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- Peng, Xiangdong, et al.
(author)
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DSCSSA: A Classification Framework for Spatiotemporal Features Extraction of Arrhythmia Based on the Seq2Seq Model With Attention Mechanism
- 2022
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In: IEEE Transactions on Instrumentation and Measurement. - : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9456 .- 1557-9662. ; 71
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Journal article (peer-reviewed)abstract
- In the field of arrhythmia classification, classification accuracy has always been a research hotspot. However, the noises of electrocardiogram (ECG) signals, the class imbalance of ECG data, and the complexity of spatiotemporal features of ECG data are all important factors affecting the accuracy of ECG arrhythmias classification. In this article, a novel DSCSSA ECG arrhythmias classification framework is proposed. First, discretewavelet transform (DWT) is used to denoise and reconstruct ECG signals to improve the feature extraction ability of ECG signals.Then, the synthetic minority oversampling technique (SMOTE) oversampling method is used to synthesize a new minority sample ECG signal to reduce the impact of ECG data imbalance on classification. Finally, a convolutional neural network (CNN) and sequence-to-sequence (Seq2Seq) classification model with attention mechanism based on bi directional long short-term memory(Bi-LSTM) as the codec is used for arrhythmias classification, and the model can give corresponding weight according to the importance of heartbeat features and can improve the ability toextract and filter the spatiotemporal features of heartbeats. In the classification of five heartbeat types, including normal beat (N), supraventricular ectopic beat (S), ventricular ectopic beat (V),fusion beat (F), and unknown beat (Q), the proposed method achieved the overall accuracy (OA) value and Macro-F1 score of 99.28% and 95.70%, respectively, in public the Massachusetts Institute of Technology - Boston’s Beth Israel Hospital (MIT-BIH)arrhythmia database. These methods are helpful to improve the effectiveness and clinical reference value of computer-aided ECG automatic classification diagnosis.
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| 2. |
- Wang, Guo-dong, et al.
(author)
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The genomics of selection in dogs and the parallel evolution between dogs and humans
- 2013
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In: Nature Communications. - : Nature Publishing Group. - 2041-1723. ; 4, s. 1860-
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Journal article (peer-reviewed)abstract
- The genetic bases of demographic changes and artificial selection underlying domestication are of great interest in evolutionary biology. Here we perform whole-genome sequencing of multiple grey wolves, Chinese indigenous dogs and dogs of diverse breeds. Demographic analysis show that the split between wolves and Chinese indigenous dogs occurred 32,000 years ago and that the subsequent bottlenecks were mild. Therefore, dogs may have been under human selection over a much longer time than previously concluded, based on molecular data, perhaps by initially scavenging with humans. Population genetic analysis identifies a list of genes under positive selection during domestication, which overlaps extensively with the corresponding list of positively selected genes in humans. Parallel evolution is most apparent in genes for digestion and metabolism, neurological process and cancer. Our study, for the first time, draws together humans and dogs in their recent genomic evolution.
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| 3. |
- Zhou, Fengyi, et al.
(author)
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Emergence of polar skyrmions in 2D Janus CrInX3 (X=Se, Te) magnets
- 2024
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In: NPJ 2D MATERIALS AND APPLICATIONS. - : Springer Nature. - 2397-7132. ; 8:1
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Journal article (peer-reviewed)abstract
- In the realm of multiferroicity in 2D magnets, whether magnetic and polar skyrmions can coexist within a single topological entity has emerged as an important question. Here, we study Janus 2D magnets CrInX3 (X=Se, Te) for a comprehensive investigation of the magnetic ground state, magnetic excited state, and corresponding ferroelectric polarization by first-principles electronic structure calculations and Monte Carlo simulations. Specifically, we have thoroughly elucidated the magnetic exchange mechanisms, and have fully exemplified the magnetic field dependence of the magnon spectrum. More importantly, our study reveals a previously unrecognized, remarkably large spin-spiral-induced ferroelectric polarization (up to 194.9 mu C/m(2)) in both compounds. We propose an approach to identify polar skyrmions within magnetic skyrmions, based on the observed direct correlation between spin texture and polarization density. Elucidating this correlation not only deepens our understanding of magnetic skyrmions but also paves the way for innovative research in the realm of multiferroic skyrmions.
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