Jinhai Wang

In order to improve the accuracy of concentration detection in turbid media, this study proposes a solution concentration detection method based on a variable-angle wedge-shaped sample cell. Each angle of the wedge-shaped sample cell corresponds to a specific optical path combination. By measuring at multiple wedge angles, we can expand a variety of optical path combinations, providing more information about the turbid media. In this study, transmission light intensity distribution characteristics of phantom solutions were collected at wedge angles ranging from 10 to 45°, with a 5° interval, and a multipath combination detection model was constructed. By analyzing the distribution characteristics of the transmission light intensity along the gradient direction, multipath combination information was fused, and a calibration model was constructed using partial least-squares regression. The results show that, compared to the detection method using a fixed-angle to construct multiple optical paths, the multipath combination method effectively improves the accuracy of concentration detection in turbid media, with a prediction set correlation coefficient (Rp) reaching 0.995. Therefore, this study proposes a new method to enhance the accuracy of turbid media detection by utilizing scattering characteristics through the construction of a multipath combination model by varying the wedge-shaped sample cell angles.

Background: In motor imagery brain–computer interface (MI-BCI) research, electroencephalogram (EEG) signals are complex and nonlinear. This complexity and nonlinearity render signal processing and classification challenging when employing traditional linear methods. Information entropy, with its intrinsic nonlinear characteristics, effectively captures the dynamic behavior of EEG signals, thereby addressing the limitations of traditional methods in capturing linear features. However, the multitude of entropy types leads to unclear application scenarios, with a lack of systematic descriptions. Methods: This study conducted a review of 63 high-quality research articles focused on the application of entropy in MI-BCI, published between 2019 and 2023. It summarizes the names, functions, and application scopes of 13 commonly used entropy measures. Results: The findings indicate that sample entropy (16.3%), Shannon entropy (13%), fuzzy entropy (12%), permutation entropy (9.8%), and approximate entropy (7.6%) are the most frequently utilized entropy features in MI-BCI. The majority of studies employ a single entropy feature (79.7%), with dual entropy (9.4%) and triple entropy (4.7%) being the most prevalent combinations in multiple entropy applications. The incorporation of entropy features can significantly enhance pattern classification accuracy (by 8–10%). Most studies (67%) utilize public datasets for classification verification, while a minority design and conduct experiments (28%), and only 5% combine both methods. Conclusions: Future research should delve into the effects of various entropy features on specific problems to clarify their application scenarios. As research methodologies continue to evolve and advance, entropy features are poised to play a significant role in a wide array of fields and contexts.

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.

Magnetic induction tomography (MIT) is an emerging imaging technology holding significant promise in the field of cerebral hemorrhage monitoring. The commonly employed imaging method in MIT is time-difference imaging. However, this approach relies on magnetic field signals preceding cerebral hemorrhage, which are often challenging to obtain. Multiple bioelectrical impedance information with different frequencies is added to this study on the basis of single-frequency information, and the collected signals with different frequencies are identified to obtain the magnetic field signal generated by single-layer heterogeneous tissue. The Stacked Autoencoder (SAE) neural network algorithm is used to reconstruct the images of head multi-layer tissues. Both numerical simulation and phantom experiments are carried out. The results indicate that the relative error of the multi-frequency SAE reconstruction is only 7.82%, outperforming traditional algorithms. Moreover, under a noise level of 40 dB, the anti-interference capability of the MIT algorithm based on frequency identification and SAE is superior to traditional algorithms. This research explores a novel approach for the dynamic monitoring of cerebral hemorrhage and demonstrates the potential advantages of MIT in non-invasive monitoring.

Objective: The presence of mental fatigue seriously affects daily life and working conditions. Non-invasive transcranial electrical stimulation has become an increasingly popular tool for relieving mental fatigue. We investigated whether transcranial direct current stimulation (tDCS) and transcranial alternating current stimulation (tACS) could be used to alleviate the state of mental fatigue in a population of healthy young adults and compared their effects.Methods: We recruited 10 participants for a blank control, repeated measures study. Each participant received 15 min of anodal tDCS, α-tACS, and blank stimulation. Participants were required to fill in the scale, perform the test task and collect ECG signals in the baseline, fatigue and post-stimulus states. We then assessed participants’ subjective fatigue scale scores, test task accuracy and HRV characteristics of ECG signals separately.Results: We found that both anodal tDCS and α-tACS significantly (P < 0.05) reduced subjective fatigue and improved accuracy on the test task compared to the blank group, and the extent of change was greater with tACS. For the HRV features extracted from ECG signals. After tACS intervention, SDNN (t = −3.241, P = 0.002), LF (t = −3.511, P = 0.001), LFn (t = −3.122, P = 0.002), LFn/HFn (−2.928, P = 0.005), TP (t = −2.706, P = 0.008), VLF (t = −3.002, P = 0.004), SD2 (t = −3.594, P = 0.001) and VLI (t = −3.564, P = 0.001) showed a significant increasing trend, and HFn (t = 3.122, P = 0.002), SD1/SD2 (t = 3.158, P = 0.002) and CCM_1 (t = 3.106, P = 0.003) showed a significant decreasing trend. After tDCS intervention, only one feature, TINN, showed a significant upward trend (P < 0.05). The other features showed non-significant changes but roughly the same trend as the tACS group.Conclusion: Both tDCS and α-tACS can be effective in relieving mental fatigue, and α-tACS is more effective than tDCS. This study provides theoretical support for tDCS with α-tACS having a alleviating effect on mental fatigue and the use of ECG as a valid objective assessment tool.

Non-invasive neuromodulation techniques are widely utilized to study and improve cognitive function, with the aim of modulating different cognitive processes. For workers performing high-intensity mental and physical tasks, extreme fatigue may not only affect their working efficiency but may also lead to cognitive decline or cognitive impairment, which, in turn, poses a serious threat to their physical health. The use of non-invasive neuromodulation techniques has important research value for improving and enhancing cognitive function. In this paper, we review the research status, existing problems, and future prospects of transcranial direct current stimulation (tDCS), transcranial alternating current stimulation (tACS), transcranial magnetic stimulation (TMS), and transcutaneous acupoint stimulation (TAS), which are the most studied physical methods in non-invasive neuromodulation techniques to improve and enhance cognition. The findings presented in this paper will be of great reference value for the in-depth study of non-invasive neuromodulation techniques in the field of cognition.

The unique ability of piezoelectric materials to generate electricity spontaneously has attracted widespread interest in the medical field. In addition to the ability to convert mechanical stress into electrical energy, piezoelectric materials offer the advantages of high sensitivity, stability, accuracy and low power consumption. Because of these characteristics, they are widely applied in devices such as sensors, controllers and actuators. However, piezoelectric materials also show great potential for the medical manufacturing of artificial organs and for tissue regeneration and repair applications. For example, the use of piezoelectric materials in cochlear implants, cardiac pacemakers and other equipment may help to restore body function. Moreover, recent studies have shown that electrical signals play key roles in promoting tissue regeneration. In this context, the application of electrical signals generated by piezoelectric materials in processes such as bone healing, nerve regeneration and skin repair has become a prospective strategy. By mimicking the natural bioelectrical environment, piezoelectric materials can stimulate cell proliferation, differentiation and connection, thereby accelerating the process of self-repair in the body. However, many challenges remain to be overcome before these concepts can be applied in clinical practice, including material selection, biocompatibility and equipment design. On the basis of the principle of electrical signal regulation, this article reviews the definition, mechanism of action, classification, preparation and current biomedical applications of piezoelectric materials and discusses opportunities and challenges for their future clinical translation.

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.