title: A wavelet-based approach for time series pattern detection and events prediction applied to telemonitoring data.
abstract: This work aims the development of a predictive strategy able to estimate future events with relevant impact in the cardiovascular status. Based on wavelet transform, a new time series similarity metric is introduced, which is capable to detect a pre-defined pattern in time series data. In addition, a methodology combining a wavelet scheme with state space multi-models is proposed to achieve the prediction of future signal values. Blood pressure signals, collected by a telemonitoring platform (TEN-HMS), are used to detect the occurrence of future hypertension events.
authors.affiliation: Departamento de Engenharia Informática e de Sistemas, Instituto Superior de Engenharia de Coimbra, Portugal. teresa@isec.pt
authors.forename: T
authors.initials: T
authors.lastname: Rocha
authors.forename: S
authors.initials: S
authors.lastname: Paredes
authors.forename: P
authors.initials: P
authors.lastname: Carvalho
authors.forename: J
authors.initials: J
authors.lastname: Henriques
mesh_terms: D001795:Blood Pressure Determination / Q000379:methods*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D006973:Hypertension / Q000175:diagnosis*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D017216:Telemedicine / Q000379:methods*; D058067:Wavelet Analysis