Automatic classifier based on heart rate variability to identify fallers among hypertensive subjects

Effects of continuous supplementation of Acanthopanax senticosus Harms on the cardiac autonomic function of community-dwelling elderly individuals during resting and standing tests: a randomized controlled trial

Background

Cardiac autonomic function (CAF) decreases with aging, and Acanthopanax senticosus Harms (ASH) consumption reportedly induces anti-stress effects. This study aimed to assess the effect of continuous supplementation of ASH on CAF during resting and standing tests in the elderly population.

Methods

This double-blind, randomized controlled trial was conducted in the morning in a laboratory setting and was carried out between June 2017 and July 2017 at Kambaikan, Doshisha University (Karasuma-higashi-iru, Imadegawa-dori, Kamigyo-ku, Kyoto 602-8580, Japan). In total, 28 community-dwelling elderly individuals (mean ± standard deviation = 72.5 ± 4.5 years) were included. Each subject was instructed to consume ASH or placebo supplements twice daily for 4 weeks. An autonomic reflex orthostatic tolerance recorder was used to measure CAF in pre- and post-intervention phases. Parameters were measured in a seated position and included coefficient of variation of R-R intervals (CVRR), low frequency (LF), high frequency (HF), LF/HF ratio, blood pressure, and heart rate (HR). Changes in each parameter were evaluated before and after standing. All parameters were defined as the difference between the mean value obtained in a standing position for 2 min and that obtained in a 2-min seated position.

Results

A two-way analysis of variance revealed a significant group-time interaction effect on CVRR, HF, and ΔLF/HF ratio. Following the intervention, CVRR, HF, LF/HF ratio, systolic blood pressure (SBP), HR, ΔLF/HF ratio, ΔSBP, and ΔHR improved significantly in the ASH group only.

Conclusions

Four-week supplementation of ASH improved CAF in community-dwelling elderly individuals during resting and standing tests.

Clinical Trial Registration

https://center6.umin.ac.jp/cgi-open-bin/ctr/ctr_view.cgi?recptno=R000031218, UMIN Clinical Trials Registry (UMIN000027251).

Are ultra-short heart rate variability features good surrogates of short-term ones? State-of-the-art review and recommendations

Ultra-short heart rate variability (HRV) analysis refers to the study of HRV features in excerpts of length <5 min. Ultra-short HRV is widely growing in many healthcare applications for monitoring individual's health and well-being status, especially in combination with wearable sensors, mobile phones, and smart-watches. Long-term (nominally 24 h) and short-term (nominally 5 min) HRV features have been widely investigated, physiologically justified and clear guidelines for analysing HRV in 5 min or 24 h are available. Conversely, the reliability of ultra-short HRV features remains unclear and many investigations have adopted ultra-short HRV analysis without questioning its validity. This is partially due to the lack of accepted algorithms guiding investigators to systematically assess ultra-short HRV reliability. This Letter critically reviewed the existing literature, aiming to identify the most suitable algorithms, and harmonise them to suggest a standard protocol that scholars may use as a reference in future studies. The results of the literature review were surprising, because, among the 29 reviewed papers, only one paper used a rigorous method, whereas the others employed methods that were partially or completely unreliable due to the incorrect use of statistical tests. This Letter provides recommendations on how to assess ultra-short HRV features reliably and proposes an inclusive algorithm that summarises the state-of-the-art knowledge in this area.

Systematic mapping study of data mining–based empirical studies in cardiology

Data mining provides the methodology and technology to transform huge amount of data into useful information for decision making. It is a powerful process to extract knowledge and discover new patterns embedded in large data sets. Data mining has been increasingly used in medicine, particularly in cardiology. In fact, data mining applications can greatly benefits all parts involved in cardiology such as patients, cardiologists and nurses. This article aims to perform a systematic mapping study so as to analyze and synthesize empirical studies on the application of data mining techniques in cardiology. A total of 142 articles published between 2000 and 2015 were therefore selected, studied and analyzed according to the four following criteria: year and channel of publication, research type, medical task and empirical type. The results of this mapping study are discussed and a list of recommendations for researchers and cardiologists is provided.

Fall Prediction in Hypertensive Patients via Short-Term HRV Analysis

Falls are a major problem of later life having severe consequences on quality of life and a significant burden in occidental countries. Many technological solutions have been proposed to assess the risk or to predict falls and the majority is based on accelerometers and gyroscopes. However, very little was done for identifying first time fallers, which are very difficult to recognize. This paper presents a metamodel predicting falls using short term Heart Rate Variability (HRV) analysis acquired at the baseline. About 170 hypertensive patients (age: 72 ± 8 years, 56 female) were investigated, of which 34 fell once in the 3 months after the baseline assessment. This study is focused on hypertensive patients, which were considered as convenient pragmatic sample, as they undergo regular outpatient visits, during which short term Electrocardiogram (ECG) can be easily recorded without significant increase of healthcare costs. For each subject, 11 consecutive excerpts of 5 min each (55 min) were extracted from ECGs recorded between 10:30 and 12:30 and analysed. Linear and nonlinear HRV features were extracted and averaged among the 11 excerpts, which were, then, considered for the statistical and data mining analysis. The best predictive metamodel was based on Multinomial Naïve Bayes, which enabled to predict first-time fallers with sensitivity, specificity, and accuracy rates of 72%, 61%, and 68%, respectively.

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

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.