Hamiltonbasse1201

SARS-CoV-2 analysis employing individual pluripotent base tissues along with organoids.

From the results, we observed that pitch synchronous segmentation yields better classification performance compared to fixed window based segmentation. The results of this analysis support our hypothesis that pitch synchronous segmentation is better suited for PD classification using connected speech.Clinical Relevance- The automatic speech analysis framework used in this analysis establishes the greater efficiency of pitch synchronous segmentation over the traditional methods.A 24GHz Doppler radar system for accurate contactless monitoring of heart and respiratory rates is demonstrated here. High accuracy predictions are achieved by employing a CNN+LSTM neural network architecture for regression analysis. Detection accuracies of 99% and 98% have been attained for heart rate and respiration rate, respectively.Clinical Relevance- This work establishes a non-contact radar system with 99% detection accuracy for a heart rate variability warning system. This system can enable convenient and fast monitoring for daily care at home.Upper gastrointestinal (GI) disorders are highly prevalent, with gastroparesis (GP) and functional dyspepsia (FD) affecting 3% and 10% of the US population, respectively. Despite overlapping symptoms, differing etiologies of GP and FD have distinct optimal treatments, thus making their management a challenge. One such cause, that of gastric slow wave abnormalities, affects the electromechanical coordination of pacemaker cells and smooth muscle cells in propelling food through the GI tract. Abnormalities in gastric slow wave initiation location and propagation patterns can be treated with novel pacing technologies but are challenging to identify with traditional spectral analyses from cutaneous recordings due to their occurrence at the normal slow wave frequency. This work advances our previous work in developing a 3D convolutional neural network to process multi-electrode cutaneous recordings and successfully classify, in silico, normal versus abnormal slow wave location and propagation patterns. Here, we use transfer learning to build a method that is robust to heterogeneity in both the location of the abnormal initiation on the stomach surface as well as the recording start times with respect to slow wave cycles. We find that by starting with training lowest-complexity models and building complexity in training sets, transfer learning one model to the next, the final network exhibits, on average, 80% classification accuracy in all but the most challenging spatial abnormality location, and below 5% Type-I error probabilities across all locations.Non-invasive health monitoring has the potential to improve the delivery and efficiency of medical treatment.

This study was aimed at developing a neural network to classify the lung volume state of a subject (i.e. high lung volume (HLV) or low lung volume (LLV), where the subject had fully inhaled or exhaled, respectively) by analyzing cardiac cycles extracted from vibrational cardiography (VCG) signals.

A total of 15619 cardiac cycles were recorded from 50 subjects, of which 9989 cycles were recorded in the HLV state and the remaining 5630 cycles were recorded in the LLV state. A 1D convolutional neural network (CNN) was employed to classify the lung volume state of these cardiac cycles.

The CNN model was evaluated using a train/test split of 80/20 on the data. The developed model was able to correctly classify the lung volume state of 99.4% of the testing data.

VCG cardiac cycles can be classified based on lung volume state using a CNN.

These results provide evidence of a correlation between VCG and respiration volume, which could inform further analysis into VCG-based cardio-respiratory monitoring.

These results provide evidence of a correlation between VCG and respiration volume, which could inform further analysis into VCG-based cardio-respiratory monitoring.Independent Component Analysis (ICA) has became the most popular method to remove eye-blinking artifacts from electroencephalogram (EEG) recording. For long term EEG recording, ICA was commonly considered to costing a lot of computation time. TL13-112 Furthermore, with no ground truth, the discussion about the quality of ICA decomposition in a nonstationary environment was specious. In this study, we investigated the "signal" (P300 waveform) and the "noise" (averaged eye-blinking artifacts) on a cross-modal long-term EEG recording to evaluate the efficiency and effectiveness of different methods on ICA eye-blinking artifacts removal. As a result, it was found that, firstly, down sampling is an effective way to reduce the computation time in ICA. Appropriate down sampling ratio could speed up ICA computation 200 times and keep the decomposition performance stable, in which the computation time of ICA decomposition on a 2800 s EEG recording was less than 5 s. Secondly, dimension reduction by PCA was also a way to improve the efficiency and effectiveness of ICA. TL13-112 Finally, the comparison by cropping the dataset indicated that performing ICA on each run of the experiment separately would achieve a better result for eye-blinking artifacts removal than using all the EEG data input for ICA.For the extraction of underlying sources of brain activity, time structure-based techniques for applying Independent Component Analysis (ICA) have been demonstrably more robust than state-of-the-art statistical-based methods, such as FastICA. Since the early application of conventional ICA on electroencephalogram (EEG) recordings, Space-Time ICA (ST-ICA) has emerged as more capable approach for extracting complex underlying activity, but not without the 'curse of dimensionality'. The challenges in the future development of ST-ICA will require a focus on the optimisation of the mixing matrix, and on component clustering techniques. This paper proposes a new optimisation approach for the mixing matrix, which makes ST-ICA more tractable, when using a time structure-based ICA technique, LSDIAG. Such techniques rely on constructing a multi-layer covariance matrix, Cxk of the original dataset to generate the inverse of the mixing matrix; Csk = WCxkWT. This means a simple truncation of the mixing matrix is not appropriate.