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Machine learning techniques for remote healthcare

Machine learning techniques for remote healthcare
Machine learning techniques for remote healthcare
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.
9781461488422
129-172
Springer
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd
Das, Saptarshi
e06f2eb0-1e3e-453c-ba78-82eed18ceac9
Maharatna, Koushik
93bef0a2-e011-4622-8c56-5447da4cd5dd

Das, Saptarshi and Maharatna, Koushik (2014) Machine learning techniques for remote healthcare. In, Systems Design for Remote Healthcare. New York, US. Springer, pp. 129-172.

Record type: Book Section

Abstract

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.

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Published date: 2014
Organisations: Electronic & Software Systems

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Local EPrints ID: 362133
URI: https://eprints.soton.ac.uk/id/eprint/362133
ISBN: 9781461488422
PURE UUID: ad924f71-76ad-4c05-a8ba-2c595bd83629

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Date deposited: 18 Feb 2014 10:34
Last modified: 10 Nov 2017 21:04

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Contributors

Author: Saptarshi Das

University divisions

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