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  1. 5081
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    A unified approach for characterizing static/dynamic connectivity frequency profiles using filter banks
  2. 5082
    “…The results are evaluated on the MIT-BIH dataset and achieved an accuracy of 97.6% (using Cubic Support Vector Machine as a final stage classifier).…”
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    From ECG signals to images: a transformation based approach for deep learning
  3. 5083
    “…The symposium was hosted by Harvard Business School and the Harvard MIT Center for Regulatory Science, and included a range of speakers and attendees from industry, government, and academics. …”
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    Ushering in safe, effective, secure, and ethical medicine in the digital era
  4. 5084
    “…As a generally available set of standard test material, the MIT-BIH (Massachusetts Institute of Technology-Beth Israel Hospital) Arrhythmia Database is used to evaluate the proposed method, and the experiment takes 99.7% accuracy, 97.45% sensitivity, and 99.87% specificity. …”
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    Automatic Premature Ventricular Contraction Detection Using Deep Metric Learning and KNN
  5. 5085
    “…We evaluate the performance of the algorithm using the MIT-BIH Arrhythmia Database. The proposed model achieves an overall sensitivity of 99.64%, positive predictivity of 99.71%, and detection error rate of 0.19 for the manually defined constraint graph and overall sensitivity of 99.76%, positive predictivity of 99.68%, and detection error rate of 0.55 for the automatic learning constraint graph.…”
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    A greedy graph search algorithm based on changepoint analysis for automatic QRS complex detection
  6. 5086
    “…AVAILABILITY AND IMPLEMENTATION: ProkSeq is implemented in Python and is published under the MIT source license. The pipeline is available as a Docker container https://hub.docker.com/repository/docker/snandids/prokseq-v2.0, or can be used through Anaconda: https://anaconda.org/snandiDS/prokseq. …”
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    ProkSeq for complete analysis of RNA-Seq data from prokaryotes
  7. 5087
    “…AVAILABILITY AND IMPLEMENTATION: EM-stellar (code and data) is written in Python and is freely available under MIT license on GitHub (https://github.com/cellsmb/em-stellar). …”
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    EM-stellar: benchmarking deep learning for electron microscopy image segmentation
  8. 5088
    “…AeQTL is implemented in Python and the source code is freely available at https://github.com/Huang-lab/AeQTL under the MIT license.…”
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    AeQTL: eQTL analysis using region-based aggregation of rare genomic variants
  9. 5089
    “…The source code of svmATAC is available at https://github.com/mrcuizhe/svmATAC under the MIT license.…”
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    Enhancement and Imputation of Peak Signal Enables Accurate Cell-Type Classification in scATAC-seq
  10. 5090
    “…BSBolt is available at https://github.com/NuttyLogic/BSBolt under an MIT license.…”
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    BiSulfite Bolt: A bisulfite sequencing analysis platform
  11. 5091
    “…The proposed method was evaluated on the MIT-BIH arrhythmia database which is a publicly available ECG dataset collected from 47 patients. …”
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    Interpatient ECG Heartbeat Classification with an Adversarial Convolutional Neural Network
  12. 5092
    por Avsar, Korkut
    Publicado 2021
    “…Damit ist in vielen Teilen der Welt die Erreichung dieser Ziele gefährdet und es wird sogar mit zunehmenden Fallzahlen in den nächsten Jahren gerechnet. …”
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    Tuberkulose - Wann daran denken, wie diagnostizieren?
  13. 5093
    “…CytoPy is open-source and licensed under the MIT license. CytoPy is available at https://github.com/burtonrj/CytoPy, with notebooks accompanying this manuscript (https://github.com/burtonrj/CytoPyManuscript) and software documentation at https://cytopy.readthedocs.io/.…”
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    CytoPy: An autonomous cytometry analysis framework
  14. 5094
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    Gradients of connectivity as graph Fourier bases of brain activity
  15. 5095
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    Long-term stability of avalanche scaling and integrative network organization in prefrontal and premotor cortex
  16. 5096
    “…Raw ECG data are extracted from 10 subjects and ECG data are also taken from the MIT BIH database for the proposed method. RESULTS: The performance comparison of traditional variational mode decomposition (VMD) and the proposed technique is also calculated using mean square error, percentage root mean square difference, signal to noise ratio and correlation coefficient. …”
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    Electrocardiogram Signals Denoising Using Improved Variational Mode Decomposition
  17. 5097
    “…The training, optimization, and evaluation of the proposed CNN are based on the CHB-MIT dataset. The CNN reaches a median sensitivity of 90% and a very high specificity over 99% corresponding to a median false positive rate of 6.8 s per hour. …”
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    Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network
  18. 5098
    “…Individual changes in resting heart rate variability (HRV) parameters were assessed in seven Polish cyclists during a training process consisting of: a six-week period (P1) of predominantly low- and moderate-intensity training (L-MIT) and a six-week period (P2) where the proportion of high-intensity interval training (HIT) increased. …”
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    An Attempt to Predict Changes in Heart Rate Variability in the Training Intensification Process among Cyclists
  19. 5099
    “…The metastable phase VO(2)(B), obtained via a hydrothermal route, was converted into the stable VO(2)(M1), which shows a metal–insulator transition (MIT) at 68 °C that is useful for different applications, from energy-efficient smart windows to dynamic concrete. …”
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    Systematic Exploration of the Synthetic Parameters for the Production of Dynamic VO(2)(M1)
  20. 5100
    por Jin, Wen, Nobre, Anna C., van Ede, Freek
    Publicado 2020
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    Temporal Expectations Prepare Visual Working Memory for Behavior
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