Practical Lessons on 12-Lead ECG Classification: Meta-Analysis of Methods From PhysioNet/Computing in Cardiology Challenge 2020

Cardiovascular diseases (CVDs) are one of the most fatal disease groups worldwide. Electrocardiogram (ECG) is a widely used tool for automatically detecting cardiac abnormalities, thereby helping to control and manage CVDs. To encourage more multidisciplinary researches, PhysioNet/Computing in Cardiology Challenge 2020 (Challenge 2020) provided a public platform involving multi-center databases and automatic evaluations for ECG classification tasks. As a result, 41 teams successfully submitted their solutions and were qualified for rankings. Although Challenge 2020 was a success, there has been no in-depth methodological meta-analysis of these solutions, making it difficult for researchers to benefit from the solutions and results. In this study, we aim to systematically review the 41 solutions in terms of data processing, feature engineering, model architecture, and training strategy. For each perspective, we visualize and statistically analyze the effectiveness of the common techniques, and discuss the methodological advantages and disadvantages. Finally, we summarize five practical lessons based on the aforementioned analysis: (1) Data augmentation should be employed and adapted to specific scenarios; (2) Combining different features can improve performance; (3) A hybrid design of different types of deep neural networks (DNNs) is better than using a single type; (4) The use of end-to-end architectures should depend on the task being solved; (5) Multiple models are better than one. We expect that our meta-analysis will help accelerate the research related to ECG classification based on machine-learning models.

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Genetic Markers of Toxicity From Capecitabine and Other Fluorouracil-Based Regimens: Investigation in the QUASAR2 Study, Systematic Review, and Meta-Analysis

Journal of Clinical Oncology ◽

10.1200/jco.2013.51.1857 ◽

2014 ◽

Vol 32 (10) ◽

pp. 1031-1039 ◽

Cited By ~ 137

Author(s):

Dan Rosmarin ◽

Claire Palles ◽

David Church ◽

Enric Domingo ◽

Angela Jones ◽

...

Keyword(s):

Odds Ratio ◽

Literature Search ◽

Meta Analysis ◽

Good Evidence ◽

Systematic Literature Search ◽

Genetic Biomarkers ◽

The Common ◽

Commercial Kits ◽

Combination Regimens ◽

Better Than

Purpose Fluourouracil (FU) is a mainstay of chemotherapy, although toxicities are common. Genetic biomarkers have been used to predict these adverse events, but their utility is uncertain. Patients and Methods We tested candidate polymorphisms identified from a systematic literature search for associations with capecitabine toxicity in 927 patients with colorectal cancer in the Quick and Simple and Reliable trial (QUASAR2). We then performed meta-analysis of QUASAR2 and 16 published studies (n = 4,855 patients) to examine the polymorphisms in various FU monotherapy and combination therapy regimens. Results Global capecitabine toxicity (grades 0/1/2 v grades 3/4/5) was associated with the rare, functional DPYD alleles 2846T>A and *2A (combined odds ratio, 5.51; P = .0013) and with the common TYMS polymorphisms 5′VNTR2R/3R and 3′UTR 6bp ins-del (combined odds ratio, 1.31; P = 9.4 × 10−6). There was weaker evidence that these polymorphisms predict toxicity from bolus and infusional FU monotherapy. No good evidence of association with toxicity was found for the remaining polymorphisms, including several currently included in predictive kits. No polymorphisms were associated with toxicity in combination regimens. Conclusion A panel of genetic biomarkers for capecitabine monotherapy toxicity would currently comprise only the four DPYD and TYMS variants above. We estimate this test could provide 26% sensitivity, 86% specificity, and 49% positive predictive value—better than most available commercial kits, but suboptimal for clinical use. The test panel might be extended to include additional, rare DPYD variants functionally equivalent to *2A and 2846A, though insufficient evidence supports its use in bolus, infusional, or combination FU. There remains a need to identify further markers of FU toxicity for all regimens.

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Correlational Neural Networks

Neural Computation ◽

10.1162/neco_a_00801 ◽

2016 ◽

Vol 28 (2) ◽

pp. 257-285 ◽

Cited By ~ 33

Author(s):

Sarath Chandar ◽

Mitesh M. Khapra ◽

Hugo Larochelle ◽

Balaraman Ravindran

Keyword(s):

Canonical Correlation ◽

State Of The Art ◽

Representation Learning ◽

Advantages And Disadvantages ◽

Common Representation ◽

Series Of Experiments ◽

The Common ◽

Cross Language ◽

Joint Representation ◽

Better Than

Common representation learning (CRL), wherein different descriptions (or views) of the data are embedded in a common subspace, has been receiving a lot of attention recently. Two popular paradigms here are canonical correlation analysis (CCA)–based approaches and autoencoder (AE)–based approaches. CCA-based approaches learn a joint representation by maximizing correlation of the views when projected to the common subspace. AE-based methods learn a common representation by minimizing the error of reconstructing the two views. Each of these approaches has its own advantages and disadvantages. For example, while CCA-based approaches outperform AE-based approaches for the task of transfer learning, they are not as scalable as the latter. In this work, we propose an AE-based approach, correlational neural network (CorrNet), that explicitly maximizes correlation among the views when projected to the common subspace. Through a series of experiments, we demonstrate that the proposed CorrNet is better than AE and CCA with respect to its ability to learn correlated common representations. We employ CorrNet for several cross-language tasks and show that the representations learned using it perform better than the ones learned using other state-of-the-art approaches.

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PENGARUH MODEL DAN STRATEGI PELATIHAN TERHADAP MUTU PELAYANAN DOSEN

Jurnal Teknologi Pendidikan (JTP) ◽

10.24114/jtp.v5i1.481 ◽

2013 ◽

Vol 5 (1) ◽

Author(s):

Efendi Napitupulu

Keyword(s):

Service Quality ◽

Instructional Quality ◽

T Test ◽

Quality Model ◽

Conventional Model ◽

Training Strategy ◽

The Individual ◽

And Training ◽

Better Than

The objectives of this research were to determine the effects of model and training strategy on lecturer service quality. Two models of lecturer instructional quality improvement were applied in this research: the quality oriented and conventional models, while training strategy was divided into cooperative and indivualistic strategy. This research was experimental design used was the 2x2 factorial design. A questionnaire was administered to collect data on lecturer service quality consisting of 58 items (r= .98). ANAVA and t-test was used to analyze the data, t-test were used to analyze the differences between experimental groups at .05 level of significance. The result of this study indicated that (1) In general it can be concluded, that the quality of the lecturer service quality in the classroom, the quality oriented model was better than that of the conventional model; (2) In the cooperative strategy, the quality- oriented model was better than the conventional model; (3) In the individual strategy, the quality-orientedmodel was better than the conventional model, (4) there was no interaction effects between lecturer instuction quality model and training strategy on lecturer service quality. From the research findings it can be concluded that: The service quality of the trained lecturer which was quality-oriented model was better than that of the conventional model, whichever the strategy was used to improved the instructional quality of the lecturer. In other words, training to improved the lecturer instructional quality in the classroom, the model which stressed quality was better than model which did not stres quality.

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Deep Learning Approach for Automatic Classification of Ocular and Cardiac Artifacts in MEG Data

Journal of Engineering ◽

10.1155/2018/1350692 ◽

2018 ◽

Vol 2018 ◽

pp. 1-10 ◽

Cited By ~ 8

Author(s):

Ahmad Hasasneh ◽

Nikolas Kampel ◽

Praveen Sripad ◽

N. Jon Shah ◽

Jürgen Dammers

Keyword(s):

Classification Scheme ◽

Data Augmentation ◽

High Reliability ◽

Combined Model ◽

Independent Components ◽

Artifact Rejection ◽

Auxiliary Signal ◽

Electrocardiogram Ecg ◽

And Training

We propose an artifact classification scheme based on a combined deep and convolutional neural network (DCNN) model, to automatically identify cardiac and ocular artifacts from neuromagnetic data, without the need for additional electrocardiogram (ECG) and electrooculogram (EOG) recordings. From independent components, the model uses both the spatial and temporal information of the decomposed magnetoencephalography (MEG) data. In total, 7122 samples were used after data augmentation, in which task and nontask related MEG recordings from 48 subjects served as the database for this study. Artifact rejection was applied using the combined model, which achieved a sensitivity and specificity of 91.8% and 97.4%, respectively. The overall accuracy of the model was validated using a cross-validation test and revealed a median accuracy of 94.4%, indicating high reliability of the DCNN-based artifact removal in task and nontask related MEG experiments. The major advantages of the proposed method are as follows: (1) it is a fully automated and user independent workflow of artifact classification in MEG data; (2) once the model is trained there is no need for auxiliary signal recordings; (3) the flexibility in the model design and training allows for various modalities (MEG/EEG) and various sensor types.

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Scanning Secondary Electron Microscopy of Myofibrils Using Transmission Techniques

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100116401 ◽

1975 ◽

Vol 33 ◽

pp. 556-557

Author(s):

M. K. Lamvik

Keyword(s):

Electron Microscopy ◽

Electron Microscope ◽

Carbon Films ◽

Photographic Recording ◽

Transmission Electron ◽

Thin Carbon ◽

The Common ◽

Scanning Electron ◽

Better Than ◽

Cellular Organelles

When observing small objects such as cellular organelles by scanning electron microscopy, it is often valuable to use the techniques of transmission electron microscopy. The common practice of mounting and coating for SEM may not always be necessary. These possibilities are illustrated using vertebrate skeletal muscle myofibrils.Micrographs for this study were made using a Hitachi HFS-2 scanning electron microscope, with photographic recording usually done at 60 seconds per frame. The instrument was operated at 25 kV, with a specimen chamber vacuum usually better than 10-7 torr. Myofibrils were obtained from rabbit back muscle using the method of Zak et al. To show the component filaments of this contractile organelle, the myofibrils were partially disrupted by agitation in a relaxing medium. A brief centrifugation was done to clear the solution of most of the undisrupted myofibrils before a drop was placed on the grid. Standard 3 mm transmission electron microscope grids covered with thin carbon films were used in this study.

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The Validity of Specific Cognitive Abilities for the Prediction of Training Success in Germany

Journal of Personnel Psychology ◽

10.1027/1866-5888/a000110 ◽

2014 ◽

Vol 13 (3) ◽

pp. 123-133 ◽

Cited By ~ 11

Author(s):

Wiebke Goertz ◽

Ute R. Hülsheger ◽

Günter W. Maier

Keyword(s):

Cognitive Abilities ◽

Personnel Selection ◽

Meta Analysis ◽

Mental Ability ◽

Occupational Category ◽

General Mental Ability ◽

Training Success ◽

Specific Cognitive Abilities ◽

Better Than

General mental ability (GMA) has long been considered one of the best predictors of training success and considerably better than specific cognitive abilities (SCAs). Recently, however, researchers have provided evidence that SCAs may be of similar importance for training success, a finding supporting personnel selection based on job-related requirements. The present meta-analysis therefore seeks to assess validities of SCAs for training success in various occupations in a sample of German primary studies. Our meta-analysis (k = 72) revealed operational validities between ρ = .18 and ρ = .26 for different SCAs. Furthermore, results varied by occupational category, supporting a job-specific benefit of SCAs.

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The interaction between ability and training structure: A meta-analysis

PsycEXTRA Dataset ◽

10.1037/e518392013-587 ◽

2010 ◽

Cited By ~ 1

Author(s):

Vanessa K. Kowollik ◽

Eric A. Day ◽

Xiaoqian Wang ◽

Matthew J. Schuelke ◽

Michael G. Hughes

Keyword(s):

Meta Analysis ◽

Training Structure ◽

And Training

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The Science of Wisdom in a Polarized World: Knowns and Unknowns

10.31234/osf.io/w9ygc ◽

2020 ◽

Cited By ~ 1

Author(s):

Igor Grossmann ◽

Nic M. Weststrate ◽

Monika Ardelt ◽

Justin Peter Brienza ◽

Mengxi Dong ◽

...

Keyword(s):

Individual Differences ◽

Problem Solving ◽

Best Practices ◽

Cognitive Sciences ◽

Methodological Challenges ◽

The Common ◽

Empirical Approaches ◽

And Training ◽

Contextual Differences

Interest in wisdom in the cognitive sciences, psychology, and education has been paralleled by conceptual confusions about its nature and assessment. To clarify these issues and promote consensus in the field, wisdom researchers met in Toronto in July of 2019, resolving disputes through discussion. Guided by a survey of scientists who study wisdom-related constructs, we established a common wisdom model, observing that empirical approaches to wisdom converge on the morally-grounded application of metacognition to reasoning and problem-solving. After outlining the function of relevant metacognitive and moral processes, we critically evaluate existing empirical approaches to measurement and offer recommendations for best practices. In the subsequent sections, we use the common wisdom model to selectively review evidence about the role of individual differences for development and manifestation of wisdom, approaches to wisdom development and training, as well as cultural, subcultural, and social-contextual differences. We conclude by discussing wisdom’s conceptual overlap with a host of other constructs and outline unresolved conceptual and methodological challenges.

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