timeflux_dsp.nodes.quality

quality

class timeflux_dsp.nodes.quality.Discretize(range, default=None)

Bases: timeflux.core.node.Node

Discretize data based on amplitude range.

Attributes: i (Port): Default input, expects DataFrame. o (Port): Default output, provides DataFrame.

Parameters

  • range (dict) – Dictionary with keys are discrete value and values

  • ranges. (are tuple with corresponding data) –

  • default – Default discrete value (for data that are not contained in any range)

Instantiate the node.

update()

Update the input and output ports.

class timeflux_dsp.nodes.quality.ECGQuality(rate, length, step)

Bases: timeflux.nodes.window.Window

Estimate ECG Quality

This nodes estimates ECG Quality using neurokit toolbox, by applying function ecg_process on a rolling window.

Variables

  • i (Port) – Default input, expects DataFrame.

  • o (Port) – Default output, provides DataFrame.

Parameters

  • rate (float) – Nominal sampling rate of the input data. If None, rate is get from the meta.

  • length (float) – The length of the window, in seconds.

  • step (float) – The sliding step, in seconds.

Instantiate the node.

update()

Update the input and output ports.

class timeflux_dsp.nodes.quality.LineQuality(rate, range, window_length=3, window_step=0.5, bandpass_frequencies=(1, 65), line_centers=(50, 100, 150))

Bases: timeflux.core.branch.Branch

Estimate level of line noise

This nodes estimates LineNoise as the ratio between good power and total power on a rolling window. Good power is defined as the sum of squared samples for signal after bandpass and Notch filtering. Total power is defined as the sum of squared samples for signal after bandpass filtering only.

Variables

  • i (Port) – Default input, expects DataFrame.

  • o (Port) – Default output, provides DataFrame.

Parameters

rate – Nominal sampling rate of the input data. If None, rate is get from the meta.

Instantiate the node.

update()

Update the input and output ports.

class timeflux_dsp.nodes.quality.AmplitudeQuality(range, window_length=3, window_step=0.5, method='ptp')

Bases: timeflux.core.branch.Branch

Estimate discrete signal quality index based on a temporal feature from the amplitude.

This nodes rolls a window and applies a numpy function given by method (eg. ptp, max, min, mean…) over rows and discretize the result based on range .

Variables

  • i (Port) – Default input, expects DataFrame.

  • o (Port) – Default output, provides DataFrame.

Instantiate the node.

update()

Update the input and output ports.