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Fatigue has become an important health problem in modern life; excessive mental fatigue may induce various cardiovascular diseases. Most current mental fatigue recognition is based only on specific scenarios and tasks. To improve the accuracy of daily mental fatigue recognition, this paper proposes a multimodal fatigue grading method that combines three signals of electrocardiogram (ECG), photoplethysmography (PPG), and blood pressure (BP). We collected ECG, PPG, and BP from 22 subjects during three time periods: morning, afternoon, and evening. Based on these three signals, 56 characteristic parameters were extracted from multiple dimensions, which comprehensively covered the physiological information in different fatigue states. The extracted parameters were compared with the feature optimization ability of recursive feature elimination (RFE), maximal information coefficient, and joint mutual information, and the optimum feature matrix selected was input into random forest (RF) for a three-level classification. The results showed that the accuracy of classification of fatigue using only one physiological feature was 88.88%, 92.72% using a combination of two physiological features, and 94.87% using all three physiological features. This study indicates that the fusion of multiple physiological traits contains more comprehensive information and better identifies the level of mental fatigue, and the RFE-RF model performs best in fatigue identification. The BP variability index is useful for fatigue classification. [ABSTRACT FROM AUTHOR]

The identification of fatigue in personal workers in particular environments can be achieved through early warning techniques. In order to prevent excessive fatigue of medical workers staying in infected areas in the early phase of the coronavirus disease pandemic, a system of low-load wearable electrocardiogram (ECG) devices was used as intelligent acquisition terminals to perform a continuous measurement ECG collection. While machine learning (ML) algorithms and heart rate variability (HRV) offer the promise of fatigue detection for many, there is a demand for ever-increasing reliability in this area, especially in real-life activities. This study proposes a random forest-based classification ML model to identify the four categories of fatigue levels in frontline medical workers using HRV. Based on the wavelet transform in ECG signal processing, stationary wavelet transform was applied to eliminate the main perturbation of ECG in the motion state. Feature selection was performed using ReliefF weighting analysis in combination with redundancy analysis to optimize modeling accuracy. The experimental results of the overall fatigue identification achieved an accuracy of 97.9% with an AUC value of 0.99. With the four-category identification model, the accuracy is 85.6%. These results proved that fatigue analysis based on low-load wearable ECG monitoring at low exertion can accurately determine the level of fatigue of caregivers and provide further ideas for researchers working on fatigue identification in special environments. [ABSTRACT FROM AUTHOR]

Micro-magnetic stimulation is a research hotspot in the field of neuromodulation. However, it is difficult to measure the weak magnetic field produced by a millimeter-sized inductor. In this study, a mutual inductance model considering different positions and sizes was established for a common planar square spiral coil micro-magnetic stimulator. A physical model was simulated using the Comsol finite element method to verify the accuracy of the mutual inductance model. A weak magnetic field detection system was constructed using the TI AD8130 and NE5532 chips, and the magnetic field strengths of excitation micro-coils sized 3.612 × 3.612 and 5.55 × 5.55 mm2 were measured. The results show that when the size ratio of the detection coil (DC) to the excitation coil (EC) is under a specific ratio (DC:EC = 1:1, 2:1, 1.53:1,2.36:1), the measurement range of the magnetic field strength is in the range 0–3.06 mT with an error of 0.05 mT, and the frequency is in the range 1–120 kHz. The measurement accuracy rate reaches 97.62%. The results of this study have potential application in the measurement of the weak magnetic field. [ABSTRACT FROM AUTHOR]

A Concurrent-wavelength Reconstruction Algorithm (CRA) based on multi-wavelength information fusion is proposed in this paper that aims to further improve the accuracy of Fluorescence Molecular Tomography (FMT) reconstruction. Combining multi-spectral data with FMT technology, the information of 650 and 750 nm wavelengths near-infrared was used to increase the feature information of the dominant 850 nm wavelength near-infrared effectively. Principal component analysis, which can remove redundant information and achieve data dimensionality reduction, was then utilized to extract the feature information. Finally, tomographic reconstruction of the anomalies was performed based on the stacked auto-encoder neural network model. The comparison results of numerical experiments showed that the reconstruction effect of CRA was superior to the performance of the single wavelength model. The correlation coefficient between CRA reconstructed anomalies' fluorescence yield values and the real fluorescence yield values remained at 0.95 or more under the noise of different levels of signal-to-noise ratios. Therefore, the CRA proposed in this paper could effectively improve on the ill-posedness of the inverse problem, which could further enhance the accuracy of FMT reconstruction. [ABSTRACT FROM AUTHOR]

The energy storage density of a capacitor depends on its relative permittivity and breakdown strength. Breakdown of a thin film always first occurs at weak defect spots of dielectrics under a high electric field. It is of great significance to study the defect-induced breakdown of dielectrics to improve the breakdown strength of the dielectric. The majority of studies about the defect-induced breakdown only determine a certain voltage inducing the breakdown, and the single-hole breakdown spots influence the defect-induced breakdown and the intrinsic breakdown under a high electric field, which is hard to facilitate the in-depth study of improving the breakdown strength. Herein, the self-healing breakdown techniques are applied to avoid the influence of single-hole breakdown. An automated real-time testing system is used to study the defect-induced breakdown of various complex film–electrode systems, which accomplishes the temporal and spatial localization of breakdown events according to the physical chemistry characteristics of breakdowns and intelligently displays breakdown events, and detailed classification methods of the defect-induced breakdown are discussed concisely and efficiently. This real-time testing system is effective in revealing the defect-induced breakdown of various complex film–electrode systems under a high electric field, paving the way for uncovering the breakdown mechanism and studying how to improve the capacitor's breakdown strength and energy density. [ABSTRACT FROM AUTHOR]

To propose a new method for real-time monitoring of blood pressure of blood loss (BPBL), this article combines pulse transit time (PTT) and heart rate variability (HRV) as input parameters to build a model for BPBL estimation. In this article, effective parameters such as PTT, R–R interval (RRI), and HRV were extracted and used to establish the blood pressure (BP) estimation. Three BP estimation models were created: the PTT model, the RRI model, and the HRV model, and they were divided into an experimental group and a control group. Finally, the effects of the different estimation models on the accuracy of BPBL were evaluated using the experimental results. The result showed that both the RRI model and the HRV model have a good improvement effect on the prediction accuracy of BPBL, and the HRV model has the highest prediction accuracy than the PTT model and the RRI model. The correlation coefficients between the actual systolic BP (SBP) and diastolic BP (DBP) and the estimated SBP and DBP of the HRV model were 0.9580 and 0.9749, respectively, and the root-mean-square error of the HRV model for both SBP and DBP were 7.59 and 6.56 mmHg, respectively. The results suggest that the accuracy of the BPBL estimated by the HRV models is better than that of the PTT model, which means that HRV seems to be more effective in improving the accuracy of BP estimation compared with RRI. These results in this article provide a new idea for other researchers in the field of BPBL estimation research. [ABSTRACT FROM AUTHOR]

Magnetic induction tomography (MIT) is a promising approach in rapid diagnosis and continuous monitoring of cerebral hemorrhage. A new algorithm for the reconstruction of intracerebral hemorrhage with MIT, including the location and volume of hemorrhage, is proposed in this study. First, 2D magnetic resonance imaging and computed tomography images of patients with cerebral hemorrhage were used for the development of simulation models. The Stacked Autoencoder (SAE) network was then used to predict the location and volume of hemorrhage by conductivity reconstruction. Finally, the one-dimensional quantitative monitoring index is proposed as an auxiliary diagnostic indicator for assessment of real-time intracranial electrical characteristics. The 2D simulation results showed that the SAE was able to quickly image the location and volume of the hemorrhages. Compared with the back-projection algorithm, the prediction speed of each frame was improved 15-fold, and the accuracy improved by 90.53%. The extracted one-dimensional quantitative monitoring indicators can describe the bleeding status. The diagnostic accuracy and the imaging speed of cerebral hemorrhage were both improved by constructing a realistic head section model and using the proposed SAE network. This research provides a new alternative for dynamic monitoring of hemorrhages and shows the potential advantages of MIT in noninvasive detection. [ABSTRACT FROM AUTHOR]

Breast cancer is a malignant tumor disease for which early detection, diagnosis, and treatment are of paramount significance in prolonging the life of patients. Magnetic Detection Electrical Impedance Tomography (MDEIT) based on the Convolutional Neural Network (CNN), which aims to realize non-invasive, high resolution detection of breast tumors, is proposed. First, the MDEIT forward problem of the coronal and horizontal planes of the breast was simulated and solved using the Finite Element Method to obtain sample datasets of different lesions. Then, the CNN was built and trained to predict the conductivity distribution in different orientations of the breast model. Finally, noise and phantom experiments were performed in order to assess the anti-noise performance of the CNN algorithm and its feasibility of detecting breast tumors in practical applications. The simulation results showed that the reconstruction relative error with the CNN algorithm can be reduced to 10%, in comparison with the truncated singular value decomposition algorithm and back propagation algorithm. The CNN algorithm had better stability in the anti-noise performance test. When the noise of 60 dB was added, the shape of the breast tumor could still be restored by the CNN algorithm. The phantom experimental results showed that through the CNN based reconstruction algorithm, the reconstruction conductivity distribution image was legible and the position of the breast tumor could be determined. It is reasonable to conclude that the MDEIT reconstruction method proposed in this study has practical importance for the early and non-invasive detection of breast tumors. [ABSTRACT FROM AUTHOR]

Magnetic induction tomography (MIT) is a non-invasive and non-contact imaging technology, which can be used in medical diagnosis by reconstructing the electrical distribution of biological tissues. Unlike other large medical imaging equipment, the device of MIT is with small size and low cost. The theoretical basis of MIT is by measuring the phase difference of magnetic flux density generated around the imaging objects, analyzing the eddy current distribution, and then using the reconstruction algorithms to obtain the electrical characteristic distribution of the object. This review introduces the development of imaging systems and the reconstruction algorithms of MIT as a medical assisted diagnostic technology, including the optimal design of the sensors, the excitation methods of the system, the calculation methods of the eddy current, and the improved methods of different reconstruction algorithms. [ABSTRACT FROM AUTHOR]

The commonly used respiratory monitoring methods in clinical medicine are chest belt or ventilators, which are not easy to carry and cumbersome to operate. In order to solve this problem, a low-cost and portable magnetic induction phase detection system for respiratory monitoring is proposed. In this study, a magnetic induction tomography system for respiration detection is established, and the phase sensitivity of the system to conductive object is evaluated through a series of experiments. At the same time, phantom experiments are carried out to simulate the respiratory process, and the phase monitoring indicators are collected to describe different respiratory states. The experimental results show that the phase detection results are consistent with the changes in the respiratory cycle. The signal-to-noise ratio of the system is 79 dB. It proves the feasibility of using magnetic induction phase measurement in respiratory monitoring and provides a new detection method of respiratory monitoring. [ABSTRACT FROM AUTHOR]

CCD-based fluorescence tomography is widely used for small animal whole-body imaging. In this report, systematic signal-to-noise ratio (SNR) analyses of a fluorescence tomography imaging (FTI) system were performed, resulting in an easy-to-follow strategy to optimize hardware configurations and operational conditions for acquiring high-quality imaging data and for improving the overall system performance. Phantom experiments were conducted to demonstrate the performance improvement by these optimizations. The improved performance was further verified by imaging a tumor-bearing mouse in vivo. This report provides general and practical guidelines for setting up a high-performance electron multiplying charge coupled device based FTI system to achieve an optimized SNR, which can be useful for future FTI technology development. [ABSTRACT FROM AUTHOR]

Electrical impedance tomography (EIT) is a technique for reconstructing the conductivity distribution by injecting currents at the boundary of a subject and measuring the resulting changes in voltage. Image reconstruction in EIT is a nonlinear and ill-posed inverse problem. The Tikhonov method with L2 regularization is always used to solve the EIT problem. However, the L2 method always smoothes the sharp changes or discontinue areas of the reconstruction. Image reconstruction using the L1 regularization allows addressing this difficulty. In this paper, a sum of absolute values is substituted for the sum of squares used in the L2 regularization to form the L1 regularization, the solution is obtained by the barrier method. However, the L1 method often involves repeatedly solving large-dimensional matrix equations, which are computationally expensive. In this paper, the projection method is combined with the L1 regularization method to reduce the computational cost. The L1 problem is mainly solved in the coarse subspace. This paper also discusses the strategies of choosing parameters. Both simulation and experimental results of the L1 regularization method were compared with the L2 regularization method, indicating that the L1 regularization method can improve the quality of image reconstruction and tolerate a relatively high level of noise in the measured voltages. Furthermore, the projected L1 method can also effectively reduce the computational time without affecting the quality of reconstructed images. [ABSTRACT FROM AUTHOR]

Abdominal hemorrhage is an important clinical disease that can be life-threatening in severe cases. Therefore, timely detection and treatment of abdominal hemorrhage is crucial for the health and safety of patients. Magnetic induction tomography is a non-invasive, nonradioactive, and non-contact electromagnetic imaging technology with potential application value for disease screening and continuous monitoring. In this paper, a simulation model of electrical impedance distribution close to the real human abdominal tissue was constructed, and based on this model, the magnetic induction tomography simulation method of internal bleeding was studied by the finite element numerical method, and the comparison was verified by phantom experiments. The eddy current density distribution inside the abdominal tissue and the magnetic induction phase data at the tissue boundary are solved, and sensitivity analysis of phase differences caused by changes in the radius and position of bleeding volume was conducted, and three sensitivity indicators were proposed. Both the simulation and phantom experiment show that when there are six types of tissues with different conductivity in the abdomen, the radius of bleeding increases from 10 to 30 mm, and the radius phase difference sensitivity index Ar increases approximately linearly monotonically. Its radius transformation sensitivity Kr is 3.0961 × 10-5°/cm. When the position of the bleeding volume changes, the sensitivity index Ax of the x-axis displacement phase difference shows a quasilinear monotonic decrease, and the x-axis displacement sensitivity Kx is -6.3744 × 10-6°/cm. The y-axis displacement phase difference sensitivity Ay index shows a quasilinear relationship and monotonically increases, with a y-axis displacement sensitivity Ky of 5.2870 × 10-4°/cm. The results indicate that the phase difference sensitivity before and after the occurrence of bleeding can be used as a quantitative monitoring indicator to monitor the occurrence and trend of intra-abdominal hemorrhage, laying the foundation for the preliminary screening and continuous monitoring of abdominal hemorrhage diseases using magnetic induction imaging., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)

Ambulatory electrocardiogram (ECG) testing plays a crucial role in the early detection, diagnosis, treatment evaluation, and prevention of cardiovascular diseases. Clear ECG signals are essential for the subsequent analysis of these conditions. However, ECG signals obtained during exercise are susceptible to various noise interferences, including electrode motion artifact, baseline wander, and muscle artifact. These interferences can blur the characteristic ECG waveforms, potentially leading to misjudgment by physicians. To suppress noise in ECG signals more effectively, this paper proposes a novel deep learning-based noise reduction method. This method enhances the diffusion model network by introducing conditional noise, designing a multi-kernel convolutional transformer network structure based on noise prediction, and integrating the diffusion model inverse process to achieve noise reduction. Experiments were conducted on the QT database and MIT-BIH Noise Stress Test Database and compared with the algorithms in other papers to verify the effectiveness of the present method. The results indicate that the proposed method achieves optimal noise reduction performance across both statistical and distance-based evaluation metrics as well as waveform visualization, surpassing eight other state-of-the-art methods. The network proposed in this paper demonstrates stable performance in addressing electrode motion artifact, baseline wander, muscle artifact, and the mixed complex noise of these three types, and it is anticipated to be applied in future noise reduction analysis of clinical dynamic ECG signals., (© 2024 Author(s). Published under an exclusive license by AIP Publishing.)