Machine Learning Techniques for Remote Healthcare

  • Saptarshi DasEmail author
  • Koushik Maharatna


In this chapter, popular machine learning techniques are discussed in the context of remote healthcare. In this domain the main challenges are low computational complexity and hardware implementation, and not just conventional way of mathematical analysis of machine learning algorithms. Statistical view-point of different machine learning techniques, standard parametric and nonparametric algorithms for classification and clustering are briefly discussed. A practical 12-lead Electrocardiogram (ECG) signal based myocardial scar classification example has also been shown as a representative example. Complexity of few classification algorithms, online implementation issues for statistical feature extraction and some open research problems have also been introduced briefly.


Feature Vector Linear Discriminant Analysis Empirical Mode Decomposition Biomedical Signal Quadratic Discriminant Analysis 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.School of Electronics and Computer Science, University of SouthamptonSouthamptonUK

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