Signal Quality

signal_quality_index()	Compute a 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

ecgdatakit.processing.signal_quality_index(lead, *, fs=None)

Compute a composite signal quality index (SQI) in the range [0, 1].

Combines four sub-metrics: kurtosis SQI, power-ratio SQI, R-peak regularity SQI, and baseline stability SQI.

Parameters:

• lead (Lead | ndarray[tuple[Any, ...], dtype[double]]) – Input ECG lead or raw signal array.

• fs (int | None) – Sample rate in Hz. Required when lead is a numpy array.

Returns:

Score between 0.0 (unusable) and 1.0 (excellent).

Return type:

float

ecgdatakit.processing.classify_quality(lead, *, fs=None)

Classify signal quality as a human-readable category.

Parameters:

• lead (Lead | ndarray[tuple[Any, ...], dtype[double]]) – Input ECG lead or raw signal array.

• fs (int | None) – Sample rate in Hz. Required when lead is a numpy array.

Returns:

"excellent" (SQI > 0.8), "acceptable" (0.5–0.8), or "unacceptable" (< 0.5).

Return type:

str

ecgdatakit.processing.snr_estimate(lead, *, fs=None)

Estimate signal-to-noise ratio in dB.

Uses a frequency-domain approach: signal power in 1–40 Hz vs noise power above 100 Hz (up to Nyquist).

Parameters:

• lead (Lead | ndarray[tuple[Any, ...], dtype[double]]) – Input ECG lead or raw signal array.

• fs (int | None) – Sample rate in Hz. Required when lead is a numpy array.

Returns:

Estimated SNR in decibels.

Return type:

float