about potential future directions for advancing state-of-the-art research in this field.

Surface electromyography (sEMG), a human-machine interface for gesture recognition, has shown promising potential for decoding motor intentions, but a variety of nonideal factors restrict its practical application in assistive robots. In this paper, we summarized the current mainstream gesture recognition strategies and proposed a gesture recognition method based onmultimodal canonical correlation analysis feature fusion classification (MCAFC) for a nonideal condition that occurs in daily life, i.e., posture variations. The deep features of the sEMG and acceleration signals were first extracted via convolutional neural networks. A canonical correlation analysis was subsequently performed to associate the deep features of the two modalities. The transformed features were utilized as inputs to a linear discriminant analysis classifier to recognize the corresponding gestures. Both offline and real-time experiments were conducted on eight able-bodied subjects. The experimental results indicated that MCAFC achieved an average classification accuracy, average motion completion rate, and average motion completion time of 93.44%, 94.05%, and 1.38 s, respectively, with multiple dynamic postures, indicating significantly better performance than that of comparable methods. The results demonstrate the feasibility and superiority of the proposed multimodal signal feature fusion method for gesture recognition with posture variations, providing a new scheme for myoelectric control.

In recent years, The combination of Attention mechanism and deep learning has a wide range of applications in the field of medical imaging. However, due to its complex computational processes, existing hardware architectures have high resource consumption or low accuracy, and deploying them efficiently to DNN accelerators is a challenge. This paper proposes an online-programmable Attention hardware architecture based on compute-in-memory (CIM) marco, which reduces the complexity of Attention in hardware and improves integration density, energy efficiency, and calculation accuracy. First, the Attention computation process is decomposed into multiple cascaded combinatorial matrix operations to reduce the complexity of its implementation on the hardware side; second, in order to reduce the influence of the non-ideal characteristics of the hardware, an online-programmable CIM architecture is designed to improve calculation accuracy by dynamically adjusting the weights; and lastly, it is verified that the proposed Attention hardware architecture can be applied for the inference of deep neural networks through Spice simulation. Based on the 100nm CMOS process, compared with the traditional Attention hardware architectures, the integrated density and energy efficiency are increased by at least 91.38 times, and latency and computing efficiency are improved by about 12.5 times.

Continuous-time asynchronous data converters namely, analog-to-digital converters and analog-to-time converters, can be beneficial for certain types of applications, such as, processing of biological signals with sparse information. A particular case of these converters is the integrate-and-fire converter (IFC) that is inspired by the neural system. If it is possible to develop a standard-cell-based (SCB) IFC circuit to perform well in advanced technology nodes, it will benefit from the simplicity of SCB circuit designs and can be implemented in widely available field-programmable gate arrays (FPGAs). This way, this paper proposes two IFC circuits designed and prototyped in a 130 nm CMOS standard process. The first is a novel SCB open-loop dynamic IFC. The latter, is a closed-loop analog IFC with conventional blocks. This paper presents a through comparison between the two IFC circuits. They have a power dissipation of 59 μW and 53 μW, and an energy per pulse of 18 pJ and 1060 pJ, SCB and analog IFC, respectively. The SCB IFC has one of the lowest energy per pulse consumption reported for IFC circuits. The analog IFC, being fully differential, is to our knowledge the first of its kind. Moreover, they do not require an external clock. They can convert signals with a peak-to-peak amplitude from 1.6 mV to 28 mV and 0.6 mV to 2.4 mV, and a frequency range of 2 Hz to 42 kHz and 10 Hz to 4 kHz, SCB and analog IFC, respectively. Presenting low normalized RMS conversion plus reconstruction errors, below 5.2%. The maximum pulse density (average firing-rate) is 3300 kHz, for the SCB and 50 kHz, for the analog IFC.

This paper presents a 10-channel, 120 nW/channel, reconfigurable capacitance-to-digital converter (CDC) enabling sub- μW wearable sensing applications. The proposed multi-channel architecture supports 10 channels with a shared reconfigurable 6-bit differential analog-to-digital converter (ADC). The reconfigurable nature of the CDC enables adaptive sensing range and sensing speed based on the target application. Furthermore, the architecture performs both on/off-chip parasitic correction and baseline calibration to measure the change in capacitance ( ∆C), excluding baseline and parasitic capacitances. The experimental results show the measurement range of ∆C are 5.34 pF for 1x sensitivity and 1.8 pF for 3x sensitivity respectively. The capacitive divider-based architecture excludes power-hungry operational trans-impedance amplifiers for capacitance to voltage conversion, and the architecture supports programmable channel access to activate or deactivate each channel independently. The random interrupt protection logic avoids any broken sample or data error in a sampling window. Additionally, the channel monitoring logic helps keep track of specific channel information. The measured silicon result shows a total power consumption of 1.2 μW for 1.6 kHz sampling frequency when driven by a 32 kHz clock, which is 8.6x less than prior works. The CDC is also tested with DMMP (dimethyl-methylphosphonate) gas sensor in gas chromatography (GC). Implemented in 65 nm CMOS process, the 10-channel CDC occupies 0.251 mm 2 of active area (0.0251 mm 2 /Ch).

Bioinspired robotics and smart prostheses have many applications in the healthcare sector. Patients can use them for rehabilitation or day-to-day assistance, allowing them to regain some agency over their movements. The most common way to make these smart artificial limbs is by adding a "human-like" electronic skin to detect force and emulate touch detection. This paper presents a fully integrated CMOS-based stress sensor design with a high dynamic range (100 kPa to 100 MPa) supported by an adaptive gain-controlled chopping amplifier. The sensor chip includes four identical sensing structures capable of measuring the chip's local stress gradient and complete readout circuitry supporting data transfer via I2C protocol. The sensor takes 10.2 ms to measure through all four structures and goes into a low-power mode when not in use. The designed chip consumes a total current of  ∼ 300 μA for one complete operation cycle and  ∼ 30 μA during low power mode in simulations. Moreover, the complete design is CMOS-based, making it easier for large-scale commercial fabrication and more affordable for patients in the long run. This paper further proposes the concept of a tactile smart skin by integrating a network of sensor chips with flexible polymers.

This paper introduces a wirelessly powered scattered neural recording wearable system that can facilitate continuous, untethered, and long-term electroencephalogram (EEG) recording. The proposed system, including 32 standalone EEG recording devices and a central controller, is incorporated in a wearable form factor. The standalone devices are sparsely distributed on the scalp, allowing for flexible placement and varying quantities to provide extensive spatial coverage and scalability. Each standalone device featuring a low-power EEG recording application-specific integrated circuit (ASIC) wirelessly receives power through a 60 MHz inductive link. The low-power ASIC design (84.6 µW) ensures sufficient wireless power reception through a small receiver (Rx) coil. The 60 MHz inductive link also serves as the data carrier for wireless communication between standalone devices and the central controller, eliminating the need for additional data antennas. All these efforts contribute to the miniaturization of standalone devices with dimensions of 12 × 12 × 5 mm 3 , enhancing device wearability. The central controller applies the pulse width modulation (PWM) scheme on the 60 MHz carrier, transmitting user commands at 4 Mbps to EEG recording ASICs. The ASIC employs a novel synchronized PWM demodulator to extract user commands, operating signal digitization and data transmission. The analog frontend (AFE) amplifies the EEG signal with a gain of 45 dB and applies band-pass filtering from 0.03 Hz to 400 Hz, with an input-referred noise (IRN) of 3.62 µV RMS . The amplified EEG signal is then digitized by a 10-bit successive approximation register (SAR) analog-to-digital converter (ADC) with a peak signal-to-noise and distortion ratio (SNDR) of 55.4 dB. The resulting EEG data is transmitted to an external software-defined radio (SDR) Rx through load-shift-keying (LSK) backscatter at 3.75 Mbps. The system's functionality is fully evaluated in human experiments.

This paper 5 presents a novel charge balancing (CB) with a current-control (CC) mode and a voltage-control (VC) mode for implantable biphasic stimulators, which can achieve one-step accurate anodic pulse generating. Compared with the conventional short-pulse-injection-based CB, the proposed method could reduce the balancing time and avoid inducing undesired artifact. The CC operation compensates the majority stimulation charge at high speed, while the VC operation guarantees a high CB precision. In order to eliminate the oscillation during the mode transition, a smooth CC-VC transition method is adopted. In addition, a digital auxiliary monitoring loop is introduced against the variations of the tissue-electrode interface impedance during the stimulation process to meet long-term CB requirement. The proposed stimulator has been fabricated in a 0.18 μm BCD process with 10 V voltage compliance, and the measured CB precision is less than 3 mV. The functionalities of the proposed CB have been verified successfully through in vitro experiments.

In the paper, we present an integrated flow cytometer with a 2D array of magnetic sensors based on dual-frequency oscillators in a 65-nm CMOS process, with the chip packaged with microfluidic controls. The sensor architecture and the presented array signal processing allows uninhibited flow of the sample for high throughput without the need for hydrodynamic focusing to a single sensor. To overcome the challenge of sensitivity and specificity that comes as a trade off with high throughout, we perform two levels of signal processing. First, utilizing the fact that a magnetically tagged cell is expected to excite sequentially an array of sensors in a time-delayed fashion, we perform inter-site cross-correlation of the sensor spectrograms that allows us to suppress the probability of false detection drastically, allowing theoretical sensitivity reaching towards sub-ppM levels that is needed for rare cell or circulating tumor cell detection. In addition, we implement two distinct methods to suppress correlated low frequency drifts of singular sensors-one with an on-chip sensor reference and one that utilizes the frequency dependence of the susceptibility of super-paramagnetic magnetic beads that we deploy as tags. We demonstrate these techniques on a 7 ×7 sensor array in 65 nm CMOS technology packaged with microfluidics with magnetically tagged dielectric particles and cultu lymphoma cancer cells.

Motor imagery refers to the brain's response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio (SNR) due to various artifacts originating from other physiological sources. To enhance the classification performance of motor imagery tasks by increasing the SNR of EEG signals, several signal decomposition approaches have been proposed. Empirical mode decomposition (EMD) has shown promising results in extracting EEG components associated with motor imagery tasks more effectively than traditional linear decomposition algorithms such as Fourier and wavelet methods. Nevertheless, the EMD-based algorithm suffers from a significant challenge known as mode mixing, where frequency components intertwine with the intrinsic mode functions obtained through EMD. This issue severely hampers the accuracy of motor imagery classification. Despite numerous algorithms proposed, mode mixing remains a persistent issue. In this paper, we propose the Deep-EMD algorithm, a deep neural network-based approach to mode mixing problem. We employ two datasets to compare the motor imagery classification and mode mixing improvement achieved by the conventional EMD algorithm. Our experimental results demonstrate that the Deep-EMD algorithm effectively mitigates the mode mixing problem in decomposed EEG components, leading to improved motor imagery classification performance.

Interfaces with peripheral nerves have been widely developed to enable bioelectronic control of neural activity. Peripheral nerve neuromodulation shows great potential in addressing motor dysfunctions, neurological disorders, and psychiatric conditions. The integration of high-density neural electrodes with stimulation and recording circuits poses a challenge in the design of neural interfaces. Recent advances in active electrode strategies have achieved improved reliability and performance by implementing in-situ control, stimulation, and recording of neural fibers. This paper presents an overview of state-of-the-art neural interface systems that comprise a range of neural electrodes, neurostimulators, and bio-amplifier circuits, with a special focus on interfaces for the peripheral nerves. A discussion on the efficacy of active electrode systems and recommendations for future directions conclude this paper.

In the evolving field of medical imaging and machine learning (ML), this paper introduces a novel framework for evaluating synthetic pulmonary imaging aiming to assess synthetic data quality and applicability. Our study concentrates on synthetic X-ray chest images, crucial for diagnosing respiratory diseases. We employ SPINE (Synthetic Pulmonary Imaging Evaluation) framework, a threefold synthetic images evaluation method including expert domain assessment, statistical data analysis and adversarial evaluation. In order to replicate and validate our methodology, we followed an End-to-End data and model management process which begins with a dataset of Normal and Pneumonia chest X-rays, generating synthetic images using Generative Adversarial Networks (GANs) and training a baseline classifier, essential in the adversarial evaluation axis, testing synthetic images against real data assessing their predictive value. The critical outcome of our approach is the post-market analysis of synthetic images. This innovative method evaluates synthetic images using clinical, statistical, and scientific criteria independently from traditional generation performance metrics. This independent evaluation provides deep insights into the clinical and research effectiveness of the synthetic data. By ensuring these images mirror real data's statistical properties and maintain clinical accuracy, our framework establishes a new standard for the ethical and reliable use of synthetic data in medical imaging and research., (© 2024 The Authors.)

This paper proposes an envelope-detector-less (EDL) amplitude-shift-keying (ASK) forward telemetry (FT) demodulator for wireless power/data transfer (WPDT) systems. The EDL ASK FT demodulator can substitute bulky and power-hungry components, which are an envelope detector and an analog comparator in the conventional ASK FT demodulator, with a digital controller, reducing both power dissipation and chip area. The proposed demodulator shares the gate control signals of pass transistors, which are used in an ac-dc regulator for wireless power reception, to maintain a constant load voltage while efficiently demodulating the forward telemetry data. Also, a proposed digital cleaner in the EDL demodulator refines this control signal into a wide pulse without suffering from resonant frequency noise, while a synchronizer can align its frequency with the data rate and resonant frequency. The 0.25-μm CMOS prototype chip of the proposed power-path-less EDL ASK FT demodulator, equipped with the ac-dc regulator, demonstrates a significant 38.2% reduction in power dissipation compared to the conventional ASK FT demodulator. Moreover, the EDL ASK FT demodulator occupies only 0.023-mm 2 silicon area and achieves a low bit error rate (BER) less than 10- 4 while maintaining a regulated voltage of 4.5 V on the load.

Low-intensity focused ultrasound (FUS) is an emerging non-invasive and spatially/temporally precise method for modulating the firing rates and patterns of peripheral nerves. This paper describes an image-guided platform for chronic and patient-specific FUS neuromodulation. The system uses custom wearable probes containing separate ultrasound imaging and modulation transducer arrays realized using piezoelectric transducers assembled on a flexible printed circuit board (PCB). Dual-mode probes operating around 4 MHz (imaging) and 1.3 MHz (modulation) were fabricated and tested on tissue phantoms. The resulting B-mode images were analyzed using a template-matching algorithm to estimate the location of the target nerve and then direct the modulation beam toward the target. The ultrasound transmit voltage used to excite the modulation array was optimized in real-time by automatically regulating functional feedback signals (the average rates of emulated muscle twitches detected by an on-board motion sensor) through a proportional and integral (PI) controller, thus providing robustness to inter-subject variability and probe positioning errors. The proposed closed-loop neuromodulation paradigm was experimentally demonstrated in vitro using an active tissue phantom that integrates models of the posterior tibial nerve and nearby blood vessels together with embedded sensors and actuators.

This paper proposed an event-driven clockless level-crossing ADC (LC-ADC) suitable for biomedical applications. Thanks to the LC loop, the sampling rate of the converter automatically adapts to the input activities. Activity-dependent power consumption and data compression can thus be realized, saving system power, especially during time-sparse signal acquisition. Meanwhile, a SAR-assisted loop is exploited to resolve the loop-delay-induced distortion in conventional LC-ADC. Therefore, the resolution and power efficiency of the LC-ADC are improved effectively while maintaining the event-driven feature. Implemented in a 55nm process, the proposed LC-ADC achieves a scalable power consumption and a peak SNDR of 62.2dB for a 20kHz input. It also achieves a Walden FoM of 29.7fJ/conv.-step and a Schreier FoM of 158.6dB, which is best in class, without using off-chip calibration. Sub μW power is realized when the input frequency is below 1.5kHz. The proposed LC-ADC is also verified by simulated electrocardiogram (ECG), neural spike, and electromyogram (EMG) signals. It provides a ~7X data compression for ECG input, providing an attractive solution for time-sparse signal acquisition in biomedical applications.

This paper presents a digital edge neuromorphic spiking neural network (SNN) processor chip for a variety of edge intelligent cognitive applications. This processor allows high-speed, high-accuracy and fully on-chip spike-timing-based multi-layer SNN learning. It is characteristic of hierarchical multi-core architecture, event-driven processing paradigm, meta-crossbar for efficient spike communication, and hybrid and reconfigurable parallelism. A prototype chip occupying an active silicon area of 7.2 mm 2 was fabricated using a 65-nm 1P9M CMOS process. when running a 256-256-256-256-200 4-layer fully-connected SNN on downscaled 16 × 16 MNIST images. it typically achieved a high-speed throughput of 802 and 2270 frames/s for on-chip learning and inference, respectively, with a relatively low power dissipation of around 61 mW at a 100 MHz clock rate under a 1.0V core power supply, Our on-chip learning results in comparably high visual recognition accuracies of 96.06%, 83.38%, 84.53%, 99.22% and 100% on the MNIST, Fashion-MNIST, ETH-80, Yale-10 and ORL-10 datasets, respectively. In addition, we have successfully applied our neuromorphic chip to demonstrate high-resolution satellite cloud image segmentation and non-visual tasks including olfactory classification and textural news categorization. These results indicate that our neuromorphic chip is suitable for various intelligent edge systems under restricted cost, energy and latency budgets while requiring in-situ self-adaptative learning capability.

Purpose: Transducer positioning for liver ablation by magnetic resonance-guided high-intensity focused ultrasound (MRgHIFU) is challenging due to the presence of air-filled organs or bones on the beam path. This paper presents a software tool developed to optimize the positioning of a HIFU transducer dedicated to abdominal thermal therapy, to maximize the treatment's efficiency while minimizing the near-field risk., Methods: A software tool was developed to determine the theoretical optimal position (TOP) of the transducer based on the minimization of a cost function using the particle swarm optimization (PSO). After an initialization phase and a manual segmentation of the abdomen of 5 pigs, the program randomly generates particles with 2 degrees of freedom and iteratively minimizes the cost function of the particles considering 3 parameters weighted according to their criticality. New particles are generated around the best position obtained at the previous step and the process is repeated until the optimal position of the transducer is reached. MR imaging data from in vivo HIFU ablation in pig livers was used for ground truth comparison between the TOP and the experimental position (EP)., Results: As compared to the manual EP, the rotation difference with the TOP was on average -3.1 ± 7.1° and the distance difference was on average -7.1 ± 5.4 mm. The computational time to suggest the TOP was 20s. The software tool is modulable and demonstrated consistency and robustness when repeating the calculation and changing the initial position of the transducer., Competing Interests: The authors declare no conflict of interest., (© 2024 The Authors.)

This paper presents a supervised contrastive learning (SCL) framework for respiratory sound classification and the hardware implementation of learned ResNet on field programmable gate array (FPGA) for real-time monitoring. At the algorithmic level, multiple techniques such as features augmentation and MixUp are combined holistically to mitigate the impact of data scarcity and imbalanced classes in the training dataset. Bayesian optimization further enhances the classification accuracy through parameter tuning in pre-processing and SCL. The proposed framework achieves 0.8725 total score (including runtime score) on a ResNet-18 model in both event and record multi-class classification tasks using the SJTU Paediatric Respiratory Sound Database (SPRSound). In addition, algorithm-hardware co-optimizations including Quantization-Aware Training (QAT), merge of network layers, optimization of memory size and number of parallel threads are performed for hardware implementation on FPGA. This approach reduces 40% model size and 70% computation latency. The learned ResNet is implemented on a Xilinx Zynq ZCU102 FPGA with 16ms latency and less than 2% inference score degradation compared to the software model.

In this paper, we present a robotically steerable laser ablation probe with application to interstitial thermal therapy. Existing laser interstitial thermal therapy (LITT) methods utilize a straight probe to deliver laser energy around the tip or to the side of the tip. These methods are inadequate to provide effective treatment for large, irregularly shaped tumors. Our robotic probe can be manipulated inside soft tissue to perform ablation at multiple locations, thus enabling conformable ablation for large and complicated tumors. Instead of directly firing laser into soft tissue, a Polydimethylsiloxane (PDMS)/Carbon nanoparticles (CNPs) mixture hosts a multi-mode optical fiber at the probe tip to work as a heater when laser is activated to improve the procedural safety. This paper presents the design and fabrication of the robotic ablation probe, simulation of laser thermal transformation using finite element analysis, and experimental studies that characterize the robot motion and heating effects and demonstrate in vitro ablation.

This paper presents an arterial distension monitoring scheme using a field-programmable gate array (FPGA)-based inference machine in an ultrasound scanner circuit system. An arterial distension monitoring requires a precise positioning of an ultrasound probe on an artery as a prerequisite. The proposed arterial distension monitoring scheme is based on a finite state machine that incorporates sequential support vector machines (SVMs) to assist in both coarse and fine adjustments of probe position. The SVMs sequentially perform recognitions of ultrasonic A-mode echo pattern for a human carotid artery. By employing sequential SVMs in combination with convolution and average pooling, the number of features for the inference machine is significantly reduced, resulting in less utilization of hardware resources in FPGA. The proposed arterial distension monitoring scheme was implemented in an FPGA (Artix7) with a resource utilization percentage less than 9.3%. To demonstrate the proposed scheme, we implemented a customized ultrasound scanner consisting of a single-element transducer, an FPGA, and analog interface circuits with discrete chips. In measurements, we set virtual coordinates on a human neck for 9 human subjects. The achieved accuracy of probe positioning inference is 88%, and the Pearson coefficient (r) of arterial distension estimation is 0.838.

Recent years have witnessed significant advances brought by microfluidic biochips in automating biochemical protocols. Accurate preparation of fluid samples is an essential component of these protocols, where concentration prediction and generation are critical. Equipped with the advantages of convenient fabrication and control, microfluidic mixers demonstrate huge potential in sample preparation. Although finite element analysis (FEA) is the most commonly used simulation method for accurate concentration prediction of a given microfluidic mixer, it is time-consuming with poor scalability for large biochip sizes. Recently, machine learning models have been adopted in concentration prediction, with great potential in enhancing the efficiency over traditional FEA methods. However, the state-of-the-art machine learning-based method can only predict the concentration of mixers with fixed input flow rates and fixed sizes. In this paper, we propose a new concentration prediction method based on graph neural networks (GNNs), which can predict output concentrations for microfluidic mixters with variable input flow rates. Moreover, a transfer learning method is proposed to transfer the trained model to mixers of different sizes with reduced training data. Experimental results show that, for microfluidic mixers with fixed input flow rates, the proposed method obtains an average reduction of 88% in terms of prediction errors compared with the state-of-the-art method. For microfluidic mixers with variable input flow rates, the proposed method reduces the prediction error by 85% on average. Besides, the proposed transfer learning method reduces the training data by 84% for extending the pre-trained model for microfluidic mixers of different sizes with acceptable prediction error.

This paper presents a tri-modal self-adaptive photoplethysmography (PPG) sensor interface IC for concurrently monitoring heart rate, SpO 2 , and pulse transit time, which is a critical intermediate parameter to derive blood pressure. By implementing a highly-reconfigurable analog front-end (AFE) architecture, flexible signal chain timing control, and flexible dual-LED drivers, this sensor interface provides wide operating space to support various PPG-sensing use cases. A heart-beat-locked-loop (HBLL) scheme is further extended to achieve time-multiplexed dual-input pulse transit time extraction based on two PPG sensors placed at fingertip and chest. A self-adaptive calibration scheme is proposed to automatically match the chip's operating point with the current use case, guaranteeing a sufficient signal-to-noise ratio for the user while consuming minimum system power. This paper proposes a DC offset cancellation (DCOC) approach comprised by a logarithmic transimpedance amplifier and an 8-bit SAR ADC, achieving a measured 38 nA residue error and 8.84 μA maximum input current. Fabricated in a 65nm CMOS process, the proposed tri-modal PPG sensor interface consumes 2.3-5.7 μW AFE power and 1.52 mm 2 die area with 102dB (SpO 2 mode), 110-116 dB (HR & PTT mode) dynamic range. A SpO 2 test case and a HR & PTT test case are both demonstrated in the paper, achieving 18.9 μW and 43.7 μW system power, respectively.

A wirelessly powered and data communication system is presented which is implemented as a full system, designed for multisite implanted biomedical applications. The system is capable of receiving wireless power and data communication for each implant separately, using inductive links with different resonance frequencies. To achieve this, dual-band coils are presented in the system. In addition, the system supports bi-directional half-duplex data communication, utilizing amplitude and load shift keying (ASK and LSK) modulation schemes over a single inductive link. The system employs a digitally assisted active rectifier and an automatic resonance tuning system, to improve the power transfer efficiency (PTE) through various coupling coefficients, while minimizing the reverse current and power dissipation. The power control unit enables closed-loop monitoring to prevent high or low power delivery, and it can detect inefficient or excessive wireless power transmission or prevent temperature elevation by limiting the voltage to a safe level. A new structure of self-sampling separated- V b ASK demodulator is proposed in the paper which is utilized within the data conversion chain, serving both the external and implanted units. The whole system is fabricated using a standard 180-nm 1.8/3.3 V CMOS process with a core area of 0.82 mm[Formula: see text]. The system is tested with coupled multisite inductive links and offers the maximum overall PTE of 31.2%, from the Tx coil to the implant load.

This paper introduces a lightweight bilateral underactuated upper limb exoskeleton (UULE) designed to assist chronic stroke patients with distal joint (Elbow-Wrist) impairments during bimanual activities of daily living (ADL). The UULE aims to assist patients in shoulder flexion/extension, elbow flexion/extension, forearm pronation/supination, and wrist flexion/extension. Notable features include (i) a cable-driven mechanism maintaining a lightweight structure (1.783 kg); (ii) passive joints conforming to less-impaired proximal joints, reducing restrictions on their movements; (iii) a compact design with passive ball joints enabling bilateral configuration for scapula protraction/retraction; and (iv) implementation of the master-slave joint assistance training strategy in an underactuated exoskeleton, achieving symmetric robot joint motion in bimanual ADL. Experiments with ten healthy subjects demonstrated the UULE's effectiveness by revealing significant reductions in muscle activity in a symmetric bimanual ADL task. These advancements address critical limitations of current exoskeletons, showcasing the UULE as a promising contribution to lightweight and effective robotic rehabilitation strategies for chronic stroke patients.

Power spectral analysis (PSA) is one of the most popular and insightful methods, currently employed in several biomedical applications, aiming to identify and monitor various health related conditions. Among the most common applications of PSA is heart rate variability (HRV) analysis, which allows the extraction of further insights compared with conventional time-domain methods. Unfortunately, existing PSA approaches exhibit high computational complexity, hindering their execution on power-constrained embedded internet of things (IoT) devices. Such IoT devices are increasingly used for monitoring various conditions mainly by processing the input signals in the less complex time-domain. In this paper, a new low-complexity PSA system based on fast Gaussian gridding (FGG) is proposed, which can be used to calculate the Lomb-Scargle periodogram (LSP) of a non-uniformly spaced RR tachogram. The proposed approach is implemented on a popular ARM Cortex-M4 based embedded system, which is widely used in common wearables, and compared with conventional LSP-based approaches. Utilizing this experimental setup, a meticulous analysis is performed in terms of power, performance and quality under different operational settings, such as the total input/output samples, precision of computations, computer arithmetic (floating/fixed-point), and clock frequency. The experimental results show that the proposed FGG-based LSP approach, when specifically optimized for the targeted embedded device, outperforms existing approaches by up-to 92.99% and 91.70% in terms of energy consumption and total execution time respectively, with minimal accuracy loss.

Goal : This paper introduces DISPEL, a Python framework to facilitate development of sensor-derived measures (SDMs) from data collected with digital health technologies in the context of therapeutic development for neurodegenerative diseases. Methods : Modularity, integrability and flexibility were achieved adopting an object-oriented architecture for data modelling and SDM extraction, which also allowed standardizing SDM generation, naming, storage, and documentation. Additionally, a functionality was designed to implement systematic flagging of missing data and unexpected user behaviors, both frequent in unsupervised monitoring. Results : DISPEL is available under MIT license. It already supports formats from different data providers and allows traceable end-to-end processing from raw data collected with wearables and smartphones to structured SDM datasets. Novel and literature-based signal processing approaches currently allow to extract SDMs from 16 structured tests (including six questionnaires), assessing overall disability and quality of life, and measuring performance outcomes of cognition, manual dexterity, and mobility. Conclusion : DISPEL supports SDM development for clinical trials by providing a production-grade Python framework and a large set of already implemented SDMs. While the framework has already been refined based on clinical trials' data, ad-hoc validation of the provided algorithms in their specific context of use is recommended to the users., Competing Interests: AS, GC, AJ, AK, JPA, EB, CMK, CM, DR, and SB were employees of Biogen at the time of developing this paper and might hold shares of the company. SDD reports consultancy activity with Hoffmann-La Roche Ltd. outside of this study., (© 2024 The Authors.)

Large capacitive loading of electrodes induces massive error current and imperfect settling in the electrochemical signal acquisition process, leading to inaccurate acquisition results. To efficiently mitigate this inaccuracy, this paper presents a current-and-voltage dual-mode acquisition technique in which a voltage front-end (VFE) is employed to acquire the electrode voltage error and compensate the nonlinearity induced by the electrode capacitive loading. Therefore, the gain and bandwidth requirements of the current front end (CFE) can be relaxed to reduce the complexity and power consumption. With a relieved gain requirement, an inverter-based capacitive trans-impedance amplifier (IB-CTIA) is adopted to boost the input transconductance for low-noise design. By reusing the supply current, the IB-CTIA effectively achieves a low input-referred current noise of 3.9 pA rms and a dynamic range (DR) of 126 dB with only 18-μW static power. The prototype chip is fabricated in a 180-nm CMOS process. Interleukin-6 immunoassays (IL-6) are implemented to verify the chip's performance. With the proposed nonlinear error compensation, the correlation coefficient of the detection result is improved from 0.951 to 0.980 and the limit of detection (LoD) is reduced from 8.31 pg/mL to 6.90 pg/mL.

In this paper, a high gain amplifier with phase compensation loop is presented. A structure of parallel gate cross-coupled transistors to both ends of differential pair drain and source is designed to improves the load impedance, which obtains sufficient gain and further reduces power consumption. A novel capacitor bootstrap load circuit is proposed. The capacitor bootstrap topology is constructed by the drain source resistance of the transistor working in the cut-off region, where the gate source parasitic capacitor of the transistor is in parallel with the bootstrap capacitor rather than the existing series structure, thereby only a small bootstrap capacitor is required. By avoiding the use of large capacitors, chip area can be effectively reduced without compromising performance such as gain and bandwidth. The amplifier is fabricated using 10-μm n-type a-IGZO TFT technology. Measurement results show that the proposed amplifier achieves a voltage gain of 43.5dB and a common mode rejection ratio of 61.2dB while maintaining low power consumption. The amplifier also exhibits a -3dB bandwidth covering 0.4~2.1KHz, encompassing major bioelectric frequency bands. A real-time ECG signal was successfully captured using the fabricated TFT amplifier and gel electrodes. It has great potential in flexible sensing and acquisition applications such as electro cardiogram (ECG), electro encephalogram (EEG), pulse detection, and other wearable applications.

This paper presents an innovative T/Y-maze-based wireless power transmission (WPT) system designed to monitor spatial reference memory and learning behavior in freely moving rats. The system facilitates uninterrupted optical/electrical stimulation and neural recording experiments through the integration of wireless headstages or implants in T/Y maze setups. Utilizing an array of resonators covering the entire underneath of the mazes, the wireless platform ensures scalability with various configurations. The array is designed to ensure a natural localization mechanism to localize the Tx power toward the location of the Rx coil. The system is analyzed and modeled using ANSYS HFSS software to optimize design. The primary goal was to achieve uniform wireless power delivery throughout the mazes through a comparative study of different transmitter (Tx) array configurations, such as float, series, and parallel resonators. The calculated Specific Absorption Rate (SAR) in rat tissue model equals 1.7 W/kg at the power carrier frequency of 13.56 MHz. A prototype of the proposed maze-based WPT design, featuring 8 Tx resonators, a Tx coil and power amplifier, and a headstage power harvesting unit, is successfully implemented and its performance characterized for all three resonator configurations. The implemented T maze-based WPT system has a total length of 128 cm. In the overlapping Tx resonators configuration, a homogeneity of 94% is achieved for the measured power transfer efficiency at over 30%, while continuously delivering over 60 mW for series configuration.

Stereotactic neurosurgery is a well-established surgical technique for navigation and guidance during treatment of intracranial pathologies. Intracerebral hemorrhage (ICH) is an example of various neurosurgical conditions that can benefit from stereotactic neurosurgery. As a part of our ongoing work toward real-time MR-guided ICH evacuation, we aim to address an unmet clinical need for a skull-mounted frameless stereotactic aiming device that can be used with minimally invasive robotic systems for MR-guided interventions. In this paper, we present NICE-Aiming, a Neurosurgical, Interventional, Configurable device for Effective-Aiming in MR-guided robotic neurosurgical interventions. A kinematic model was developed and the system was used with a concentric tube robot (CTR) for ICH evacuation in (i) a skull phantom and (ii) in the first ever reported ex vivo CTR ICH evacuation using an ex vivo ovine head. The NICE-Aiming prototype provided a tip accuracy of 1.41±0.35 mm in free-space. In the MR-guided gel phantom experiment, the targeting accuracy was 2.07±0.42 mm and the residual hematoma volume was 12.87 mL (24.32% of the original volume). In the MR-guided ex vivo ovine head experiment, the targeting accuracy was 2.48±0.48 mm and the residual hematoma volume was 1.42 mL (25.08% of the original volume).

This paper presents a high-precision CMOS fluorescence photometry sensor using a novel lock-in amplification scheme based on switched-biasing and ping-pong auto-zeroing techniques. The CMOS sensor includes two photodiodes and a lock-in amplifier (LIA) operating at 1 kHz. The LIA comprises a differential low-noise amplifier using a novel switched-biasing ping-pong auto-zeroed scheme, an automatic phase aligner, a programmable gain amplifier, a band-pass filter, a mixer, and an output low-pass filter. The design is fabricated in 0.18-μm CMOS process, and the measurement shows that the LIA can retrieve noisy input signals with a dynamic reserve of 42 dB, while consuming only 0.7 mW from a 1.8 V supply voltage. The measured results show that the LIA can detect a wide range of incident light power from 8 nW to 24 μW. The proposed design is encapsulated in a 3D-printed housing allowing for real-time in vitro biomarker detection. This ambulatory platform uses an LED and a fiber optic to convey the excitation light to the sample and retrieve the fluorescence signal. Experiments with a beads solution diluted in PBS demonstrate that the sensor has a sensitivity of 1:100 k. Experimental results obtained in vitro with NIH3T3 mouse cells tagged with membrane dye show the ability of the prototype to detect different densities of cell culture. The portable prototype, which includes optical filters and a small 30 mm × 36 mm × 30 mm printed circuit board enclosed inside the 3D-printed housing, consumes 36.7 mW and weighs 120 g.

Most operant conditioning circuits predominantly focus on simple feedback process, few studies consider the intricacies of feedback outcomes and the uncertainty of feedback time. This paper proposes a neuromorphic circuit based on operant conditioning with addictiveness and time memory for automatic learning. The circuit is mainly composed of hunger output module, neuron module, excitement output module, memristor-based decision module, and memory and feedback generation module. In the circuit, the process of output excitement and addiction in stochastic feedback is achieved. The memory of interval between the two rewards is formed. The circuit can adapt to complex scenarios with these functions. In addition, hunger and satiety are introduced to realize the interaction between biological behavior and exploration desire, which enables the circuit to continuously reshape its memories and actions. The process of operant conditioning theory for automatic learning is accomplished. The study of operant conditioning can serve as a reference for more intelligent brain-inspired neural systems.

This paper presents an electronic skin (e-skin) taxel array readout chip in 0.18μm CMOS technology, achieving the highest reported spatial resolution of 200μm, comparable to human fingertips. A key innovation is the integration on chip of a 12×16 polyvinylidene fluoride (PVDF)-based piezoelectric sensor array with per-taxel signal conditioning frontend and spiking readout combined with local embedded neuromorphic first-order processing through Complex Receptive Fields (CRFs). Experimental results show that Spiking Neural Network (SNN)-based classification of the chip's spatiotemporal spiking output for input tactile stimuli such as texture and flutter frequency achieves excellent accuracies up to 97.1% and 99.2%, respectively. SNN-based classification of the indentation period applied to the on-chip PVDF sensors achieved 95.5% classification accuracy, despite using only a small 256-neuron SNN classifier, a low equivalent spike encoding resolution of 3-5 bits, and a sub-Nyquist 2.2kevent/s population spiking rate, a state-of-the-art power consumption of 12.33nW per-taxel, and 75μW-5mW for the entire chip is obtained. Finally, a comparison of the texture classification accuracies between two on-chip spike encoder outputs shows that the proposed neuromorphic level-crossing sampling (NLCS) architecture with a decaying threshold outperforms the conventional bipolar level-crossing sampling (LCS) architecture with fixed threshold.

This paper presents a compact and low-cost on-chip sensor and readout circuit. The sensor achieves high-resolution 5-degrees-of-freedom (DoF) tracking (x, y, z, yaw, and pitch). With the help of an external wire wound sensor, it can also achieve high-resolution 6-degrees-of-freedom (DoF) tracking (x, y, z, yaw, pitch, and roll angles). The sensor uses low-frequency magnetic fields to detect the position and orientation of instruments, providing a viable alternative to using X-rays in image-guided surgery. To measure the local magnetic field, a highly miniaturised on-chip magnetic sensor capable of sensing the magnetic field has been developed incorporating an on-chip magnetic sensor coil, analog-front end, continuous-time ΔΣ analog-to-digital converter (ADC), LVDS transmitter, bandgap reference, and voltage regulator. The microchip is fabricated using 65 nm CMOS technology and occupies an area of 1.06 mm2, the smallest reported among similar designs to the best of our knowledge. The 5-DoF system accurately navigates with a precision of 1.1 mm within the volume-of-intrest (VOI) of 15×15×15 cm 3 . The 6-DoF system achieves a navigation accuracy of 0.8 mm and an angular error of 1.1 degrees in the same VOI. These results were obtained at a 20 Hz update rate in benchtop characterisation. The prototype sensor demonstrates accurate position tracking in real-life pre-clinical in-vivo settings within the porcine lung of a live swine, achieving a reported worst-case registration accuracy of 5.8 mm.

To avoid unexpected failures of units in manufacturing systems, failure mode recognition and prognostics are critically important in prognostics health management (PHM). Most existing methods either ignored the effects of various failure modes on remaining useful lifetime (RUL) prediction or implemented failure mode recognition and RUL prediction as two independent tasks, which failed to exploit failure mode information to obtain accurate RUL prediction. In fact, RUL highly depends on failure modes because sensor signals under different failure modes usually present different degradation patterns. To address the issue, this paper proposes a joint learning model of failure mode recognition and RUL prediction for degradation processes based on multiple sensor signals. The proposed joint learning model first extracts features by considering the degradation mechanism to ensure good interpretability for degradation modeling, and then takes the extracted features as inputs to a deep neural network. By conducting failure mode recognition and RUL prediction as a collaborative task, the proposed model can fully characterize the complex relationship among the extracted features, RUL and failure modes, and outputs the recognized failure modes and the predicted RUL of units simultaneously. A case study on the degradation of aircraft gas turbine engines is presented to evaluate the proposed model performance., Note to Practitioners—: The paper aims to develop a joint learning method for failure mode recognition and RUL prediction of operating units. Specifically, the developed method addresses a challenging issue in practice, i.e., how to effectively conduct failure mode recognition and RUL prediction as a joint task based on interpretable extracted degradation features from multiple sensor signals. To implement this method in practice, four steps are included as follows: First , collect multiple sensor signals, failure time, and failure modes of historical units. Second , construct the joint learning model based on features extracted from sensor signals by considering the degradation mechanism. Third , estimate model parameters using the data of historical units. Fourth , recognize the failure mode and predict the RUL of an in-service unit. Since the proposed method is a data-driven neural network with flexible model structure that considers complex data relationships, it is expected to be applicable to many practical situations and use cases, especially for manufacturing systems with complex structures and unknown failure thresholds.

Brain-machine interfaces (BMI) are widely adopted in neuroscience investigations and neural prosthetics, with sensing channel counts constantly increasing. These Investigations place increasing demands for high data rates and low-power implantable devices despite high tissue losses. The Impulse radio ultra-wideband (IR-UWB), a revived wireless technology for short-range radios, has been widely used in various applications. Since the requirements and solutions are application-oriented, in this review paper we focus on neural recording implants with high-data rates and ultra-low power requirements. We examine in detail the working principle, design methodology, performance, and implementations of different architectures of IR-UWB transceivers in a quantitative manner to draw a deep comparison and extract the bottlenecks and possible solutions concerning the dedicated application. Our analysis shows that current solutions rely on enhanced or combined modulation techniques to improve link margin. An in-depth study of prior-art publications that achieved Gbps data rates concludes that edge-combination architecture and non-coherent detectors are remarkable for transmitter and receiver, respectively. Although the aim to minimize power and improve data rate - defined as energy efficiency (pJ/b) - extending communication distance despite high tissue losses and limited power budget, good narrow-band interference (NBI) tolerance coexisted in the same frequency band of UWB systems, and compatibility with energy harvesting designs are among the critical challenges remained unsolved. Furthermore, we expect that the combination of artificial intelligence (AI) and the inherent advantages of UWB radios will pave the way for future improvements in BMIs.

The study of neuron interactions and hardware implementations are crucial research directions in neuroscience, particularly in developing large-scale biological neural networks. The FitzHugh-Nagumo (FHN) model is a popular neuron model with highly biological plausibility, but its complexity makes it difficult to apply at scale. This paper presents a cost-saving and improved precision approximation algorithm for the digital implementation of the FHN model. By converting the computational data into floating-point numbers, the original multiplication calculations are replaced by adding the floating-point exponent part and fitting the mantissa part with piecewise linear. In the hardware implementation, shifters and adders are used, greatly reducing resource overhead. Implementing FHN neurons by this approximation calculations on FPGA reduces the normalized root mean square error (RMSE) to 3.5% of the state-of-the-art (SOTA) while maintaining a performance overhead ratio improvement of 1.09 times. Compared to implementations based on approximate multipliers, the proposed method achieves a 20% reduction in error at the cost of a 2.8% increase in overhead.This model gained additional biological properties compared to LIF while reducing the deployment scale by only 9%. Furthermore, the hardware implementation of nine coupled circular networks with eight nodes and directional diffusion was carried out to demonstrate the algorithm's effectiveness on neural networks. The error decreased to 60% compared to the single neuron of the SOTA. This hardware-friendly algorithm allows for the low-cost implementation of high-precision hardware simulation, providing a novel perspective for studying large-scale, biologically plausible neural networks.

This paper presents an energy-efficient wireless power receiver for implantable electrical stimulation applications, which can achieve one-step adiabatic bipolar-supply that is generated by a hybrid single-stage dual-output regulating (SSDOR) rectifiers. The structure using only four switches overcomes the disadvantages that the two output voltage values in the traditional dual-output rectifiers are close to each other. A constant-current (CC) controlled adiabatic dynamic voltage scaling (DVS) technique is proposed to minimize the voltage headroom of the stimulating drivers and improve the stimulation efficiency significantly. In addition, the receiver adopts only one general constant on-time (COT) low-frequency control to adjust the stimulation current, reducing both the power consumption and the complexity of the control circuits. The proposed receiver has been fabricated in a 0.18 μm BCD process with ±6 V voltage compliance and 2.5 mA maximum stimulating current. With a current range from ±1.5 mA to ±2.5 mA, the measured maximum average headroom voltage is only 80 mV and the peak total efficiency of the receiver is 85.6%. The functionalities of the proposed receiver have been successfully verified through in vitro experiments.

Intracortical brain-computer interfaces offer superior spatial and temporal resolutions, but face challenges as the increasing number of recording channels introduces high amounts of data to be transferred. This requires power-hungry data serialization and telemetry, leading to potential tissue damage risks. To address this challenge, this paper introduces an event-based neural compressive telemetry (NCT) consisting of 8 channel-rotating Δ-ADCs, an event-driven serializer supporting a proposed ternary address event representation protocol, and an event-based LVDS driver. Leveraging a high sparsity of extracellular spikes and high spatial correlation of the high-density recordings, the proposed NCT achieves a compression ratio of >11.4×, while consumes only 1 μW per channel, which is 127× more efficient than state of the art. The NCT well preserves the spike waveform fidelity, and has a low normalized RMS error <23% even with a spike amplitude down to only 31 μV.

In this paper, a novel fixed-window level-crossing analog-to-digital converter (LCADC) is proposed for the ECG monitoring application. The proposed circuit is implemented using fewer comparators and reference levels compared to the conventional structure, which results in a decrease in complexity and occupied silicon area. Also, the power consumption is reduced considerably by decreasing the activity of the comparator. Simulation results show a 5-fold reduction in activity by applying the standard ECG signals to the proposed structure. The proposed circuit is implemented in 0.18 μm CMOS technology using a 0.9 V supply voltage. Measurement results show a 5.9 nW power consumption and a 7.4-bit resolution. The circuit occupies a 0.05846 mm 2 silicon area. A typical level-crossing-based R-peak-detection algorithm is applied to the output samples of the LCADC, which shows the effectiveness of using this type of sampling.