SDNN: Total HRV from PPG Signals
SDNN (Standard Deviation of Normal-to-Normal intervals) is the standard deviation of all accepted interbeat intervals over a recording period, providing a global measure of total heart rate variability that reflects all cyclic components (sympathetic, parasympathetic, circadian) contributing to HRV.
SDNN is calculated as the square root of the variance of all NN intervals: SDNN = √(1/(N-1) · Σ(NNᵢ - mean(NN))²). For standardized clinical HRV analysis, SDNN is computed from 24-hour Holter recordings per ESC/NASPE Task Force guidelines (1996). Short-term SDNN from 5-minute recordings captures primarily parasympathetic and respiratory components, and values are not directly comparable to 24-hour SDNN.
Normal 24-hour SDNN values in healthy adults range from 100–200 ms, decreasing with age. SDNN < 50 ms from 24-hour recording is a strong independent predictor of cardiovascular mortality post-myocardial infarction (relative risk 5.3× in the ATRAMI study). From PPG, 5-minute SDNN at rest typically ranges 30–70 ms in healthy adults, with PPG-derived values showing ICC = 0.90–0.95 compared to simultaneous ECG SDNN in controlled validation studies.
Consumer wearables report overnight SDNN or modified SDNN metrics rather than standardized 5-minute or 24-hour values. Garmin, WHOOP, and Oura compute SDNN from sleep PPG segments, but each uses proprietary epoch selection and artifact rejection criteria that prevent direct cross-device comparison. For research applications, standardized recording conditions (5-minute supine, controlled breathing) are essential for valid PPG SDNN measurement.
Frequently Asked Questions
What is the difference between SDNN and RMSSD?
SDNN measures total variability (all frequency components including sympathetic). RMSSD measures beat-to-beat variability (primarily parasympathetic/vagal tone). SDNN from short recordings correlates with RMSSD, but 24-hour SDNN captures additional slow oscillations.
Why can't 5-minute SDNN be compared to 24-hour SDNN?
24-hour SDNN includes circadian variation, sleep-wake transitions, and very low frequency components that inflate the total variance. 5-minute SDNN captures only fast oscillations (HF and part of LF), producing systematically lower values.
How accurate is PPG-derived SDNN?
At rest, PPG SDNN agrees with ECG SDNN within 5–10 ms (ICC 0.90–0.95). During movement, IBI detection errors from motion artifacts inflate PPG SDNN spuriously. Only artifact-free segments should be used for SDNN computation.