Engineering, 2013, 5, 237-243 http://dx.doi.org/10.4236/eng.2013.510B049 Published Online October 2013 (http://www.scirp.org/journal/eng) Copyright © 2013 SciRes. ENG Heart Rate Variability Applied to Short-Term Cardiovascular Event Risk Assessment Simao Paredes 1 , Teresa Rocha 1 , Paulo de Carvalho 2 , Jorge Henriques 2 , Ramona Cabiddu 3 , João Morais 4 1 Computer Science and Systems Engineering Department, Polytechnic Institute of Coimbra (IPC/ISEC), Coimbra, Portugal 2 Centre for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal 3 Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milano, Italy 4 CardiologyDepartment, Leiria-Pombal Hospital Centre, Leiria, Portugal Email: sparedes@isec.pt, teresa@isec.pt, carvalho@dei.uc.pt, jh@dei.uc.pt, ramona.cabiddu@gmail.com, joaomorais@hsaleiria.min-saude.pt Received June 2013 ABSTRACT Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Therefore, there are several well known risk assessment tools, unfortunately they present some limitations. This work addresses this problem with two different methodologies: 1) combination of risk assessment tools based on fusion of Bayesian classifiers complemented with genetic algorithm optimization; 2) personalization of risk assessment through the creation of groups of patients that maximize the performance of each risk assessment tool. This last ap- proach is implemented based on subtractive clustering applied to a reduced-dimension space. Both methodologies were developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva- tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies: 1) San- ta Cruz Hospital, Portugal, N = 460 patients; 2) Leiria-Pombal Hospital Centre, Portugal, N = 99 patients. This work im- proved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%, 81.5%. Besides this enhancement, the proposed methodologies allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification) should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters de- rived from heart rate variability (HRV), is introduced in this work. HRV is a strong and independent predictor of mor- tality in patients following acute myocardial infarction. The impact of HRV parameters in the characterization of coro- nary artery disease (CAD) patients will be conducted during hospitalization of these patients in the Leiria-Pombal Hos- pital Centre (LPHC). Keywords: CVD Risk Assessment; Knowledge Management; Management of Cardiovascular Diseases; Decision-Support Systems 1. Introduction Coronary heart disease (CHD) 1 , approximately half of all cardiovascular disease (CVD) deaths, is the single most common cause of death in Europe [1]. European Heart Network supports that around 80% of CHD are preventable [2], which shows that the improve- ment of preventive health care can originate important benefits reducing the incidence of cardiovascular diseases. Therefore, preventive health care assumes a critical importance in the present health care context. It is the key aspect in reducing the social and economic costs directly originated by cardiovascular diseases. In fact, it is commonly accepted that current health care paradigm has to move from reactive care towards preventive care, reducing the amount of in hospital care. Health telemo- nitoring systems are essential to achieve this target, as they allow the remote monitoring of patients who are in 1 Coronary heart disease (heart attacks), cerebrovascular disease (stroke) raised blood pressure (hypertension), peripheral artery disease, rheu- matic heart disease, congenital heart disease and heart failure are dis- orders of the heart and blood vessels globally designated by cardiovas- cular diseases (CVD).