Understanding HRV

This page explains, one level deeper and with sources, the HRV (heart rate variability) that Feelmo starts from. It's technical — for those who want to understand the score more fully.

Heartbeats and "the interval between them"

On an electrocardiogram, each beat produces a sharp peak (the R-wave). The interval between one R-wave and the next is the R-R interval (the interval between normal beats, excluding arrhythmic ones, is also called the N-N interval).

Where "heart rate" (beats per minute) describes average speed, HRV describes how much that interval fluctuates. Two hearts can both read "60 bpm" — one ticking regularly, the other stretching and contracting with the breath — and the latter is generally considered to have greater autonomic adaptability (Task Force, 1996; Shaffer & Ginsberg, 2017).

Figure: the R-wave on an ECG and the interval between consecutive R-waves (R–R). The widths are not constant — they fluctuate, and that is HRV.

Three domains of analysis

HRV analysis is broadly organized into three "domains" (Task Force, 1996; Shaffer & Ginsberg, 2017).

1. Time domain

Expresses the spread of R-R intervals as statistics along the time axis.

2. Frequency domain

Decomposes the fluctuation of R-R intervals into frequency components.

• HF (high frequency, 0.15–0.40 Hz) — respiratory variation; mainly reflects parasympathetic activity
• LF (low frequency, 0.04–0.15 Hz) — involves the baroreflex and others; includes both sympathetic and parasympathetic
• LF/HF ratio — once treated as "sympatho-vagal balance," but this interpretation has been shown to be too simplistic (Billman, 2013). Feelmo does not treat LF/HF as "balance itself"

Figure: heart rate rises slightly on the inhale and falls on the exhale (respiratory sinus arrhythmia, RSA). This respiratory fluctuation is the main source of the HF component and reflects vagal activity (Berntson et al., 1993).

3. Nonlinear

Heart rhythm is not a simple cycle; it has a fractal, complex structure. Poincaré plots, entropy, and DFA (detrended fluctuation analysis) are studied as ways to capture the "quality" and complexity of regulation (Shaffer & Ginsberg, 2017).

How it is measured — ECG and the optical sensor

• ECG (electrocardiogram) — the reference method, recording the heart's electrical activity directly through electrodes
• PPG (photoplethysmography) — the method used by Apple Watch and others, capturing the pulse wave of blood flow with LED light. The interval variation derived from it is, strictly speaking, called PRV (pulse rate variability)

At rest, PRV has been reported in a systematic review to agree well with ECG-based HRV (Schäfer & Vagedes, 2013). During body movement or exercise, agreement declines, which is why data at rest and during sleep is emphasized.

Recording length and conditions

• The standard short-term recording has been 5 minutes (Task Force, 1996)
• The validity of shorter "ultra-short" recordings differs by metric; RMSSD has been reported to remain relatively stable even in short recordings (Munoz et al., 2015; Laborde et al., 2017)
• Every metric is affected by posture, breathing, time of day, caffeine, and movement, so keeping measurement conditions consistent matters most (Laborde et al., 2017)

How Feelmo handles it

Feelmo places RMSSD as its standard among these metrics, centers on sleep data where external factors are fewest, and reads it against your own baseline.

How multiple metrics are integrated into a 0–100 Regulation Score — that specific composition and weighting is the core of Lumo Core and is not disclosed, as a trade secret. See also The Science of Regulation.

References

1. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation. 1996;93(5):1043–1065.
2. Shaffer F, Ginsberg JP. An Overview of Heart Rate Variability Metrics and Norms. Frontiers in Public Health. 2017;5:258.
3. Laborde S, Mosley E, Thayer JF. Heart Rate Variability and Cardiac Vagal Tone in Psychophysiological Research – Recommendations for Experiment Planning, Data Analysis, and Data Reporting. Frontiers in Psychology. 2017;8:213.
4. Billman GE. The LF/HF ratio does not accurately measure cardiac sympatho-vagal balance. Frontiers in Physiology. 2013;4:26.
5. Schäfer A, Vagedes J. How accurate is pulse rate variability as an estimate of heart rate variability? A review on studies comparing photoplethysmographic technology with an electrocardiogram. International Journal of Cardiology. 2013;166(1):15–29.
6. Munoz ML, van Roon A, Riese H, et al. Validity of (Ultra-)Short Recordings for Heart Rate Variability Measurements. PLoS One. 2015;10(9):e0138921.
7. Berntson GG, Cacioppo JT, Quigley KS. Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology. 1993;30(2):183–196.