Sleep analytics and online selective anomaly detection

Tahereh Babaie; Sanjay Chawla; Romesh Abeysuriya

doi:10.1145/2623330.2623699

Sleep analytics and online selective anomaly detection

Tahereh Babaie, Sanjay Chawla, Romesh Abeysuriya

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

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.

Original language	English
Title of host publication	KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Publisher	Association for Computing Machinery
Pages	362-371
Number of pages	10
ISBN (Print)	9781450329569
DOIs	https://doi.org/10.1145/2623330.2623699
State	Published - 2014
Externally published	Yes
Event	20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014 - New York, NY, United States Duration: Aug 24 2014 → Aug 27 2014

Publication series

Name	Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference	20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014
Country/Territory	United States
City	New York, NY
Period	08/24/14 → 08/27/14

Keywords

anomaly/novelty detection
dynamic residue model
mining rich data types
sleep EEG anomalies

Access to Document

10.1145/2623330.2623699

Cite this

Babaie, T., Chawla, S., & Abeysuriya, R. (2014). Sleep analytics and online selective anomaly detection. In KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 362-371). (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). Association for Computing Machinery. https://doi.org/10.1145/2623330.2623699

Babaie, Tahereh ; Chawla, Sanjay ; Abeysuriya, Romesh. / Sleep analytics and online selective anomaly detection. KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2014. pp. 362-371 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

@inproceedings{bbf3e8ecaac2462ca056c7aa62f9a97f,

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",

note = "null ; Conference date: 24-08-2014 Through 27-08-2014",

}

Babaie, T, Chawla, S & Abeysuriya, R 2014, Sleep analytics and online selective anomaly detection. in KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, pp. 362-371, 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2014, New York, NY, United States, 08/24/14. https://doi.org/10.1145/2623330.2623699

Sleep analytics and online selective anomaly detection. / Babaie, Tahereh; Chawla, Sanjay; Abeysuriya, Romesh.

KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery, 2014. p. 362-371 (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Sleep analytics and online selective anomaly detection

AU - Babaie, Tahereh

AU - Chawla, Sanjay

AU - Abeysuriya, Romesh

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - anomaly/novelty detection

KW - dynamic residue model

KW - mining rich data types

KW - sleep EEG anomalies

UR - http://www.scopus.com/inward/record.url?scp=84907033524&partnerID=8YFLogxK

U2 - 10.1145/2623330.2623699

DO - 10.1145/2623330.2623699

M3 - Conference contribution

AN - SCOPUS:84907033524

SN - 9781450329569

T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

SP - 362

EP - 371

BT - KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

PB - Association for Computing Machinery

Y2 - 24 August 2014 through 27 August 2014

ER -

Babaie T, Chawla S, Abeysuriya R. Sleep analytics and online selective anomaly detection. In KDD 2014 - Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Association for Computing Machinery. 2014. p. 362-371. (Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining). doi: 10.1145/2623330.2623699

Sleep analytics and online selective anomaly detection

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this