Objective. High frequency biphasic (HFB) electrical currents are widely used in nerve blocking studies. Their safety margins largely remain unknown and need to be investigated. Approach. This study, exploring the post stimulus effects of HFB electrical currents on a nerve's conductibility, was performed on bullfrog sciatic nerves. Both compound action potentials (CAPs) and differential CAPs (DCAPs, i.e. control CAPs subtracted by CAPs following HFB currents) were obtained, and N1 and N2 components, which were the first and second upward components of DCAPs, were used for analyses of the effects introduced by HFB electrical stimulation. Main results. First, HFB currents of 10 kHz at a completely blocking threshold were applied for 5 s. The maximum amplitudes and conducting velocities of the CAPs were significantly (P < 0.02) decreased within the observed period (60 s) following HFB currents. The DCAPs displayed clear N1 and N2 components, demonstrating respectively the losses of the fibres' normal conductibility and the appearances of new delayed conductions. Decreases of N1 amplitudes along time, regarded as the recovery of the nerve's conductibility, exhibited two distinct phases: a fast one lasting several seconds and a slow one lasting longer than 5 min. Further tests showed a linear relationship between the HFB stimulation durations and recovering periods of N1 amplitudes. Supra-threshold blocking did not cause higher N1 amplitudes. Significance. This study indicates that HFB electrical currents lead to long lasting post stimulus reduction of a nerve's conductibility, which might relate to potential nerve injuries. A possible mechanism, focusing on changes in intracellular and periaxonal ionic concentrations, was proposed to underlie the reduction of the nerve's conductibility and potential nerve injuries. Greater caution and stimulation protocols with greater safety margins should be explored when utilizing HFB electrical current to block nerve conductions.

The radial basis function (RBF) neural network is a type of universal approximator, and has been widely used in various fields. Improving the training speed and compactness of RBF networks are critical for promoting their applications. In the present study, we propose a simple, fast, and effective RBF networks training method, which is based on the residual extreme points and their neighborhoods (thus called the REN method for short in this paper). The REN method calculates RBF centers and widths through a two-level iterative process, and realizes two main functionalities, namely 1) adding multiple centers within one pass through the whole data set, and 2) calculating RBF widths specifically for each center. The use of this algorithm does not need any parameter adjustments, and the models for approximation or classification can be obtained by only one run. The performance of the proposed REN algorithm is compared with the classic and powerful orthogonal least squares (OLS) algorithm. By reaching the same accuracies, the REN algorithm trains RBF networks 50 and 320 times faster, in the chirp (0˜50 Hz, 2 s, 1 kHz, 2001 samples) and two-dimensional peaks (2401 samples) signal approximation tasks respectively, than the OLS algorithm does, and the number of centers obtained by the REN algorithm is reduced by half. When incorporating the same number of centers, the REN algorithm achieves accuracies up to 3 orders of magnitude higher than the best results obtained by the OLS algorithm. In the classification task of a real discrete breast cancer data, both methods result in accuracies comparable to many existent methods, but the REN algorithm has the advantages of fast training speeds and no requirements for parameter adjustments. The REN algorithm proposed in this study may potentially be used for tasks with large scale of data or applications that require high model performances.

Objective: Predicting epileptic seizures using electroencephalogram (EEG) signals can provide a critical window for intervention to suppress impending seizures. However, the impact of varying interictal and preictal durations on seizure prediction remains unclear and warrants further exploration. Considering this, this study focuses on seizure prediction using intracranial EEG (iEEG) signals with variable interictal and preictal durations. Method: In this work, we discuss six pairwise combinations in total, involving two interictal durations (24 h and 48 h) and three preictal durations (30 min, 60 min and 90 min), during seizure prediction analysis. Furthermore, we also compare three classification strategies: the all-channel strategy (Strategy 1), the single-channel-based majority voting strategy (Strategy 2) and the channel-optimization-based majority voting strategy (Strategy 3), across each pairwise combination. Results: The long-term SWEC-ETHZ iEEG dataset is utilized to evaluate our method. The results reveal that our method achieves up to 87.7% sensitivity, 94.6% specificity, 92.8% accuracy, 0.912 Area Under the Curve (AUC) and 0.858 F1-score at the segment-based level, along with 95.9% sensitivity and 0.05/h false prediction rate (FPR) at the event-based level. Moreover, the overall performance of Strategy 3 surpasses that of Strategies 1 and 2 in each pairwise combination, indicating that channel optimization is beneficial for improving seizure prediction performance. Conclusion: This work shows high effectiveness in predicting seizures using iEEG signals, with the ability to forecast upcoming epileptic events up to 90 min in advance across both 24-h and 48-h interictal durations.

Muscle synergies have emerged as a novel framework that offers a deeper understanding of the physiological basis of motor control and have been extensively applied in various fields. This study aims to investigate muscle synergies, involving the selection of the muscles of the lower limb while running, including the deep muscles. The muscle synergies were identified from the 23 muscle activations generated by the OpenSim simulation and experimented EMG, respectively, and these outcomes were compared. Our results revealed an additional synergy required to achieve a comparable variability accounting for (VAF) levels when considering 23 muscles compared to 10 muscles. This study highlights that the potential contribution of deep muscles to explaining the muscle synergies involved in running. These findings provide valuable insights for a comprehensive understanding of muscle synergy during running and the neuromotor control of human locomotion. Further optimization of the experimental protocols and extraction methods is necessary to enhance the results of muscle synergy analyses and promote their broader application across various fields in the future.

Scholars have recently used various methods to study the human body’s characteristics, propose improvements, and rely on medical data. This research introduces a new real−time data collection and analysis system comprising two components: a smart mask for data collection and a processing unit for analysis and storage. The medical mask incorporates four sensors: three heat sensors to measure the nose tip and exhaled air temperature from the nostrils and mouth, along with a microphone for audio data. Collected data is instantly transmitted to the processing unit. Temperature readings are labeled and stored for analysis, while audio data is classified into breathing, coughing, and speaking categories using the Support Vector Machine (SVM) algorithm. The SVM’s performance is further optimized using the Lion Algorithm. Evaluation results indicate satisfactory accuracy for temperature data collected from the mask. The SVM achieved 84.3% accuracy for sound data classification, while the Lion algorithm−optimized SVM achieved an even higher accuracy of 93.6%. Future work aims to establish a reliable data collection tool that will serve as the foundation for developing an intelligent human body analysis model called the real human body neural network (rhBNN+).