Sleep Analytics and Online Selective Anomaly Detection
This addresses a specific scenario in sleep science for researchers analyzing sleep stages, but it is incremental as it combines existing techniques from machine learning and control theory.
The paper tackled the problem of detecting specific anomalies in sleep EEG data by introducing the Online Selective Anomaly Detection (OSAD) problem, which focuses on triggering alarms only for non-sleep spindle anomalies, and experiments on real data showed its effectiveness.
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 machine learning and control theory. Experiments on data from real subjects attest to the effectiveness of our approach.