Automatic Prediction of Vascular Events by Heart Rate Variability Analysis in Hypertensive Patients

Melillo, Paolo; Scala, Paolo; Luca, Nicola De; Pecchia, Leandro

doi:10.1007/978-3-319-11128-5_19

Cited by 2 publications

(2 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The results of the former are extensively described in [22] for cardiovascular risk and in [21] for risk of falling. In particular, the retrospective study was designed ad hoc and performed by enrolling hypertensive patients with at least 24 h nominal ECG.…”

Section: Validation Proceduresmentioning

confidence: 99%

“…A recent study [20] has investigated the blunted baroreflex sensitivity as an underestimated cause of falls in the elderly, by using HRV analysis. In recent papers, we demonstrated for the first time that there is a significant association between a depressed HRV and the risk of falling [21,22] and that this association can be timely detected to predict imminent falls [23][24][25].…”

Section: Introductionmentioning

confidence: 98%

See 1 more Smart Citation

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

Melillo

Orrico

Scala³

et al. 2015

J Med Syst

View full text Add to dashboard Cite

The aim of this paper is to describe the design and the preliminary validation of a platform developed to collect and automatically analyze biomedical signals for risk assessment of vascular events and falls in hypertensive patients. This m-health platform, based on cloud computing, was designed to be flexible, extensible, and transparent, and to provide proactive remote monitoring via data-mining functionalities. A retrospective study was conducted to train and test the platform. The developed system was able to predict a future vascular event within the next 12 months with an accuracy rate of 84 % and to identify fallers with an accuracy rate of 72 %. In an ongoing prospective trial, almost all the recruited patients accepted favorably the system with a limited rate of inadherences causing data losses (<20 %). The developed platform supported clinical decision by processing tele-monitored data and providing quick and accurate risk assessment of vascular events and falls.

show abstract

Section: Validation Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 98%

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

Melillo

Orrico

Scala³

et al. 2015

J Med Syst

View full text Add to dashboard Cite

show abstract

Wearable technology and ECG processing for fall risk assessment, prevention and detection

Melillo

Castaldo

Sannino

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Falls represent one of the most common causes of injury-related morbidity and mortality in later life. Subjects with cardiovascular disorders (e.g., related to autonomic dysfunctions and postural hypotension) are at higher risk of falling. Autonomic dysfunctions increasing the risk of falling in the short and mid-term could be assessed by Heart Rate Variability (HRV) extracted by electrocardiograph (ECG). We developed three trials for assessing the usefulness of ECG monitoring using wearable devices for: risk assessment of falling in the next few weeks; prevention of imminent falls due to standing hypotension; and fall detection. Statistical and data-mining methods are adopted to develop classification and regression models, validated with the cross-validation approach. The first classifier based on HRV features enabled to identify future fallers among hypertensive patients with an accuracy of 72% (sensitivity: 51.1%, specificity: 80.2%). The regression model to predict falls due to orthostatic dropdown from HRV recorded before standing achieved an overall accuracy of 80% (sensitivity: 92%, specificity: 90%). Finally, the classifier to detect simulated falls using ECG achieved an accuracy of 77.3% (sensitivity: 81.8%, specificity: 72.7%). The evidence from these three studies showed that ECG monitoring and processing could achieve satisfactory performances compared to other system for risk assessment, fall prevention and detection. This is interesting as differently from other technologies actually employed to prevent falls, ECG is recommended for many other pathologies of later life and is more accepted by senior citizens.

show abstract

Automatic Prediction of Vascular Events by Heart Rate Variability Analysis in Hypertensive Patients

Cited by 2 publications

References 32 publications

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

Cloud-Based Smart Health Monitoring System for Automatic Cardiovascular and Fall Risk Assessment in Hypertensive Patients

Wearable technology and ECG processing for fall risk assessment, prevention and detection

Contact Info

Product

Resources

About