Associations between Neurocardiovascular Signal Entropy and Physical Frailty.
Silvin Paul KnightLouise NewmanJohn D O'ConnorJames R C DavisRose Anne KennyRomán Romero OrtuñoPublished in: Entropy (Basel, Switzerland) (2020)
In this cross-sectional study, the relationship between noninvasively measured neurocardiovascular signal entropy and physical frailty was explored in a sample of community-dwelling older adults from The Irish Longitudinal Study on Ageing (TILDA). The hypothesis under investigation was that dysfunction in the neurovascular and cardiovascular systems, as quantified by short-length signal complexity during a lying-to-stand test (active stand), could provide a marker for frailty. Frailty status (i.e., "non-frail", "pre-frail", and "frail") was based on Fried's criteria (i.e., exhaustion, unexplained weight loss, weakness, slowness, and low physical activity). Approximate entropy (ApEn) and sample entropy (SampEn) were calculated during resting (lying down), active standing, and recovery phases. There was continuously measured blood pressure/heart rate data from 2645 individuals (53.0% female) and frontal lobe tissue oxygenation data from 2225 participants (52.3% female); both samples had a mean (SD) age of 64.3 (7.7) years. Results revealed statistically significant associations between neurocardiovascular signal entropy and frailty status. Entropy differences between non-frail and pre-frail/frail were greater during resting state compared with standing and recovery phases. Compared with ApEn, SampEn seemed to have better discriminating power between non-frail and pre-frail/frail individuals. The quantification of entropy in short length neurocardiovascular signals could provide a clinically useful marker of the multiple physiological dysregulations that underlie physical frailty.
Keyphrases
- community dwelling
- heart rate
- physical activity
- blood pressure
- functional connectivity
- resting state
- heart rate variability
- weight loss
- mental health
- electronic health record
- type diabetes
- big data
- metabolic syndrome
- bariatric surgery
- machine learning
- deep learning
- insulin resistance
- skeletal muscle
- general practice
- glycemic control