Irbm最新文献

Background and objective

Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) aim to detect target frequencies corresponding to specific commands in electroencephalographic (EEG) signals by classification algorithms to achieve the desired control. However, SSVEP signals suffer from low signal-to-noise ratio and large differences in brain activity. Moreover, the existing CNN models have small receptive fields, which make it difficult to receive large range of feature information and limit the effectiveness of classification algorithms.

Methods

To this end, we proposed a high-order temporal convolutional neural network (HOT-CNN) model for enhancing the performance of SSVEP target recognition. Specifically, the SSVEP-EEG signals was divided into equal-length time segments and a time-slice attention module was designed to capture the correlation between time slices. The module improves the local characterization of signals and reduces biological noise interference by automatically assigning high weights to locally relevant temporal sampling cues and lower weights to other temporal cues. Moreover, for global features, a temporal convolutional network module was designed to increases the receptive field of the network and to extract more comprehensive time domain features by using dilated causal convolution. Finally, the fusion and analysis of local and global features are achieved by designing a feature fusion and classification module to accomplish accurate classification of SSVEP signals.

Results

Our method was evaluated on large publicly available datasets containing 35 subjects and 40 categories. Experimental results indicated that HOT-CNN achieved encouraging performance compared with other advanced methods: the highest information transfer rate of 241.01bits/min was obtained using 0.5s stimuli, and the highest average accuracy of 96.39% was obtained using 1.0s stimuli.

Conclusions

The method effectively reinforced the global and local time-domain information and improved the classification performance of SSVEP, which has wide application prospects.

Background

Infectious diseases like COVID-19 pose major global health threats. Robust surveillance systems are needed to swiftly detect and contain outbreaks. This study investigates the integration of Blockchain technology and machine learning to establish a secure and ethically sound approach to tracking infectious diseases.

Methods

We established a Blockchain-based framework for the collection and analysis of epidemiological data while upholding privacy standards. We employed encryption and privacy-enhancing technologies to gather information on case numbers, locations, and disease progression. Artificial neural networks were employed to scrutinize the data and pinpoint transmission patterns. A prototype was specifically designed to work with COVID-19 data from specific countries.

Results

The Blockchain system enabled reliable and tamper-proof data gathering with enhanced transparency. The evaluation showed it allowed cost-effective tracking of infectious diseases while upholding confidentiality safeguards. The neural networks effectively modeled disease spread based on the Blockchain data.

Conclusions

This research demonstrates the viability of Blockchain and machine learning for infectious disease surveillance. The system strikes a balance between public health concerns and personal privacy considerations. It also addresses the challenges of misinformation and accountability gaps during disease outbreaks. Ongoing development can lay the foundation for an ethical framework for digital disease tracking, ensuring both pandemic preparedness and response capabilities are upheld.

Ultrasound is a powerful tool in ophthalmology with a wide range of physical effects that can interact with biological tissue. This ranges from low-intensity linear transducers for diagnosis to high-intensity pulsed or continuous focused ultrasound for therapy. Designing devices for ophthalmological applications requires creating fine focal spots, minimizing heating, and accounting for eye movements. Ultrasound is essential for ophthalmologists to provide accurate diagnosis and quantitative information on tissue composition and blood flow. Ultrasound has revolutionized cataract surgery, making it less invasive and in an outpatient basis, while enhancing the safety and predictability of glaucoma treatment using high-intensity focused ultrasound. The article aims to review the complex and multifaceted bioeffects of ultrasound used in ophthalmology, and its current and future applications of ultrasound in ophthalmology, notably regarding cavitation-mediated drug delivery.

Objectives

Electromyography is one of the few measurement methods that can be implanted, and it has been used in swallowing detection to measure superficial muscles, but has failed to provide satisfactory performances for a real-time detection. Yet, we seek to allow for the feasibility of an implantable active artificial larynx that would protect the airway during swallowing. Therefore, it requires a real-time detection of swallowing through measurements that must provide dedicated and early activity on swallowing, to close the airways soon as possible. In that regard, promising results were published about the stylohyoid and posterior digastric muscles, but no study provided simultaneous and independent measurements. So, this paper aims to evaluate both muscles with intra muscular EMG, in a large set of tasks, to evaluate their recruitment pattern for the feasibility of an implantable active artificial larynx.

Materials and methods

we used intramuscular EMG to measure the stylohyoid and the posterior digastric muscles independently. We also used surface electrodes to measure the submental muscles and provide a basis for comparison. Besides, the swallowing sound measurement method was used to locate the moment the bolus starts to enter the upper esophageal sphincter (UES). That moment defines a temporal limit after which the airway are in danger of aspiration and the temporal evolution of the muscles' is evaluated in comparison to that limit. The onsets and offsets of each muscles were located with a generalized likelihood ratio method, and the UES bolus passage was localized manually after the transformation of the signals with a Teager-Kaiser energy operator. 17 participants were measured, and were asked to perform 4 swallowing tasks and 13 non-swallowing tasks.

Results

we found a strong implication of the stylohyoid for swallowing and mastication. The posterior digastric showed a clear tendency towards swallow-related tasks, and especially swallowing, mastication, open mouth, jaw, and clench teeth. Both muscles provided significant activity before the temporal limit, with a characteristic pattern.

Conclusion

the stylohyoid and the posterior digastric muscles shows a net increase in potential for a detection, compared to the submental muscles, for the feasibility of an implantable active artificial larynx.

Objectives

Inertial Measurement Units (IMUs) are a valid alternative to optical tracking systems for human motion capture, but they are subject to several disturbances that limit their accuracy. We aim to improve the accuracy of elbow joint angle estimation from IMU measurements by introducing a novel postprocessing algorithm that uses anatomical constraints and does not require any prior calibration or knowledge of anthropometric parameters.

Materials and Methods

We propose a new error model that addresses sensor misalignment and fusion errors. We use an error state extended Kalman filter (ESEKF) with state constraints to integrate the anatomical constraints. We validate the proposed algorithm by testing it in different scenarios and comparing it with a state-of-the-art optical tracking system.

Results

The research results highlight the superior performance of the proposed method compared with existing techniques. The study demonstrates a significant reduction in errors, particularly in complex arm movements and under strong external disturbances. The results obtained in the three different tested scenarios underscore the robustness and effectiveness of the developed algorithm, reaching half the error committed by the existing calibration-free correction algorithms proposed in the literature.

Conclusions

The developed technique provides highly accurate estimates of joint angles in several challenging real-world scenarios.

Objectives

Many molecular imaging diagnoses involve comparing two regions of interest (ROIs) in the image or different images. Since the images are obtained by measuring a random phenomenon, such comparisons should be based on a statistical test to ensure reliability. Recent studies have shown that use of the bootstrap approach provides access to the statistical variability of reconstructed values in molecular images. However, although there is general agreement that this increase in information should make diagnosis based on molecular images more reliable, no approach has been proposed in the relevant literature to use bootstrap replicates to enhance the reliability of comparisons of two ROIs. In this paper, we propose to fill this gap by introducing the first statistical test that allows us to compare two sets of pixels/voxels for which bootstrap replicates are available.

Material and methods

After presenting the theoretical basis of this non-parametric statistical test, this article describes how to calculate it in practice. Finally, it proposes two experiments based on quantitative comparisons and expert judgment to assess its relevance.

Results

The results obtained are consistent with expert diagnosis on synthetic data. This validates the relevance of the D-test.

Conclusion

This paper presents the first statistical test to compare two ROIs in reconstructed images for which the statistical variability information is accessible.