RA-SAX: Resource-Aware Symbolic Aggregate Approximation for Mobile ECG Analysis

Authors:
Hossein Tayebi;Shonali Krishnaswamy;Augustinus Borgy Waluyo;Abhijat Sinha;Mohamed Medhat Gaber
Affiliations:
-;-;-;-;-
Venue:
MDM '11 Proceedings of the 2011 IEEE 12th International Conference on Mobile Data Management - Volume 01
Year:
2011

Citing 0
Cited 1

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AusDM '12 Proceedings of the Tenth Australasian Data Mining Conference - Volume 134

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Abstract

There is a growing focus on 24/7 cardiac monitoring that leverages state of the art mobile phones and commercial-off-the-shelf (COTS) wearable bio-sensors. While many signal processing techniques for mobile ECG analysis have been developed, these techniques tend to be computationally intensive. In this paper, we propose, develop and evaluate a resource-aware and energy-efficient time series analysis technique for real-time ECG analysis on mobile devices based on the well-known SAX (Symbolic Aggregate Approximation) representation for time series termed RA-SAX.