2014 SleepAnalyticsandOnlineSelectiv

• (Babaie et al., 2014) ⇒ Tahereh Babaie, Sanjay Chawla, and Romesh Abeysuriya. (2014). “Sleep Analytics and Online Selective Anomaly Detection.” In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2014) Journal. ISBN:978-1-4503-2956-9 doi:10.1145/2623330.2623699

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222014%22+Sleep+Analytics+and+Online+Selective+Anomaly+Detection
• http://dl.acm.org/citation.cfm?id=2623330.2623699&preflayout=flat#citedby

Quotes

Author Keywords

• Anomaly/novelty detection; dynamic residue model; general; mining rich data types; sleep eeg anomalies

Abstract

We introduce a new problem, the Online Selective Anomaly Detection (OSAD), to model a specific scenario emerging from research in sleep science. Scientists have segmented sleep into several stages and stage two is characterized by two patterns (or anomalies) in the EEG time series recorded on sleep subjects. These two patterns are sleep spindle (SS) and K-complex. The OSAD problem was introduced to design a residual system, where all anomalies (known and unknown) are detected but the system only triggers an alarm when non-SS anomalies appear. The solution of the OSAD problem required us to combine techniques from both data mining and control theory. Experiments on data from real subjects attest to the effectiveness of our approach.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2014 SleepAnalyticsandOnlineSelectiv	Sanjay Chawla Tahereh Babaie Romesh Abeysuriya			Sleep Analytics and Online Selective Anomaly Detection				10.1145/2623330.2623699		2014