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    Sample entropy: regularity and complexity of the heartbeat series.

    SE — sample entropy (SampEn) — is a nonlinear heart-rate-variability index that measures the regularity and complexity of the heartbeat-interval series. It estimates how likely it is that a pattern of intervals which repeats once will repeat again. Low values mean a regular, predictable rhythm; higher values mean a more complex, less predictable one. Entropy of beat-to-beat intervals recorded during physical activity has also been proposed as a measure of fitness.

    Heart rate variabilityNonlinear dynamicsEntropyComplexity
    FormulaSampEn = −ln(A / B)
    Usual settingsm = 2, r = 0.2 × SD
    Unitdimensionless
    Lower valuesmore regular rhythm

    How sample entropy works

    The method takes every short sequence of m consecutive intervals (usually m = 2) as a "template" and counts how many other sequences are similar to it within a tolerance r (usually 20 % of the standard deviation). This gives B. It then repeats the count for sequences of length m + 1, giving A. If patterns that match for 2 beats usually also match for the third beat, the series is regular and SampEn is low; if they often diverge, SampEn is high.

    SampEn(m, r, N) = −ln( A / B ) · A = matches of length m+1, B = matches of length m (self-matches excluded)

    Sample entropy was developed as an improvement of approximate entropy (ApEn): it excludes self-matches, is less dependent on record length and gives more consistent results.

    Two heartbeat interval series: a regular periodic series with low sample entropy and a complex irregular series with higher sample entropy Periodic — very low SampEn Irregular — higher SampEn Healthy heart rhythm: complex, between order and randomness beat number →
    Figure 1. Sample entropy quantifies regularity: a strictly repeating pattern has very low entropy, an irregular pattern higher entropy. Healthy heart-rate dynamics combine both — they are complex rather than purely periodic or purely random.

    Interpretation

    Low sample entropy

    • More regular, less adaptive heart-rate dynamics
    • High-intensity exercise, strong sympathetic activation, stress
    • Ageing, heart failure; in newborns a fall in entropy has been observed before sepsis

    High sample entropy

    • Complex, flexible regulation at rest in healthy people
    • Very high values can reflect noise rather than healthy complexity: artefacts, frequent extrasystoles, atrial fibrillation
    Measurement

    Entropy values depend on m, r, recording length and preprocessing. Short recordings (fewer than a few hundred beats) give unstable estimates. Compare values only between recordings made with the same settings and device.

    When to see a doctor

    Sample entropy is a supplementary, research-oriented index. If it is persistently low together with other reduced HRV indices, or if you have symptoms such as palpitations, breathlessness or chest pain, consult a doctor.

    References

    1. Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol. 2000;278(6):H2039–H2049. doi:10.1152/ajpheart.2000.278.6.H2039
    2. Lake DE, Richman JS, Griffin MP, Moorman JR. Sample entropy analysis of neonatal heart rate variability. Am J Physiol Regul Integr Comp Physiol. 2002;283(3):R789–R797.
    3. Voss A, Schulz S, Schroeder R, Baumert M, Caminal P. Methods derived from nonlinear dynamics for analysing heart rate variability. Philos Trans A Math Phys Eng Sci. 2009;367(1887):277–296. doi:10.1098/rsta.2008.0232
    4. Shaffer F, Ginsberg JP. An overview of heart rate variability metrics and norms. Front Public Health. 2017;5:258. doi:10.3389/fpubh.2017.00258

    This article is for general information and does not replace a consultation with a doctor.

    Published on 30 June 2019