Characterization of a Novel Polypyrrole (PPy) Conductive Polymer Coated Patterned Vertical CNT (pvCNT) Dry ECG Electrode

Conventional electrode-based technologies, such as the electrocardiogram (ECG), capture physiological signals using an electrolyte solution or gel that evaporates shortly after exposure, resulting in a decrease in the quality of the signal. Previously, we reported a novel dry impedimetric electrode using patterned vertically-aligned Carbon NanoTubes (pvCNT) for biopotential measurement applications. The mechanical adhesion strength of the pvCNT electrode to the substrate was weak, hence, we have improved this electrode using a thin coating of the conductive polymer polypyrrole (PPy) that strengthens its mechanical properties. Multiwall CNTs were grown vertically on a circular stainless-steel disc (⌀ = 10 mm) substrate of 50 µm thickness forming patterned pillars on a square base (100 µm × 100 µm) with an inter-pillar spacing of 200 µm and height up to 1.5 mm. The PPy coating procedure involves applying 10 µL of PPy mixed with 70% ethyl alcohol solution and rapid drying at 300 °C using a hot air gun at a distance of 10 cm. A comparative study demonstrated that the coated pvCNT had higher impedance compared to a non-coated pvCNT but lower impedance compared to the standard gel electrode. The PPy-coated pvCNT had comparable signal capture quality but stronger mechanical adhesion to the substrate

Design and Packaging of a Custom Single-lead Electrocardiogram (ECG) Sensor Embedded with Wireless Transmission

An embedded device is developed to acquire an electrocardiogram (ECG) signal by using only a single lead. The device is capable to capture the ECG signal of a human from the wrist or heart region and transmit data through Bluetooth low energy (BLE). The device includes a custom printed circuit board (PCB) for ECG acquisition. The custom PCB implements the AD 8232 chip for ECG calculation. The PCB is embedded with a commercial nRF52840 development board. Analog data from custom PCB is converted to the digital domain by using a 10-bit analog to digital converter (ADC) and transmitted through Bluetooth 5.3 both supplied by the development board. nRF52840 is 32 bit ARM® Cortex™ processing unit. The BLE data is transmitted by a baud rate of 115.2 kbps. The PCB is 2-layer (32 mm X 50 mm X 4 mm). The device is powered by a 350 mA-hr LiPo battery and consumes approximately 0.58 mA of current. With this current consumption, the battery would last around 603 hours. Finally, all parts are packaged in a box to make it compact and functional for body-worn uses. The device is a low-cost, low-weight design suitable for mobile health (mHealth) applications

Analysis of Electric Fields inside Microchannels and Single Cell Electrical Lysis with a Microfluidic Device

micromachine

Design and validation of a wearable \u27DRL-less\u27 EEG using a novel fully-reconfigurable architecture

The conventional EEG system consists of a driven-right-leg (DRL) circuit, which prohibits modularization of the system. We propose a Lego-like connectable fully reconfigurable architecture of wearable EEG that can be easily customized and deployed at naturalistic settings for collecting neurological data. We have designed a novel Analog Front End (AFE) that eliminates the need for DRL while maintaining a comparable signal quality of EEG. We have prototyped this AFE for a single channel EEG, referred to as Smart Sensing Node (SSN), that senses brain signals and sends it to a Command Control Node (CCN) via an I2C bus. The AFE of each SSN (referential-montage) consists of an off-the-shelf instrumentation amplifier (gain=26), an active notch filter fc = 60Hz), 2nd-order active Butterworth low-pass filter followed by a passive low pass filter (fc = 47.5 Hz, gain = 1.61) and a passive high pass filter fc = 0.16 Hz, gain = 0.83). The filtered signals are digitized using a low-power microcontroller (MSP430F5528) with a 12-bit ADC at 512 sps, and transmitted to the CCN every 1 s at a bus rate of 100 kbps. The CCN can further transmit this data wirelessly using Bluetooth to the paired computer at a baud rate of 115.2 kbps. We have compared temporal and frequency-domain EEG signals of our system with a research-grade EEG. Results show that the proposed reconfigurable EEG captures comparable signals, and is thus promising for practical routine neurological monitoring in non-clinical settings where a flexible number of EEG channels are needed

Body-worn fully-passive wireless analog sensors for biopotential measurement through load modulation

Fully-passive wireless and disposable bodysensors are promising for unobtrusive monitoring of physiological signals at natural settings. We present a new type of wireless analog passive sensor (WAPS) based on resistive damping, which can be used for biopotential sensing. The resistive WAPS operates by modulating the amplitudes of the incident RF signal, and composes of a loop antenna, a tuning capacitor, and a MOSFET (an additional biasing resistance is used in one variation). The scanner transmits carrier RF signal at 13:34MHz and the load modulated signal is captured with the signal analyzer. The envelope of the modulated signal correlates with the biopotential being sensed. Both enhancement and depletion MOSFETs are demonstrated, where the earlier demonstrated superior performance. The sensitivity can be as low as 10 mV, suitable for ECG and EMG physiological signal capture. The transmission power were 0 dBm while the co-axial separation between antennas were 21.5 mm. The results show that the proposed WAPS can be used to develop disposable biopotential sensor suitable for body-worn physiological signal monitoring system

QM-cluster model study of CO2 hydration mechanisms in metal-substituted human carbonic anhydrase II

Electrical energy storage need has evolved to lightweight and portable devices such as electric vehicle, drones, robotics, wearables, etc. Current technology of batteries such as Li-Ion or Li-Poly are not able to meet the requirement for future. We have been developing a new type of supercapacitor for this technological barrier. Our supercapacitors are fabricated with inkjet-printing (IJP) technique that uses very precise MEMS based cartridge to print thin-films on planar substrates. We have previously demonstrated metal-insulator-metal (MIM) capacitor fabrication and simulation, as well as stacked MIM supercapacitor fabrication. In this paper, we present electrical characterization (such as charging-discharging cycles) and scanning electron microscopy image for IJP stacked MIM supercapacitor. The electrical characterization validates the charge storage capability of the supercapacitor. We have tested the samples for up to 20 V charging voltage. The corresponding stored charge can be as high as 40 nC, and the charge density is 17.4 C/m3. These solid-state IJP stacked MIM supercapacitors are flexible with high energy-density and safe for prolonged use which can be applicable in electric vehicles, wearables, implantable, drones, and other energy storage applications

Single Channel EEG Based Score Generation to Monitor the Severity and Progression of Mild Cognitive Impairment

Mild Cognitive Impairment (MCI) is a preliminary stage of Dementia. MCI is determined by behavioral screening measures such as Montreal Cognitive Assessment (MoCA) and Mini-Mental Status Examination (MMSE). Therefore, monitoring the progression of MCI and predicting MoCA scores from objective physiological measures like the EEG is crucial as it will not only help to improve the mental healthcare of the aging population but also to reduce healthcare costs. In this study, we demonstrate a single channel EEG based MoCA score generation method, which is cost-effective and suitable for continuous patient monitoring in the longitudinal study. We collected scalp EEG data while subjects were stimulated with five auditory speech signals. We extracted 590 features from Event-Related Brain Potentials (ERPs), which included time and spectral domain characteristics of the response. The top 11 features, ranked by mutual information, were used for building regression models to generate MoCA scores of the subjects. Robustness of our model was tested using R-squared value, mean square error (MSE), residual\u27s quantile plot, and cook\u27s distance. The analysis shows R-squared=0.78 with MSE=1.63, and residual analysis suggests that the model is acceptable in terms of quantile plot, leverage, and Cook\u27s distance. The outcomes indicate that single-channel based EEG can be used to estimate cognitive scores automatically for severity detection and progression monitoring, which will help us to efficaciously assess the mental health status of elderly people to improve the prognosis and rehabilitation of age-related cognitive impairments

A Single-channel EEG-based approach to detect mild cognitive impairment via speech-evoked brain responses

Mild cognitive impairment (MCI) is the preliminary stage of dementia, which may lead to Alzheimer\u27s disease (AD) in the elderly people. Therefore, early detection of MCI has the potential to minimize the risk of AD by ensuring the proper mental health care before it is too late. In this paper, we demonstrate a single-channel EEG-based MCI detection method, which is cost-effective and portable, and thus suitable for regular home-based patient monitoring. We collected the scalp EEG data from 23 subjects, while they were stimulated with five auditory speech signals. The cognitive state of the subjects was evaluated by the Montreal cognitive assessment test (MoCA). We extracted 590 features from the event-related potential (ERP) of the collected EEG signals, which included time and spectral domain characteristics of the response. The top 25 features, ranked by the random forest method, were used for classification models to identify subjects with MCI. Robustness of our model was tested using leave-one-out cross-validation while training the classifiers. Best results (leave-one-out cross-validation accuracy 87.9%, sensitivity 84.8%, specificity 95%, and F score 85%) were obtained using support vector machine (SVM) method with radial basis kernel (RBF) (sigma = 10/cost = 10^{2}). Similar performances were also observed with logistic regression (LR), further validating the results. Our results suggest that single-channel EEG could provide a robust biomarker for early detection of MCI

BRAINsens: Body-Worn Reconfigurable Architecture of Integrated Network Sensors

Body sensor network (BSN) is a promising human–centric technology to monitor neurophysiological data. We propose a fully-reconfigurable architecture that addresses the major challenges of a heterogenous BSN, such as scalabiliy, modularity and flexibility in deployment. Existing BSNs especially with Electroencephalogarm (EEG) have these limitations mainly due to the use of driven-right-leg (DRL) circuit. We address these limitations by custom-designing DRL-less EEG smart sensing nodes (SSN) for modular and spatially distributed systems. Each single-channel EEG SSN with a input-referred noise of 0.82 μVrms and CMRR of 70 dB (at 60 Hz), samples brain signals at 512 sps. SSNs in the network can be configured at the time of deployment and can process information locally to significantly reduce data payload of the network. A Control Command Node (CCN) initializes, synchronizes, periodically scans for the available SSNs in the network, aggregates their data and sends it wirelessly to a paired device at a baud rate of 115.2 kbps. At the given settings of the I2C bus speed of 100 kbps, CCN can configure up to 39 EEG SSNs in a lego-like platform. The temporal and frequency-domain performance of the designed “DRL-less” EEG SSNs is evaluated against a research-grade Neuroscan and consumer-grade Emotiv EPOC EEG. The results show that the proposed network system with wearable EEG can be deployed in situ for continuous brain signal recording in real-life scenarios. The proposed system can also seamlessly incorporate other physiological SSNs for ECG, HRV, temperature etc. along with EEG within the same topology