BioMedical Engineering OnLine最新文献

Background: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease's burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.

Method: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.

Results: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.

Conclusion: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

Monitoring of ingestive activities is critically important for managing the health and wellness of individuals with various health conditions, including the elderly, diabetics, and individuals seeking better weight control. Monitoring swallowing events can be an ideal surrogate for developing streamlined methods for effective monitoring and quantification of eating or drinking events. Swallowing is an essential process for maintaining life. This seemingly simple process is the result of coordinated actions of several muscles and nerves in a complex fashion. In this study, we introduce automated methods for the detection and quantification of various eating and drinking activities. Wireless surface electromyography (sEMG) was used to detect chewing and swallowing from sEMG signals obtained from the sternocleidomastoid muscle, in addition to signals obtained from a wrist-mounted IMU sensor. A total of 4675 swallows were collected from 55 participants in the study. Multiple methods were employed to estimate bolus volumes in the case of fluid intake, including regression and classification models. Among the tested models, neural networks-based regression achieved an R² of 0.88 and a root mean squared error of 0.2 (minimum bolus volume was 10 ml). Convolutional neural networks-based classification (when considering each bolus volume as a separate class) achieved an accuracy of over 99% using random cross-validation and around 66% using cross-subject validation. Multiple classification methods were also used for solid bolus type detection, including SVM and decision trees (DT), which achieved an accuracy above 99% with random validation and above 94% in cross-subject validation. Finally, regression models with both random and cross-subject validation were used for estimating the solid bolus volume with an R² value that approached 1 and root mean squared error values as low as 0.00037 (minimum solid bolus weight was 3 gm). These reported results lay the foundation for a cost-effective and non-invasive method for monitoring swallowing activities which can be extremely beneficial in managing various chronic health conditions, such as diabetes and obesity.

Background: Electrotransfection is based on application of high-voltage pulses that transiently increase membrane permeability, which enables delivery of DNA and RNA in vitro and in vivo. Its advantage in applications such as gene therapy and vaccination is that it does not use viral vectors. Skeletal muscles are among the most commonly used target tissues. While siRNA delivery into undifferentiated myoblasts is very efficient, electrotransfection of siRNA into differentiated myotubes presents a challenge. Our aim was to develop efficient protocol for electroporation-based siRNA delivery in cultured primary human myotubes and to identify crucial mechanisms and parameters that would enable faster optimization of electrotransfection in various cell lines.

Results: We established optimal electroporation parameters for efficient siRNA delivery in cultured myotubes and achieved efficient knock-down of HIF-1α while preserving cells viability. The results show that electropermeabilization is a crucial step for siRNA electrotransfection in myotubes. Decrease in viability was observed for higher electric energy of the pulses, conversely lower pulse energy enabled higher electrotransfection silencing yield. Experimental data together with the theoretical analysis demonstrate that siRNA electrotransfer is a complex process where electropermeabilization, electrophoresis, siRNA translocation, and viability are all functions of pulsing parameters. However, despite this complexity, we demonstrated that pulse parameters for efficient delivery of small molecule such as PI, can be used as a starting point for optimization of electroporation parameters for siRNA delivery into cells in vitro if viability is preserved.

Conclusions: The optimized experimental protocol provides the basis for application of electrotransfer for silencing of various target genes in cultured human myotubes and more broadly for electrotransfection of various primary cell and cell lines. Together with the theoretical analysis our data offer new insights into mechanisms that underlie electroporation-based delivery of short RNA molecules, which can aid to faster optimisation of the pulse parameters in vitro and in vivo.