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Air conditioning technique assault diagnosis dataset.
Sleep high quality as well as glycaemic variability inside a real-life establishing grownups using your body.
001). In particular, RiSC improved HF difference the most for datasets with the most severe outlier contamination.EEG signal classification is an important task to build an accurate Brain Computer Interface (BCI) system. Many machine learning and deep learning approaches have been used to classify EEG signals. Besides, many studies have involved the time and frequency domain features to classify EEG signals. On the other hand, a very limited number of studies combine the spatial and temporal dimensions of the EEG signal. Brain dynamics are very complex across different mental tasks, thus it is difficult to design efficient algorithms with features based on prior knowledge. Therefore, in this study, we utilized the 2D AlexNet Convolutional Neural Network (CNN) to learn EEG features across different mental tasks without prior knowledge. First, this study adds spatial and temporal dimensions of EEG signals to a 2D EEG topographic map. Second, topographic maps at different time indices were cascaded to populate a 2D image for a given time window. Finally, the topographic maps enabled the AlexNet to learn features from the spatial and temporal dimensions of the brain signals. The classification performance was obtained by the proposed method on a multiclass dataset from BCI Competition IV dataset 2a. The proposed system obtained an average classification accuracy of 81.09%, outperforming the previous state-of-the-art methods by a margin of 4% for the same dataset. The results showed that converting the EEG classification problem from a (1D) time series to a (2D) image classification problem improves the classification accuracy for BCI systems. Also, our EEG topographic maps enabled CNN to learn subtle features from spatial and temporal dimensions, which better represent mental tasks than individual time or frequency domain features.In this paper, we review several advances in different fields that provide new potential for brain-computer interfaces enabled by directly interfacing biological neural networks with electrodes, including recent successes with liquid injected conductive channels and mesh electronics supported by 3D scaffolds. Based on this review, it is clear that the success of biological neural connectivity is dependent on the precision and density of the inserted electrodes. In order to better understand the dynamics of this relationship, we propose a simple impedance-based electrode connectivity model, based on which we perform a simulation of the impact of both electrode density and electrode precision on the amount of information lost as part of the connection. Although the examples illustrated are more informative rather than conclusive, the fundamental takeaway from this work is that electrode density is a substantially important parameter while electrode precision is necessarily helpful.Catheter ablation is a common treatment of atrial fibrillation (AF), but its success rate is around 60%. selleck kinase inhibitor selleck kinase inhibitor It is believed that the success rate can be improved if the procedure were to be guided by the specific AF triggers found in the "Flashback", i.e. the trend of around 500 ventricular beats preceding the AF onset stored in an implantable cardiac monitor (ICM). The need to automatically classify these different triggers atrial tachycardia (AT), atrial flutter, premature atrial contractions (PAC) or Spontaneous AF has motivated the design in this paper of an unsupervised classification method evaluating statistical and geometrical Heart Rate Variability (HRV) features extracted from the Flashback. From a cohort of 132 patients (57± 12 years, male 67%), 528 Flashbacks were extracted and classified into 5 different clusters after the Principal Component Analysis (PCA) was computed on the HRV features. 2 principal components explained more than 95% of the variance and were a combination of the mean R-R interval, Square root of the mean squared differences of successive R-R intervals (RMSSD), Standard deviation of the R-R intervals (SDNN) and Poincare descriptors, SD1 and SD2. RMSSD and SD1 were significantly different among all clusters (p-value less then 0.05, with Holm's correction) showing that distinct patterns can be found using this method.Clinical Relevance-Preliminary step towards ablation strategy guidance using the AF trigger patterns to improve catheter ablation success rates.Recent developments in wearable sensors demonstrate promising results for monitoring physiological status in effective and comfortable ways. One major challenge of physiological status assessment is the problem of transfer learning caused by the domain inconsistency of biosignals across users or different recording sessions from the same user. We propose an adversarial inference approach for transfer learning to extract disentangled nuisance-robust representations from physiological biosignal data in stress status level assessment. We exploit the trade-off between task-related features and person-discriminative information by using both an adversary network and a nuisance network to jointly manipulate and disentangle the learned latent representations by the encoder, which are then input to a discriminative classifier. Results on cross-subjects transfer evaluations demonstrate the benefits of the proposed adversarial framework, and thus show its capabilities to adapt to a broader range of subjects. Finally we highlight that our proposed adversarial transfer learning approach is also applicable to other deep feature learning frameworks.The multi-label electrocardiogram (ECG) classification is to automatically predict a set of concurrent cardiac abnormalities in an ECG record, which is significant for clinical diagnosis. Modeling the cardiac abnormality dependencies is the key to improving classification performance. To capture the dependencies, we proposed a multi-label classification method based on the weighted graph attention networks. In the study, a graph taking each class as a node was mapped and the class dependencies were represented by the weights of graph edges. A novel weights generation method was proposed by combining the self-attentional weights and the prior learned co-occurrence knowledge of classes. The algorithm was evaluated on the dataset of the Hefei Hi-tech Cup ECG Intelligent Competition for 34 kinds of ECG abnormalities classification. And the micro-f 1 and the macro-f 1 of cross validation respectively were 91.45% and 44.48%. The experiment results show that the proposed method can model class dependencies and improve classification performance.
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