INCLASS: Incremental Classification Strategy for Self-Aware Epileptic Seizure Detection
Lorenzo Ferretti1, Giovanni Ansaloni2, Renaud Marquis3, Tomas Teijeiro2,
Philippe Ryvlin3, David Atienza2 and Laura Pozzi1
1Computer Systems Institute, Università della Svizzera italiana, Lugano, Switzerland
2Embedded Systems Laboratory, EPFL, Lausanne, Switzerland
3Lausanne University Hospital, CHUV, Lausanne, Switzerland
ABSTRACT
Wearable Health Companions allow the unobtrusive monitoring of patients affected by chronic conditions. In particular, by acquiring and interpreting bio-signals, they enable the detection of acute episodes in cardiac and neurological ailments. Nevertheless, the processing of bio-signals is computationally complex, especially when a large number of features are required to obtain reliable detection outcomes. Addressing this challenge, we present a novel methodology, named INCLASS, that iteratively extends employed feature sets at run-time, until a confidence condition is satisfied. INCLASS builds such sets based on code analysis and profiling information. When applied to the challenging scenario of detecting epileptic seizures based on ECG and SpO2 acquisitions, INCLASS obtains savings of up to 54%, while incurring in a negligible loss of detection performance (1.1% degradation of specificity and sensitivity) with respect to always computing and evaluating all features.
Keywords: Self-aware systems, Epileptic seizure detection, Wearable health companions.
