Smart Health for Assessing the Risk of Events via ECG Database 1.0.0

File: <base>/README (3,405 bytes)
Electrocardiogram Database for Vascular Events Prediction

This research investigate the possibility to identify hypertensive subject at higher risk to develop vascular events based on Heart Rate Variability analysis. The research material in the Electrocardiogram Database for Vascular Events Prediction included nominal 24-h electrocardiographic (ECG) holter recordings of 139 hypertensive patients recruited at the Centre of Hypertension of the University Hospital of Naples Federico II, Naples, Italy. 
The ECG Holter was performed after a one-month antihypertensive therapy wash-out. The patients, aged 55 and over (including 49 female and 90 male, age 72 ± 7 years),  were followed up for 12 months after the recordings in order to record major cardiovascular and cerebrovascular events, i.e. fatal or non-fatal acute coronary syndrome including myocardial infarctions, syncopal events, coronary revascularization, fatal or non-fatal stroke and transient ischemic attack. All the events were adjudicated by the Committee for Event Adjudication in the Hypertension Center. Adjudication was based on patient history, contact with the reference general practitioner and clinical records documenting the occurrence of the event/arrhythmia. Among the study sample, in the 12-month follow-up after recordings, 17 patients experienced a recorded event (11 myocardial infarctions, 3 strokes, 3 syncopal events). Moreover, the patients were evaluated by a cardiac and carotid ultrasonography. Left ventricular mass was determined by using the formula developed by Devereux as recommend by American Society of Echocardiography (ASE) and divided by the body surface area to calculate left ventricular mass index (LVMi, g/m2). B-mode ultrasonography of carotid arteries was performed in order to compute the maximum intima media thickness (mm).
The individual recordings are each about 24 hours in duration, and contain three ECG signals each sampled at 128 samples per second. Annotation files (with the suffix .wqrs0) were prepared using an automated detector based on the length transform (Zong, W., G. B. Moody and D. Jiang. A robust open-source algorithm to detect onset and duration of QRS complexes. Computers in Cardiology, 2003: 737-740) and have not been corrected manually. Each recording is supplied with demographic and clinical information (e.g. age, gender, eventual vascular event, values of systolic and diastolic arteriosus pressure).

When referencing this material, please cite the following pubblication:
P. Melillo, R. Izzo, A. Orrico, P. Scala, M. Attanasio, M. Mirra, N. De Luca, L. Pecchia “Automatic prediction of cardiovascular and cerebrovascular events using Heart Rate Variability analysis”, Plos One, in press
The current study was supported by “the 2007-2013 NOP for Research and Competitiveness for the Convergence Regions (Calabria, Campania, Puglia and Sicilia)” with code PON04a3_00139 - Project Smart Health and Artificial intelligence for Risk Estimation.
Database creators: P. Melillo, R. Izzo, A. Orrico, P. Scala, M. Attanasio, M. Mirra, N. De Luca, L. Pecchia;
Paolo Melillo,;
Multidisciplinary Department of Medical, Surgical and Dental Science, Second University of Naples, Naples, Italy

Leandro Pecchia,
School of Engineering, University of Warwick, Coventry, UK