Heart Rate Variability

heart rate variability , or hrv for short, is a non invasive measure of your autonomic nervous system, which is the body’s main control center it is widely considered as one of the best objective metrics for physical fitness and determining your body’s readiness to perform hrv is literally the variance in time between the beats (nn interval) of your heart how is it calculated? a commonly used statistical metric for representing short time hrv (over a time duration of the order of 10s to 1 min) is the standard deviation of nn intervals (sdnn) it quantifies the variability between successive normal to normal (nn) intervals, which are the time intervals between consecutive heartbeats in this case, ppg signals are used to extract the nn intervals starting from ppg signals, the following steps are executed to determine the sdnn value ppg signal preprocessing systolic peak detection nn interval calculation sdnn calculation the definition and additional contextual information of each output is outlined below in the table accuracy the current mean absolute error of the sdnn measurement through the intelliprove algorithms is 13 ± 15 ms (milliseconds) in 96 5% of the measurements the absolute measurement error is less than 40 milliseconds a good correlation (r=0 89; p<0 00001) between video based hrv measurement and ground truth hrv values (measured by using ecg signals acquired from a chest belt sensor) can be observed how can it be used? hrv is a valuable metric for assessing stress levels or recovery after sports when a person has a low hrv , it indicates a high activity of the sympathetic nervous system (sns), which is associated with stress, overwhelm and arousal assessing the hrv at rest can provide valuable personalized insights into quantifying the chronical stress level scoring sleep quality and total wellbeing of the person a reduction in hrv will be visible when sleep quality is reduced the impact of unhealthy food or low nutritions the quality and effects of recovery exercises engaging in activities that aim to increase hrv and restore the balance of the autonomic nervous system (ans) can be beneficial for recovery the metric should be carefully measured and controlled to ensure the positive effect of the training and the needed personalized durations use hrv as a powerful metric to assess body stress load during a health check in provide personalized recommendations based on the corresponding score level interpreting results sdnn is returned as an integer > 0 and can be requested as a widgets docid\ oorapckooi6 89xrafduv or via the rest api docid\ biupvixvhltbukklr2ati definitions name unit range programmatic name health profile heart rate variability milliseconds \[ms] 0 300 heart rate variability mental health values value meaning zone example user text 0 35 below average red your heart rate variability value is below average this indicates your body is under pressure for some reason which could be due to exercise, psychological proceedings or any other external and internal stressors 36 100 average healthy yellow your heart rate variability is in a normal range to further increase your heart rate variability you can perform aerobic and/or breathing exercises or meditation >100 above average – excellent green your heart rate variability is above average which general states that the body has a stronger capability to cope up with stress or is recovering strongly from previously built up stress the table above shows value the possible values for the health insight meaning what does the value mean zone we distinguish three zones, useful – for example when creating a widget to clarify the feel towars the user green = optimal to normal yellow = normal to average red = outside the typical range; may require attention example user text an example of what could be communicated to the user in case this value is measured scientific papers pinheiro n, couceiro r, henriques j, muehlsteff j, quintal i, goncalves l, carvalho p can ppg be used for hrv analysis? annu int conf ieee eng med biol soc 2016 aug;2016 2945 2949 odinaev i, wong kl, chin jw, goyal r, chan tt, so rhy robust heart rate variability measurement from facial videos bioengineering (basel) 2023 jul 18;10(7) 851 yu sg, kim se, kim nh, suh kh, lee ec pulse rate variability analysis using remote photoplethysmography signals sensors (basel) 2021 sep 17;21(18) 6241 martinez delgado gh, correa balan aj, may chan ja, parra elizondo ce, guzman rangel la, martinez torteya a measuring heart rate variability using facial video sensors (basel) 2022 jun 21;22(13) 4690