A Variable-Volume Heart Model for Galvanic Coupling-Based Conductive Intracardiac Communication.

Conductive intracardiac communication (CIC) has become one of the most promising technologies in multisite leadless pacemakers for cardiac resynchronization therapy. Existing studies have shown that cardiac pulsation has a significant impact on the attenuation of intracardiac communication channels. In this study, a novel variable-volume circuit-coupled electrical field heart model, which contains blood and myocardium, is proposed to verify the phenomenon. The influence of measurements was combined with the model as the equivalent circuit. Dynamic intracardiac channel characteristics were obtained by simulating models with varying volumes of the four chambers according to the actual cardiac cycle. Subsequently, in vitro experiments were carried out to verify the model's correctness. Among the dependences of intracardiac communication channels, the distance between pacemakers exerted the most substantial influence on attenuation. In the simulation and measurement, the relationship between channel attenuation and pulsation was found through the variable-volume heart model and a porcine heart. The CIC channel attenuation had a variation of less than 3 dB.

Automatic Range Adjustment of the Fluorescence Immunochromatographic Assay Based on Image Processing.

As an emerging technology, fluorescence immunochromatographic assay (FICA) has the advantages of high sensitivity, strong stability and specificity, which is widely used in the fields of medical testing, food safety and environmental monitoring. The FICA reader based on image processing meets the needs of point-of-care testing because of its simple operation, portability and fast detection speed. However, the image gray level of common image sensors limits the detection range of the FICA reader, and high-precision image sensors are expensive, which is not conducive to the popularization of the instrument. In this paper, FICA strips' image was collected using a common complementary metal oxide semiconductor (CMOS) image sensor and a range adjustment mechanism was established to automatically adjust the exposure time of the CMOS image sensor to achieve the effect of range expansion. The detection sensitivity showed a onefold increase, and the upper detection limit showed a twofold increase after the proposed method was implemented. In addition, in the experiments of linearity and accuracy, the fitting degree (R2) of the fitted curves both reached 0.999. Therefore, the automatic range adjustment method can obviously improve the detection range of the FICA reader based on image processing.

Electrical exposure analysis of galvanic-coupled intra-body communication based on the empirical arm models.

BACKGROUND: Intra-body communication (IBC) is one of the highlights in studies of body area networks. The existing IBC studies mainly focus on human channel characteristics of the physical layer, transceiver design for the application, and the protocol design for the networks. However, there are few safety analysis studies of the IBC electrical signals, especially for the galvanic-coupled type. Besides, the human channel model used in most of the studies is just a multi-layer homocentric cylinder model, which cannot accurately approximate the real human tissue layer. METHODS: In this paper, the empirical arm models were established based on the geometrical information of six subjects. The thickness of each tissue layer and the anisotropy of muscle were also taken into account. Considering the International Commission on Non-Ionizing Radiation Protection (ICNIRP) guidelines, the restrictions taken as the evaluation criteria were the electric field intensity lower than 1.35 × 104 f V/m and the specific absorption rate (SAR) lower than 4 W/kg. The physiological electrode LT-1 was adopted in experiments whose size was 4 × 4 cm and the distance between each center of adjoining electrodes was 6 cm. The electric field intensity and localized SAR were all computed by the finite element method (FEM). The electric field intensity was set as average value of all tissues, while SAR was averaged over 10 g contiguous tissue. The computed data were compared with the 2010 ICNIRP guidelines restrictions in order to address the exposure restrictions of galvanic-coupled IBC electrical signals injected into the body with different amplitudes and frequencies. RESULTS: The input alternating signal was 1 mA current or 1 V voltage with the frequency range from 10 kHz to 1 MHz. When the subject was stimulated by a 1 mA alternating current, the average electric field intensity of all subjects exceeded restrictions when the frequency was lower than 20 kHz. The maximum difference among six subjects was 1.06 V/m at 10 kHz, and the minimum difference was 0.025 V/m at 400 kHz. While the excitation signal was a 1 V alternating voltage, the electric field intensity fell within the exposure restrictions gradually as the frequency increased beyond 50 kHz. The maximum difference among the six subjects was 2.55 V/m at 20 kHz, and the minimum difference was 0.54 V/m at 1 MHz. In addition, differences between the maximum and the minimum values at each frequency also decreased gradually with the frequency increased in both situations of alternating current and voltage. When SAR was introduced as the criteria, none of the subjects exceeded the restrictions with current injected. However, subjects 2, 4, and 6 did not satisfy the restrictions with voltage applied when the signal amplitude was ≥ 3, 6, and 10 V, respectively. The SAR differences for subjects with different frequencies were 0.062-1.3 W/kg of current input, and 0.648-6.096 W/kg of voltage input. CONCLUSION: Based on the empirical arm models established in this paper, we came to conclusion that the frequency of 100-300 kHz which belong to LF (30-300 kHz) according to the ICNIRP guidelines can be considered as the frequency restrictions of the galvanic-coupled IBC signal. This provided more choices for both intensities of current and voltage signals as well. On the other hand, it also makes great convenience for the design of transceiver hardware and artificial intelligence application. With the frequency restrictions settled, the intensity restrictions that the current signal of 1-10 mA and the voltage signal of 1-2 V were accessible. Particularly, in practical application we recommended the use of the current signals for its broad application and lower impact on the human tissue. In addition, it is noteworthy that the coupling structure design of the electrode interface should attract attention.

Investigation of implantable signal transmission characteristics based on visible data of the human leg.

BACKGROUND: Signal transmission characteristics between implanted medical devices and external equipment has been a common key issue, as has the problem of supplying energy to the devices. It can be used to enable signal transmission from implanted devices that the human body's conductive properties. Using signal transmission by galvanic coupling is one of the most effective signal transmission methods. METHODS: The signal transmission characteristics by galvanic coupling of implantable devices using a frequency range of 10 kHz to 1 MHz was analyzed in this article. A finite element (FEM) model and a phantom model established by visible human leg data were used to investigate the signal transmission characteristics of implant-to-surface, with implantable receiver electrodes at different locations. RESULTS: The results showed that the FEM model and the phantom model had similar implantable signal transmission characteristics, with an increase of frequency, signal attenuation basically remained unchanged. The gain in signal attenuation in the fixed attenuation values fluctuated no more than 5 dB and signal attenuation values rose as the channel length increased. CONCLUSIONS: Our results of signal transmission characteristics of surface-to-implant will provide a theoretical basis for implantable transceiver design, and for realization of a recharging method for implanted medical devices.

A Novel Field-Circuit FEM Modeling and Channel Gain Estimation for Galvanic Coupling Real IBC Measurements.

Existing research on human channel modeling of galvanic coupling intra-body communication (IBC) is primarily focused on the human body itself. Although galvanic coupling IBC is less disturbed by external influences during signal transmission, there are inevitable factors in real measurement scenarios such as the parasitic impedance of electrodes, impedance matching of the transceiver, etc. which might lead to deviations between the human model and the in vivo measurements. This paper proposes a field-circuit finite element method (FEM) model of galvanic coupling IBC in a real measurement environment to estimate the human channel gain. First an anisotropic concentric cylinder model of the electric field intra-body communication for human limbs was developed based on the galvanic method. Then the electric field model was combined with several impedance elements, which were equivalent in terms of parasitic impedance of the electrodes, input and output impedance of the transceiver, establishing a field-circuit FEM model. The results indicated that a circuit module equivalent to external factors can be added to the field-circuit model, which makes this model more complete, and the estimations based on the proposed field-circuit are in better agreement with the corresponding measurement results.

Development of a Calibration Strip for Immunochromatographic Assay Detection Systems.

With many benefits and applications, immunochromatographic (ICG) assay detection systems have been reported on a great deal. However, the existing research mainly focuses on increasing the dynamic detection range or application fields. Calibration of the detection system, which has a great influence on the detection accuracy, has not been addressed properly. In this context, this work develops a calibration strip for ICG assay photoelectric detection systems. An image of the test strip is captured by an image acquisition device, followed by performing a fuzzy c-means (FCM) clustering algorithm and maximin-distance algorithm for image segmentation. Additionally, experiments are conducted to find the best characteristic quantity. By analyzing the linear coefficient, an average value of hue (H) at 14 min is chosen as the characteristic quantity and the empirical formula between H and optical density (OD) value is established. Therefore, H, saturation (S), and value (V) are calculated by a number of selected OD values. Then, H, S, and V values are transferred to the RGB color space and a high-resolution printer is used to print the strip images on cellulose nitrate membranes. Finally, verification of the printed calibration strips is conducted by analyzing the linear correlation between OD and the spectral reflectance, which shows a good linear correlation (R² = 98.78%).

A Lightweight Multi-Mental Disorders Detection Method Using Entropy-Based Matrix from Single-Channel EEG Signals.

Background: Mental health issues are increasingly prominent worldwide, posing significant threats to patients and deeply affecting their families and social relationships. Traditional diagnostic methods are subjective and delayed, indicating the need for an objective and effective early diagnosis method. Methods: To this end, this paper proposes a lightweight detection method for multi-mental disorders with fewer data sources, aiming to improve diagnostic procedures and enable early patient detection. First, the proposed method takes Electroencephalography (EEG) signals as sources, acquires brain rhythms through Discrete Wavelet Decomposition (DWT), and extracts their approximate entropy, fuzzy entropy, permutation entropy, and sample entropy to establish the entropy-based matrix. Then, six kinds of conventional machine learning classifiers, including Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Naive Bayes (NB), Generalized Additive Model (GAM), Linear Discriminant Analysis (LDA), and Decision Tree (DT), are adopted for the entropy-based matrix to achieve the detection task. Their performances are assessed by accuracy, sensitivity, specificity, and F1-score. Concerning these experiments, three public datasets of schizophrenia, epilepsy, and depression are utilized for method validation. Results: The analysis of the results from these datasets identifies the representative single-channel signals (schizophrenia: O1, epilepsy: F3, depression: O2), satisfying classification accuracies (88.10%, 75.47%, and 89.92%, respectively) with minimal input. Conclusions: Such performances are impressive when considering fewer data sources as a concern, which also improves the interpretability of the entropy features in EEG, providing a reliable detection approach for multi-mental disorders and advancing insights into their underlying mechanisms and pathological states.

Adaptive Fiber-Ring Lasers Based on Isopropanol Filled Microfiber Coupler for High-Sensitivity Temperature Sensing.

We demonstrated a new method for temperature measurement inside a fiber ring laser (FRL) cavity. Different from traditional FRL temperature sensing system which need additional filter working as a sensor, a micro-fiber coupler (MFC) was designed as a beam splitter, filter, and temperature sensor. In addition, isopropanol, a liquid with very high photothermal coefficient, is selectively filled in the MFC in order to improve the sensitivity of the system on temperature. In the dynamic range of 20-40 °C, we obtained a good temperature sensitivity of -1.29 nm/°C, with linear fitting up to 0.998. Benefiting from the advantages of laser sensing, the acquired laser has a 3 - dB bandwidth of less than 0.2 nm and a signal-to-noise ratio (SNR) of up to 40 dB. The proposed sensor has a low cost and high sensitivity, which is expected to be used in biomedical health detection, real-time monitoring of ocean temperature, and other application scenarios.

A Review on Analytical Modeling for Collapse Mode Capacitive Micromachined Ultrasonic Transducer of the Collapse Voltage and the Static Membrane Deflections.

Analytical modeling of capacitive micromachined ultrasonic transducer (CMUT) is one of the commonly used modeling methods and has the advantages of intuitive understanding of the physics of CMUTs and convergent when modeling of collapse mode CMUT. This review article summarizes analytical modeling of the collapse voltage and shows that the collapse voltage of a CMUT correlates with the effective gap height and the electrode area. There are analytical expressions for the collapse voltage. Modeling of the membrane deflections are characterized by governing equations from Timoshenko, von Kármán equations and the 2D plate equation, and solved by various methods such as Galerkin's method and perturbation method. Analytical expressions from Timoshenko's equation can be used for small deflections, while analytical expression from von Kármán equations can be used for both small and large deflections.

15-nW Biopotential LPFs in 0.35- µm CMOS using subthreshold-source-follower Biquads with and without gain compensation.

Most biopotential readout front-ends rely on the g m- C lowpass filter (LPF) for forefront signal conditioning. A small g m realizes a large time constant ( τ = C / g m) suitable for ultra-low-cutoff filtering, saving both power and area. Yet, the noise and linearity can be compromised, given that each g m cell can involve one or several noisy and nonlinear V- I conversions originated from the active devices. This paper proposes the subthreshold-source-follower (SSF) Biquad as a prospective alternative. It features: 1) a very small number of active devices reducing the noise and nonlinearity footsteps; 2) No explicit feedback in differential implementation, and 3) extension of filter order by cascading. This paper presents an in-depth treatment of SSF Biquad in the nW-power regime, analyzing its power and area tradeoffs with gain, linearity and noise. A gain-compensation (GC) scheme addressing the gain-loss problem of NMOS-based SSF Biquad due to the body effect is also proposed. Two 100-Hz 4th-order Butterworth LPFs using the SSF Biquads with and without GC were fabricated in 0.35- µm CMOS. Measurement results show that the non-GC (GC) LPF can achieve a DC gain of -3.7 dB (0 dB), an input-referred noise of 36 µV rms (29 µV rms ), a HD3@60 Hz of -55.2 dB ( - 60.7 dB) and a die size of 0.11 mm² (0.08 mm²). Both LPFs draw 15 nW at 3 V. The achieved figure-of-merits (FoMs) are favorably comparable with the state-of-the-art.

A 0.83- µW QRS detection processor using quadratic spline wavelet transform for wireless ECG acquisition in 0.35- µm CMOS.

Healthcare electronics count on the effectiveness of the on-patient signal preprocessing unit to moderate the wireless data transfer for better power efficiency. In order to reduce the system power in long-time ECG acquisition, this work describes an on-patient QRS detection processor for arrhythmia monitoring. It extracts the concerned ECG part, i.e., the RR-interval between the QRS complex for evaluating the heart rate variability. The processor is structured by a scale-3 quadratic spline wavelet transform followed by a maxima modulus recognition stage. The former is implemented via a symmetric FIR filter, whereas the latter includes a number of feature extraction steps: zero-crossing detection, peak (zero-derivative) detection, threshold adjustment and two finite state machines for executing the decision rules. Fabricated in 0.35-µm CMOS the 300-Hz processor draws only 0.83 µW, which is favorably comparable with the prior arts. In the system tests, the input data is placed via an on-chip 10-bit SAR analog-to-digital converter, while the output data is emitted via an off-the-shelf wireless transmitter (TI CC2500) that is configurable by the processor for different data transmission modes: 1) QRS detection result, 2) raw ECG data or 3) both. Validated with all recordings from the MIT-BIH arrhythmia database, 99.31% sensitivity and 99.70% predictivity are achieved. Mode 1 with solely the result of QRS detection exhibits 6× reduction of system power over modes 2 and 3.