Jugular Venous Pulse Waveform Extraction From a Wearable Radio Frequency Sensor.

Many prevalent heart diseases can be indicated by the features of the jugular venous pulse (JVP), an efficacious indicator of right heart health. However, JVP dynamics are not widely utilized in clinical settings as its observation and sensing remain cumbersome. Non-invasive measures of cardiac behavior, including the JVP, are of growing interest to enable continuous and at-home monitoring of cardiac disorders. In this work, we propose a wearable near-field radio-frequency (RF) sensor affixed with a neck collar on the clavicle over the internal jugular vein to enable non-invasive JVP sensing. We employed a complex vector injection signal processing method to extract repeatable JVP waveform features in multiple postures. With a 21-subject human study, we demonstrated morphologically consistent JVP sensing with consistent a-, c-, and v-wave feature timings, benchmarked by synchronous electrocardiogram and phonocardiogram. Further, inter-postural experiments demonstrated the capability of the proposed system to quantify morphological changes to the JVP which are present in many cardiac disorders. The results of this work suggest the proposed near-field RF sensor is capable of non-invasive JVP monitoring, potentially enabling improved sensing in both clinical and ambulatory environments.

Systolic Time Interval Extraction in Hypertensive and Hypotensive Pig Models Using Wearable Near-Field Radio-Frequency Sensors.

Screening and monitoring for cardiovascular diseases (CVDs) can be enabled by analyzing systolic time intervals (STIs). As CVDs have a strong causal correlation with hypertension, it is important to validate STI sensor accuracy in hypertensive hearts to ensure consistent performance in this prevalent cardiac disease state. This work presents STI extraction using a non-invasive near-field radio-frequency (RF) sensor during normotension, hypertension, and hypotension in a pig model. Waveform features of semilunar and atrioventricular valve dynamics during systole were extracted to derive isovolumic contraction time (ICT) and left ventricular ejection time (LVET), benchmarked by a phonocardiogram and aortic catheterization. Study-wide mean relative ICT and LVET errors were -4.4ms and -3.6ms, respectively, demonstrating high accuracy during both normal and abnormal systemic pressures.Clinical relevance- This work demonstrates accurate STI extraction with relative error less than 5 ms from a non-invasive near-field RF sensor during normotensive, hypotensive, and hypertensive systemic pressures, validating the sensor's accuracy as a screening tool during this disease state.

Detection and Prediction of Sleep Disorders by Covert Bed-Integrated RF Sensors.

OBJECTIVE: Respiratory disturbances during sleep are a prevalent health condition that affects a large adult population. The gold standard to evaluate sleep disorders including apnea is overnight polysomnography, which requires a trained technician for live monitoring and post-processing scoring. Currently, the disorder events can hardly be predicted using the respiratory waveforms preceding the events. The objective of this paper is to develop an autonomous system to detect and predict respiratory events reliably based on real-time covert sensing. METHODS: A bed-integrated radio-frequency (RF) sensor by near-field coherent sensing (NCS) was employed to retrieve continuous respiratory waveforms without user's awareness. Overnight recordings were collected from 27 patients in the Weill Cornell Center for Sleep Medicine. We extracted respiratory features to feed into the random-forest machine learning model for disorder detection and prediction. The technician annotation, derived from observation by polysomnography, was used as the ground truth during the supervised learning. RESULTS: Apneic event detection achieved a sensitivity and specificity up to 88.6% and 89.0% for k-fold validation, and 83.1% and 91.6% for subject-independent validation. Prediction of forthcoming apneic events could be made up to 90 s in advance. Apneic event prediction achieved a sensitivity and specificity up to 81.3% and 82.1% for k-fold validation, and 80.5% and 82.4% for subject-independent validation. The most important features for event detection and prediction can be assessed in the learning model. CONCLUSION: A bed-integrated RF sensor can covertly and reliably detect and predict apneic events. SIGNIFICANCE: Predictive warning of the sleep disorders in advance can intervene serious apnea, especially for infants, servicemen, and patients with chronic conditions.

Deduced Respiratory Scores on COVID-19 Patients Learning from Exertion-Induced Dyspnea.

Dyspnea is one of the most common symptoms of many respiratory diseases, including COVID-19. Clinical assessment of dyspnea relies mainly on self-reporting, which contains subjective biases and is problematic for frequent inquiries. This study aims to determine if a respiratory score in COVID-19 patients can be assessed using a wearable sensor and if this score can be deduced from a learning model based on physiologically induced dyspnea in healthy subjects. Noninvasive wearable respiratory sensors were employed to retrieve continuous respiratory characteristics with user comfort and convenience. Overnight respiratory waveforms were collected on 12 COVID-19 patients, and a benchmark on 13 healthy subjects with exertion-induced dyspnea was also performed for blind comparison. The learning model was built from the self-reported respiratory features of 32 healthy subjects under exertion and airway blockage. A high similarity between respiratory features in COVID-19 patients and physiologically induced dyspnea in healthy subjects was observed. Learning from our previous dyspnea model of healthy subjects, we deduced that COVID-19 patients have consistently highly correlated respiratory scores in comparison with normal breathing of healthy subjects. We also performed a continuous assessment of the patient's respiratory scores for 12-16 h. This study offers a useful system for the symptomatic evaluation of patients with active or chronic respiratory disorders, especially the patient population that refuses to cooperate or cannot communicate due to deterioration or loss of cognitive functions. The proposed system can help identify dyspneic exacerbation, leading to early intervention and possible outcome improvement. Our approach can be potentially applied to other pulmonary disorders, such as asthma, emphysema, and other types of pneumonia.

Novel Muscle Sensing by Radiomyography (RMG) and Its Application to Hand Gesture Recognition.

Conventional electromyography (EMG) measures the continuous neural activity during muscle contraction, but lacks explicit quantification of the actual contraction. Mechanomyography (MMG) and accelerometers only measure body surface motion, while ultrasound, CT-scan and MRI are restricted to in-clinic snapshots. Here we propose a novel radiomyography (RMG) for continuous muscle actuation sensing that can be wearable or touchless, capturing both superficial and deep muscle groups. We verified RMG experimentally by a wearable forearm sensor for hand gesture recognition (HGR). We first converted the sensor outputs to the time-frequency spectrogram, and then employed the vision transformer (ViT) deep learning network as the classification model, which can recognize 23 gestures with an average accuracy up to 99% on 8 subjects. By transfer learning, high adaptivity to user difference and sensor variation were achieved at an average accuracy up to 97%. We further extended RMG to monitor eye and leg muscles and achieved high accuracy for eye movement and body posture tracking. RMG can be used with synchronous EMG to derive stimulation-actuation waveforms for many potential applications in kinesiology, physiotherapy, rehabilitation, and human-machine interface.

Attention Detection by Heartbeat and Respiratory Features from Radio-Frequency Sensor.

This work presents a study on users' attention detection with reference to a relaxed inattentive state using an over-the-clothes radio-frequency (RF) sensor. This sensor couples strongly to the internal heart, lung, and diaphragm motion based on the RF near-field coherent sensing principle, without requiring a tension chest belt or skin-contact electrocardiogram. We use cardiac and respiratory features to distinguish attention-engaging vigilance tasks from a relaxed, inattentive baseline state. We demonstrate high-quality vitals from the RF sensor compared to the reference electrocardiogram and respiratory tension belts, as well as similar performance for attention detection, while improving user comfort. Furthermore, we observed a higher vigilance-attention detection accuracy using respiratory features rather than heartbeat features. A high influence of the user's baseline emotional and arousal levels on the learning model was noted; thus, individual models with personalized prediction were designed for the 20 participants, leading to an average accuracy of 83.2% over unseen test data with a high sensitivity and specificity of 85.0% and 79.8%, respectively.

Physiological Features of Cardiac Ventricle and Valve Dynamics from Wearable Radio-Frequency Sensors.

Early detection of cardiovascular diseases via non-invasive, convenient, and continuous monitoring is crucial to reducing preventable deaths. This paper illustrates such monitoring using wearable near-field radio-frequency sensors to analyze ventricle and valve transients, which can be used as indicators of myriad cardiac disorders. We applied a novel vector injection signal processing method to improve timing consistency in ventricular contraction, ventricular relaxation, and valve opening extraction. The median relative timing error in valve opening detection was 14.7ms and 37.8ms for semilunar and atrioventricular valves, respectively, as benchmarked by the S1 and S2 heart sounds from a synchronous phonocardiogram. Clinical Relevance- No wearable sensor currently exists to conveniently and reliably evaluate ventricular and valvular dynamics, specifically valvular opening. Beyond extraction of the heart rate and its variation, the method in this paper has the potential to enable non-invasive measurements of detailed cardiac cycle timing features including valve openings, isovolumetric contraction/relaxation times, and ejection periods, improving the monitoring of patient health away from clinical healthcare centers.

Wearable RF Near-Field Cough Monitoring by Frequency-Time Deep Learning.

Coughing is a common symptom for many respiratory disorders, and can spread droplets of various sizes containing bacterial and viral pathogens. Mild coughs are usually overlooked in the early stage, not only because they are barely noticeable by the person and the people around, but also because the present recording method is not comfortable, private, or reliable for long-term monitoring. In this paper, a wearable radio-frequency (RF) sensor is presented to recognize the mild cough signal directly from the local trachea vibration characteristics, and can isolate interferences from nearby people. The sensor operates at the ultra-high-frequency band, and can couple the RF energy to the upper respiratory track by the near field of the sensing antenna. The retrieved tissue vibration caused by the cough airflow burst can then be analyzed by a convolutional neural network trained on the frequency-time spectra. The sensing antenna design is analyzed for performance improvement. During the human study of 5 participants over 100 minutes of prescribed routines, the overall recognition ratio is above 90% and the false positive ratio during other routines is below 2.09%.

Furniture-Integrated Respiration Sensors by Notched Transmission Lines.

Non-invasive respiration sensors integrated into furniture can be invisible to the user and greatly enhance comfort and convenience to facilitate many applications. Current sensors often require user cooperation or fitting, which discourages frequent usage. We present a new respiration sensor integrated into a bed or a chair by modifying a radio-frequency (RF) coaxial cable structure with a designed notch. The lung motion is coupled to the electromagnetic leakage at the notch through near-field coherent sensing (NCS). The sensors, covered with fabrics and positioned under the abdomen and thorax, can capture the respiratory waveforms and derive the breath rate. The heart rate can also be evaluated in the same setup with proper filtering. The sensor design can tolerate large position variation to accommodate user uncertainties. Various voluntary exercises of normal, deep, fast, held and blocked breathing were measured under different postures of supine, recumbent and sitting by the carrier frequency range between 900MHz and 2.4GHz. The breath rate from 10 participants compare well with the synchronous commercial chest-belt sensors in all breathing routines.

Wearable radio-frequency sensing of respiratory rate, respiratory volume, and heart rate.

Many health diagnostic systems demand noninvasive sensing of respiratory rate, respiratory volume, and heart rate with high user comfort. Previous methods often require multiple sensors, including skin-touch electrodes, tension belts, or nearby off-the-body readers, and hence are uncomfortable or inconvenient. This paper presents an over-clothing wearable radio-frequency sensor study, conducted on 20 healthy participants (14 females) performing voluntary breathing exercises in various postures. Two prototype sensors were placed on the participants, one close to the heart and the other below the xiphoid process to couple to the motion from heart, lungs and diaphragm, by the near-field coherent sensing principle. We can achieve a satisfactory correlation of our sensor with the reference devices for the three vital signs: heart rate (r = 0.95), respiratory rate (r = 0.93) and respiratory volume (r = 0.84). We also detected voluntary breath-hold periods with an accuracy of 96%. Further, the participants performed a breathing exercise by contracting abdomen inwards while holding breath, leading to paradoxical outward thorax motion under the isovolumetric condition, which was detected with an accuracy of 83%.

No-touch measurements of vital signs in small conscious animals.

Measuring the heartbeat and respiration of small conscious animals is important for assessing their health and behavior, but present techniques such as electrocardiogram (ECG), ultrasound, and auscultation rely on close skin contact with the animal. These methods can also require surface preparation, cause discomfort or stress to animals, and even require anesthetic administration, especially for birds, reptiles, and fish. Here, we show that radio frequency near-field coherent sensing (NCS) can provide a new solution to animal vital sign monitoring while ensuring minimal pain and distress. We first benchmarked NCS with synchronous ECG on an anesthetized rat. NCS was then applied to monitor a conscious hamster from outside its cage, and was further extended to a parakeet, Russian tortoise, and betta fish in a noninvasive manner. Our system can revolutionize vital sign monitoring of small conscious animals in their laboratory living quarters or natural habitats.

A Wearable RF Sensor for Monitoring Respiratory Patterns.

We present a non-invasive approach for continuous monitoring of respiration dynamics using a wearable radio-frequency (RF) sensor based on near-field coherent sensing. A continuous-wave RF signal at 1.8 GHz is generated by a software-defined radio, with both transmitter (Tx) and receiver (Rx) antennas placed close to the xiphoid process. The experimental prototype of the mobile sensor can modulate the internal organ motion in the near-field region of the Tx antenna and is then received by the nearby Rx antenna to be demodulated and sampled. Through peak detection, we have identified inhalation and exhalation peaks of each breath cycle to estimate the breath rate and the lung volume. The extracted respiratory parameters are compared with the conventional chest belts data for various simulated respiratory conditions including voluntary deep, fast-shallow and slow-shallow breathing. We also characterized simulated central sleep apneas, Cheyne-Stokes, Biot's, ataxic and coughing conditions. To accurately identify obstructive apnea, we presented a two-sensor approach that can capture paradoxical movement of thorax and abdomen. The on-line recognition of these respiratory patterns can be employed not only to continuously monitor patients with chronic respiratory disorders but also to provide real-time feedback for future therapeutic purposes.

Breathable and Flexible Piezoelectric ZnO@PVDF Fibrous Nanogenerator for Wearable Applications.

A novel breathable piezoelectric membrane has been developed by growing zinc oxide (ZnO) nanorods on the surface of electrospun poly(vinylidene fluoride) (PVDF) nanofibers using a low-temperature hydrothermal method. Significant improvement in the piezoelectric response of the PVDF membrane was achieved without compromising breathability and flexibility. PVDF is one of the most frequently used piezoelectric polymers due to its high durability and reasonable piezoelectric coefficient values. However, further enhancement of its piezoelectric response is highly desirable for sensor and energy-harvester applications. Previous studies have demonstrated that piezoelectric ceramic and polymer composites can have remarkable piezoelectric properties and flexibility. However, devices made of such composites lack breathability and some present health risks in wearable applications for containing heavy metals. Unlike other piezoelectric ceramics, ZnO is non-toxic material and has been widely used in many applications including cosmetics. The fabrication of ZnO@PVDF porous electrospun membrane involves a simple low-temperature ZnO growth in aqueous solution, which does not weaken the polarization of PVDF created during electrospinning in the high electric field.

A real-time spike classification method based on dynamic time warping for extracellular enteric neural recording with large waveform variability.

BACKGROUND: Computationally efficient spike recognition methods are required for real-time analysis of extracellular neural recordings. The enteric nervous system (ENS) is important to human health but less well-understood with few appropriate spike recognition algorithms due to large waveform variability. NEW METHOD: Here we present a method based on dynamic time warping (DTW) with high tolerance to variability in time and magnitude. Adaptive temporal gridding for "fastDTW" in similarity calculation significantly reduces the computational cost. The automated threshold selection allows for real-time classification for extracellular recordings. RESULTS: Our method is first evaluated on synthesized data at different noise levels, improving both classification accuracy and computational complexity over the conventional cross-correlation based template-matching method (CCTM) and PCA+k-means clustering without time warping. Our method is then applied to analyze the mouse enteric neural recording with mechanical and chemical stimuli. Successful classification of biphasic and monophasic spikes is achieved even when the spike variability is larger than millisecond in width and millivolt in magnitude. COMPARISON WITH EXISTING METHOD(S): In comparison with conventional template matching and clustering methods, the fastDTW method is computationally efficient with high tolerance to waveform variability. CONCLUSIONS: We have developed an adaptive fastDTW algorithm for real-time spike classification of ENS recording with large waveform variability against colony motility, ambient changes and cellular heterogeneity.

Non-Faradaic Electrochemical Detection of Exocytosis from Mast and Chromaffin Cells Using Floating-Gate MOS Transistors.

We present non-faradaic electrochemical recordings of exocytosis from populations of mast and chromaffin cells using chemoreceptive neuron MOS (CνMOS) transistors. In comparison to previous cell-FET-biosensors, the CνMOS features control (CG), sensing (SG) and floating gates (FG), allows the quiescent point to be independently controlled, is CMOS compatible and physically isolates the transistor channel from the electrolyte for stable long-term recordings. We measured exocytosis from RBL-2H3 mast cells sensitized by IgE (bound to high-affinity surface receptors FcεRI) and stimulated using the antigen DNP-BSA. Quasi-static I-V measurements reflected a slow shift in surface potential () which was dependent on extracellular calcium ([Ca]o) and buffer strength, which suggests sensitivity to protons released during exocytosis. Fluorescent imaging of dextran-labeled vesicle release showed evidence of a similar time course, while un-sensitized cells showed no response to stimulation. Transient recordings revealed fluctuations with a rapid rise and slow decay. Chromaffin cells stimulated with high KCl showed both slow shifts and extracellular action potentials exhibiting biphasic and inverted capacitive waveforms, indicative of varying ion-channel distributions across the cell-transistor junction. Our approach presents a facile method to simultaneously monitor exocytosis and ion channel activity with high temporal sensitivity without the need for redox chemistry.

Programmable ion-sensitive transistor interfaces. III. Design considerations, signal generation, and sensitivity enhancement.

We report on factors that affect DNA hybridization detection using ion-sensitive field-effect transistors (ISFETs). Signal generation at the interface between the transistor and immobilized biomolecules is widely ascribed to unscreened molecular charges causing a shift in surface potential and hence the transistor output current. Traditionally, the interaction between DNA and the dielectric or metal sensing interface is modeled by treating the molecular layer as a sheet charge and the ionic profile with a Poisson-Boltzmann distribution. The surface potential under this scenario is described by the Graham equation. This approximation, however, often fails to explain large hybridization signals on the order of tens of mV. More realistic descriptions of the DNA-transistor interface which include factors such as ion permeation, exclusion, and packing constraints have been proposed with little or no corroboration against experimental findings. In this study, we examine such physical models by their assumptions, range of validity, and limitations. We compare simulations against experiments performed on electrolyte-oxide-semiconductor capacitors and foundry-ready floating-gate ISFETs. We find that with weakly charged interfaces (i.e., low intrinsic interface charge), pertinent to the surfaces used in this study, the best agreement between theory and experiment exists when ions are completely excluded from the DNA layer. The influence of various factors such as bulk pH, background salinity, chemical reactivity of surface groups, target molecule concentration, and surface coatings on signal generation is studied. Furthermore, in order to overcome Debye screening limited detection, we suggest two signal enhancement strategies. We first describe frequency domain biosensing, highlighting the ability to sort short DNA strands based on molecular length, and then describe DNA biosensing in multielectrolytes comprising trace amounts of higher-valency salt in a background of monovalent saline. Our study provides guidelines for optimized interface design, signal enhancement, and the interpretation of FET-based biosensor signals.

Programmable ion-sensitive transistor interfaces. I. Electrochemical gating.

Electrochemical gating is the process by which an electric field normal to the insulator electrolyte interface shifts the surface chemical equilibrium and further affects the charge in solution [Jiang and Stein, Langmuir 26, 8161 (2010)]. The surface chemical reactivity and double-layer charging at the interface of electrolyte-oxide-semiconductor (EOS) capacitors is investigated. We find a strong pH-dependent hysteresis upon dc potential cycling. Varying salinity at a constant pH does not change the hysteretic window, implying that field-induced surface pH regulation is the dominant cause of hysteresis. We propose and investigate this mechanism in foundry-made floating-gate ion-sensitive field-effect transistors, which can serve as both an ionic sensor and an actuator. Termed the chemoreceptive neuron metal-oxide-semiconductor (CνMOS) transistor, it features independently driven control gates (CGs) and sensing gates (SGs) that are capacitively coupled to an extended floating gate (FG). The SG is exposed to fluid, the CG is independently driven, and the FG is capable of storing charge Q(FG) of either polarity. Asymmetric capacitive coupling between the CG and SG to FG results in intrinsic amplification of the measured surface potential shifts and influences the FG charge injection mechanism. This modified SG surface condition was monitored through transient recordings of the output current, performed under alternate positive and negative CG pulses. Transient recordings revealed a hysteresis where the current was enhanced under negative pulsing and reduced after positive pulsing. This hysteresis effect is similar to that observed with EOS capacitors, suggesting a field-dependent surface charge regulation mechanism at play. At high CG biases, nonvolatile charge Q(FG) tunneling into the FG occurs, which creates a larger field and tunes the pH response and the point of zero charge. This mechanism gives rise to surface programmability. In this paper we describe the operational principles, tunneling mechanism, and role of electrolyte composition under field modulation. The experimental findings are then modeled by a Poisson-Boltzmann formulation with surface pH regulation. We find that surface ionization constants play a dominant role in determining the pH tuning effect. In the following paper [K. Jayant et al., Phys. Rev. E 88, 012802 (2013)] we extend the dual-gate operation to molecular sensing and demonstrate the use of Q(FG) to achieve manipulation of surface-adsorbed DNA.

Programmable ion-sensitive transistor interfaces. II. Biomolecular sensing and manipulation.

The chemoreceptive neuron metal-oxide-semiconductor transistor described in the preceding paper is further used to monitor the adsorption and interaction of DNA molecules and subsequently manipulate the adsorbed biomolecules with injected static charge. Adsorption of DNA molecules onto poly-L-lysine-coated sensing gates (SGs) modulates the floating gate (FG) potential ψ(O), which is reflected as a threshold voltage shift measured from the control gate (CG) V(th_CG). The asymmetric capacitive coupling between the CG and SG to the FG results in V(th_CG) amplification. The electric field in the SG oxide E(SG_ox) is fundamentally different when we drive the current readout with V(CG) and V(ref) (i.e., the potential applied to the CG and reference electrode, respectively). The V(CG)-driven readout induces a larger E(SG_ox), leading to a larger V(th_CG) shift when DNA is present. Simulation studies indicate that the counterion screening within the DNA membrane is responsible for this effect. The DNA manipulation mechanism is enabled by tunneling electrons (program) or holes (erase) onto FGs to produce repulsive or attractive forces. Programming leads to repulsion and eventual desorption of DNA, while erasing reestablishes adsorption. We further show that injected holes or electrons prior to DNA addition either aids or disrupts the immobilization process, which can be used for addressable sensor interfaces. To further substantiate DNA manipulation, we used impedance spectroscopy with a split ac-dc technique to reveal the net interface impedance before and after charge injection.

Field modulation in bilayer graphene band structure.

Using an external electric field, one can modulate the band gap of Bernal stacked bilayer graphene by breaking the A-[Formula: see text] symmetry. We analyze strain effects on the bilayer graphene using the extended Hückel theory and find that reduced interlayer distance results in higher band gap modulation, as expected. Furthermore, above about 2.5 Å interlayer distance, the band gap is direct, follows a convex relation with the electric field and saturates to a value determined by the interlayer distance. However, below about 2.5 Å, the band gap is indirect, the trend becomes concave and a threshold electric field is observed, which also depends on the stacking distance.

Non-Faradaic electrochemical detection of protein interactions by integrated neuromorphic CMOS sensors.

Electronic detection of the binding event between biotinylated bovine serum albumen (BSA) and streptavidin is demonstrated with the chemoreceptive neuron MOS (CnuMOS) device. Differing from the ion-sensitive field-effect transistors (ISFET), CnuMOS, with the potential of the extended floating gate determined by both the sensing and control gates in a neuromorphic style, can provide protein detection without requiring analyte reference electrodes. In comparison with the microelectrode arrays, measurements are gathered through purely capacitive, non-Faradaic interactions across insulating interfaces. By using a (3-glycidoxypropyl)trimethoxysilane (3-GPS) self-assembled monolayer (SAM) as a simple covalent link for attaching proteins to a silicon dioxide sensing surface, a fully integrated, electrochemical detection platform is realized for protein interactions through monotone large-signal measurements or small-signal impedance spectroscopy. Calibration curves were created to coordinate the sensor response with ellipsometric measurements taken on witness samples. By monitoring the film thickness of streptavidin capture, a sensitivity of 25ng/cm2 or 2A of film thickness was demonstrated. With an improved noise floor the sensor can detect down to 2ng/(cm2mV) based on the calibration curve. AC measurements are shown to significantly reduce long-term sensor drift. Finally, a noise analysis of electrochemical data indicates 1/f(alpha) behavior with a noise floor beginning at approximately 1Hz.