Online anomaly detection for long-term ECG monitoring using wearable devices
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TLDR
This work proposes an online ECG monitoring solution where normal heartbeats of each specific user are modeled by dictionaries yielding sparse representations, andheartbeats that do not conform to this model are detected as anomalous, thus enabling online and long-term monitoring.About:
This article is published in Pattern Recognition.The article was published on 2019-04-01 and is currently open access. It has received 40 citations till now.read more
Citations
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Journal ArticleDOI
A Review on Outlier/Anomaly Detection in Time Series Data
TL;DR: In this paper, a taxonomy is presented based on the main aspects that characterize an outlier detection technique in the context of time series, and a structured and comprehensive state-of-the-art on unsupervised anomaly detection techniques is provided.
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Learning representations of multivariate time series with missing data
Filippo Maria Bianchi,Lorenzo Livi,Lorenzo Livi,Karl Øyvind Mikalsen,Michael Kampffmeyer,Robert Jenssen +5 more
TL;DR: A novel autoencoder architecture based on recurrent neural networks to generate compressed representations of MTS is proposed, which can process inputs characterized by variable lengths and it is specifically designed to handle missing data.
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Mean-shift outlier detection and filtering
TL;DR: In this paper, the authors proposed a mean-shift outlier detector, which replaces every object by the mean of its k-nearest neighbors and detects outliers based on the distance shifted.
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Computational Diagnostic Techniques for Electrocardiogram Signal Analysis.
TL;DR: Latest computational diagnostic techniques based on ECG signals for estimating CVDs conditions are summarized here and show great potential for helping health care professionals and their application in daily life benefits both patients and sub-healthy people.
References
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Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Journal ArticleDOI
A Survey on Transfer Learning
Sinno Jialin Pan,Qiang Yang +1 more
TL;DR: The relationship between transfer learning and other related machine learning techniques such as domain adaptation, multitask learning and sample selection bias, as well as covariate shift are discussed.
Book
Distributed Optimization and Statistical Learning Via the Alternating Direction Method of Multipliers
TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Journal ArticleDOI
PhysioBank, PhysioToolkit, and PhysioNet: components of a new research resource for complex physiologic signals.
Ary L. Goldberger,Luís A. Nunes Amaral,Leon Glass,Jeffrey M. Hausdorff,Plamen Ch. Ivanov,Roger G. Mark,Joseph E. Mietus,George B. Moody,Chung-Kang Peng,H. Eugene Stanley +9 more
TL;DR: The newly inaugurated Research Resource for Complex Physiologic Signals (RRSPS) as mentioned in this paper was created under the auspices of the National Center for Research Resources (NCR Resources).
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$rm K$ -SVD: An Algorithm for Designing Overcomplete Dictionaries for Sparse Representation
TL;DR: A novel algorithm for adapting dictionaries in order to achieve sparse signal representations, the K-SVD algorithm, an iterative method that alternates between sparse coding of the examples based on the current dictionary and a process of updating the dictionary atoms to better fit the data.