Struct NonLinearMetrics

pub struct NonLinearMetrics<T> {
    pub samp_en: T,
    pub dfa: T,
    pub lzc: T,
}

A struct representing various non-linear heart rate variability (HRV) metrics.

The NonLinearMetrics struct holds key HRV parameters computed from RR intervals using non-linear techniques. These metrics provide insight into the complexity and fractal characteristics of heart rate dynamics, which reflect autonomic nervous system regulation of the heart. These non-linear metrics are important for understanding the self-organizing behavior of the cardiovascular system over time.

Fields

samp_en: T

Sample Entropy (SampEn). A measure of the regularity and complexity of the RR intervals. Lower values indicate a more regular signal.

dfa: T

Detrended Fluctuation Analysis (DFA). A measure of long-range correlations in the RR intervals, indicating fractal-like properties of the heart rate variability signal.

lzc: T

Lempel-Ziv Complexity (LZC). A measure of the signal’s complexity based on the number of distinct patterns present in the RR intervals.

Implementations

impl<T: Float + Sum<T> + Copy + Debug + AddAssign> NonLinearMetrics<T>

pub fn compute_default(rr_intervals: &[T]) -> Self

Computes various non-linear HRV metrics from the given RR intervals with default parameters.

The function calculates the following HRV metrics:

• SampEn (Sample Entropy): A measure of regularity and complexity of the signal.
• DFA (Detrended Fluctuation Analysis): A measure of long-range correlations in the RR intervals.
• LZC (Lempel-Ziv Complexity): A measure of the signal’s complexity based on the number of distinct patterns.

Arguments

• rr_intervals - A slice of RR intervals representing the time between successive heartbeats (in milliseconds).

Returns

Returns a NonLinearMetrics struct containing the computed non-linear HRV metrics.

pub fn compute( rr_intervals: &[T], tolerance: &T, scales: Option<&[usize]>, ) -> Self

Computes various non-linear HRV metrics from the given RR intervals with custom tolerance and scales.

The function calculates the following HRV metrics:

• SampEn (Sample Entropy): A measure of regularity and complexity of the signal.
• DFA (Detrended Fluctuation Analysis): A measure of long-range correlations in the RR intervals.
• LZC (Lempel-Ziv Complexity): A measure of the signal’s complexity based on the number of distinct patterns.

Arguments

• rr_intervals - A slice of RR intervals representing the time between successive heartbeats (in milliseconds).
• tolerance - A tolerance value used for calculating sample entropy.
• scales - An optional list of scale sizes to be used for DFA calculation.

Returns

Returns a NonLinearMetrics struct containing the computed non-linear HRV metrics.

pub fn compute_samp_en(rr_intervals: &[T], tolerance: &T) -> T

Computes the Sample Entropy (SampEn) from the given RR intervals and tolerance value.

SampEn is a measure of the complexity and regularity of the time series. Lower values indicate a more regular signal, while higher values suggest more complexity.

Arguments

• rr_intervals - A slice of RR intervals representing the time between successive heartbeats.
• tolerance - A tolerance value used to define how similar two sequences must be to be considered similar.

Returns

Returns the Sample Entropy (SampEn) of the RR intervals as a floating point value.

pub fn compute_dfa(rr_intervals: &[T], scales: Option<&[usize]>) -> T

Computes the Detrended Fluctuation Analysis (DFA) from the given RR intervals and scale values.

DFA measures the long-range correlations in the RR intervals, indicating fractal-like properties of heart rate variability. This method applies linear regression to segments of the data and calculates the fluctuation at each scale.

Arguments

• rr_intervals - A slice of RR intervals representing the time between successive heartbeats.
• scales - An optional slice of scale sizes to use for DFA. If None, scales are generated automatically.

Returns

Returns the scaling exponent (α) from DFA, indicating the long-term correlation.

pub fn compute_lzc(rr_intervals: &[T]) -> T

Computes the Lempel-Ziv Complexity (LZC) of the RR intervals.

LZC is a measure of the complexity of a binary sequence generated from the RR intervals. It quantifies the number of distinct patterns that appear in the binary sequence.

Arguments

• rr_intervals - A slice of RR intervals representing the time between successive heartbeats.

Returns

Returns the Lempel-Ziv Complexity (LZC) as a floating point value.

Trait Implementations

impl<T: Clone> Clone for NonLinearMetrics<T>

fn clone(&self) -> NonLinearMetrics<T>

Returns a duplicate of the value.

fn clone_from(&mut self, source: &Self)

Performs copy-assignment from source.

impl<T: Debug> Debug for NonLinearMetrics<T>

fn fmt(&self, f: &mut Formatter<'_>) -> Result

Formats the value using the given formatter.

impl<T: Default> Default for NonLinearMetrics<T>

fn default() -> NonLinearMetrics<T>

Returns the “default value” for a type.

impl<T: PartialEq> PartialEq for NonLinearMetrics<T>

fn eq(&self, other: &NonLinearMetrics<T>) -> bool

Tests for self and other values to be equal, and is used by ==.

fn ne(&self, other: &Rhs) -> bool

Tests for !=. The default implementation is almost always sufficient, and should not be overridden without very good reason.

impl<T: Copy> Copy for NonLinearMetrics<T>

impl<T> StructuralPartialEq for NonLinearMetrics<T>