Processing API Reference¶

Import: from ecgdatakit.processing import ...

Requires: pip install ecgdatakit[processing] (scipy ≥ 1.10)

All filter and transform functions accept a Lead and return a new Lead (immutable pattern via dataclasses.replace). The original lead is never modified.

Note

All processing functions accept both Lead objects and raw numpy arrays. When passing a numpy array, provide the sample rate via fs:

filtered = diagnostic_filter(my_array, fs=500)

See Data Models for details.

Filters ¶


`lowpass()`	Apply a Butterworth low-pass filter
`highpass()`	Apply a Butterworth high-pass filter
`bandpass()`	Apply a Butterworth band-pass filter
`notch()`	Apply an IIR notch (band-stop) filter
`remove_baseline()`	Remove baseline wander using a high-pass filter
`diagnostic_filter()`	AHA diagnostic: 0.05–150 Hz bandpass + notch
`monitoring_filter()`	Monitoring: 0.67–40 Hz bandpass + notch

Resampling ¶


`resample()`	Resample a lead to a different sample rate

Normalization ¶

All normalization functions accept a single lead or a list[Lead] for per-lead normalization.


`normalize_minmax()`	Scale signal to the [−1, 1] range
`normalize_zscore()`	Normalize to zero mean and unit variance (z-score)
`normalize_amplitude()`	Scale peak amplitude to a target value

R-Peak Detection ¶


`detect_r_peaks()`	Detect R-peak locations in an ECG lead
`heart_rate()`	Compute average heart rate in beats per minute
`rr_intervals()`	Compute RR intervals in milliseconds
`instantaneous_heart_rate()`	Compute instantaneous heart rate at each beat

Heart Rate Variability ¶


`time_domain()`	Compute time-domain HRV metrics from RR intervals
`frequency_domain()`	Compute frequency-domain HRV metrics from RR intervals
`poincare()`	Compute Poincaré plot descriptors (SD1, SD2)

Transforms & Segmentation ¶


`power_spectrum()`	Compute the power spectral density of an ECG lead
`fft()`	Compute the single-sided FFT magnitude spectrum
`segment_beats()`	Segment individual heartbeats around R-peaks
`average_beat()`	Compute the ensemble-averaged heartbeat (template)

Signal Quality ¶


`signal_quality_index()`	Composite signal quality index (SQI) in [0, 1]
`classify_quality()`	Classify signal quality as a human-readable category
`snr_estimate()`	Estimate signal-to-noise ratio in dB

Lead Derivation ¶


`derive_lead_iii()`	Derive Lead III from Leads I and II (Einthoven’s law)
`derive_augmented()`	Derive augmented limb leads aVR, aVL, aVF
`derive_standard_12()`	Assemble a full 12-lead ECG
`find_lead()`	Find a lead by label (case-insensitive)

ECG Cleaning ¶


`clean_ecg()`	Clean an ECG lead signal