@inproceedings{110ec44d64f34b81b655399c07d201d3,
title = "Sleep analytics and online selective anomaly detection",
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.",
keywords = "anomaly/novelty detection, dynamic residue model, mining rich data types, sleep EEG anomalies",
author = "Tahereh Babaie and Sanjay Chawla and Romesh Abeysuriya",
year = "2014",
doi = "10.1145/2623330.2623699",
language = "English",
isbn = "9781450329569",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
publisher = "Association for Computing Machinery",
pages = "362--371",
booktitle = "KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
address = "United States",
note = "20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 ; Conference date: 24-08-2014 Through 27-08-2014",
}