Methods

This section explains what each method does, what data it expects, what it returns, and the main limitations that affect interpretation. Biosiglib defines the shared behavior; follow the implementation links on each page to use the method in Python or MATLAB.

Method	Area	What it does
ECG baseline removal from fiducial isoelectric samples	ECG	Estimates a slowly varying ECG baseline from local means around fiducial positions and subtracts its spline interpolation.
Pan-Tompkins-style ECG R-wave detection	ECG	Detects ordered R-wave occurrence times from a sampled ECG signal and exposes intermediate processing signals for plotting and debugging.
Slope-range ECG-derived respiration	ECG	Estimates an ECG-derived respiration amplitude series from derivative ECG morphology around detected R waves.
Frequency-domain HRV metrics	HRV	Integrates conventional LF and HF powers or respiration-separated OSP powers on an authoritative frequency grid.
Missing-event gap filling	HRV	Reconstructs missing event timestamps by iteratively interpolating intervals inside locally detected gaps.
Integral pulse frequency modulation heart-timing reconstruction	HRV	Estimates uniformly sampled instantaneous heart rate and an optional TVIPFM autonomic modulating signal from event times.
Respiration-related HRV decomposition by orthogonal subspace projection	HRV	Separates a uniformly sampled HRV modulating signal into a component linearly related to respiration and an orthogonal residual.
False-positive event removal	HRV	Removes detections that follow abnormally short event-to-event intervals using a fixed adaptive-baseline rule.
Time-domain beat or pulse variability metrics	HRV	Computes standard time-domain HRV metrics from cleaned beat-to-beat or pulse-to-pulse intervals.
Low-pass differentiator filter design	TOOLS	Designs a low-pass differentiating FIR filter and reports its linear-phase delay.
Median-filtered adaptive threshold	TOOLS	Computes a capped adaptive threshold from a one-dimensional signal using median-filter-based local baseline estimation.
Causal filtering with NaN-aware gap handling	TOOLS	Applies ordinary causal filtering while interpolating short NaN gaps and preserving long NaN gaps.
Zero-phase filtering with NaN-aware gap handling	TOOLS	Applies ordinary zero-phase filtering while interpolating short NaN gaps and preserving long NaN gaps.
Snap detections to local maxima	TOOLS	Refines detection sample positions by moving each detection to the maximum signal sample in a NaN-aware local search window.