Healthcare Big Data Voice Pathology Assessment Framework
In: IEEE Access, Jg. 4 (2016), S. 7806-7815
Online
academicJournal
Zugriff:
The fast-growing healthcare big data plays an important role in healthcare service providing. Healthcare big data comprise data from different structured, semi-structured, and unstructured sources. These data sources vary in terms of heterogeneity, volume, variety, velocity, and value that traditional frameworks, algorithms, tools, and techniques are not fully capable of handling. Therefore, a framework is required that facilitates collection, extraction, storage, classification, processing, and modeling of this vast heterogeneous volume of data. This paper proposes a healthcare big data framework using voice pathology assessment (VPA) as a case study. In the proposed VPA system, two robust features, MPEG-7 low-level audio and the interlaced derivative pattern, are used for processing the voice or speech signals. The machine learning algorithms in the form of a support vector machine, an extreme learning machine, and a Gaussian mixture model are used as the classifier. In the experiments, the proposed VPA system shows its efficiency in terms of accuracy and time requirement.
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Healthcare Big Data Voice Pathology Assessment Framework
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Autor/in / Beteiligte Person: | M. Shamim Hossain ; Muhammad, Ghulam |
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Zeitschrift: | IEEE Access, Jg. 4 (2016), S. 7806-7815 |
Veröffentlichung: | IEEE, 2016 |
Medientyp: | academicJournal |
ISSN: | 2169-3536 (print) |
DOI: | 10.1109/ACCESS.2016.2626316 |
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