API¶

Physiological data¶

class peakdet.Physio(data, fs=None, history=None, metadata=None)[source]¶

Class to hold physiological data and relevant information

Parameters:	data (array_like) – Input data array fs (float, optional) – Sampling rate of data (Hz). Default: None history (list of tuples, optional) – Functions performed on data. Default: None metadata (dict, optional) – Metadata associated with data. Default: None

data¶

Physiological waveform

Type:	`numpy.ndarray`

fs¶

Sampling rate of data in Hz

Type:	float

history¶

History of functions that have been performed on data, with relevant parameters provided to functions.

Type:	list of tuples

peaks¶

Indices of peaks in data

Type:	`numpy.ndarray`

troughs¶

Indices of troughs in data

Type:	`numpy.ndarray`

Loading data¶

peakdet.load_physio(data, *, fs=None, dtype=None, history=None, allow_pickle=False)[source]¶

Returns Physio object with provided data

Parameters:	data (str or array_like or Physio_like) – Input physiological data. If array_like, should be one-dimensional fs (float, optional) – Sampling rate of data. Default: None dtype (data_type, optional) – Data type to convert data to, if conversion needed. Default: None history (list of tuples, optional) – Functions that have been performed on data. Default: None allow_pickle (bool, optional) – Whether to allow loading if data contains pickled objects. Default: False
Returns:	data – Loaded physiological data
Return type:	`peakdet.Physio`
Raises:	`TypeError` – If provided data is unable to be loaded

peakdet.load_history(file, verbose=False)[source]¶

Loads history from file and replays it, creating new Physio instance

Parameters:	file (str) – Path to input JSON file verbose (bool, optional) – Whether to print messages as history is being replayed. Default: False
Returns:	file – Full filepath to saved output
Return type:	str

peakdet.load_rtpeaks(fname, channel, fs)[source]¶

Loads data file as obtained from the rtpeaks Python module

Data file fname should have a single, comma-delimited header of format:

time,channel#,channel#,…,channel#

Raw data should be stored in columnar format, also comma-delimited, beneath this header. All data should be stored as integers. For more information, see the rtpeaks homepage: https://github.com/rmarkello/rtpeaks.

Parameters:	fname (str) – Path to data file to be loaded channel (int) – Integer corresponding to the channel number in fname from which data should be loaded fs (float) – Sampling rate at which fname was acquired
Returns:	data – Loaded physiological data
Return type:	`peakdet.Physio`

Processing data¶

Functions for processing and interpreting physiological data

peakdet.operations.interpolate_physio(data, target_fs, *, kind='cubic')[source]¶

Interpolates data to desired sampling rate target_fs

Parameters:	data (Physio_like) – Input physiological data to be interpolated target_fs (float) – Desired sampling rate for data kind (str or int, optional) – Type of interpolation to perform. Must be one of available kinds in `scipy.interpolate.interp1d()`. Default: ‘cubic’
Returns:	interp – Interpolated input data
Return type:	`peakdet.Physio`

peakdet.operations.filter_physio(data, cutoffs, method, *, order=3)[source]¶

Applies an order-order digital method Butterworth filter to data

Parameters:	data (Physio_like) – Input physiological data to be filtered cutoffs (int or list) – If method is ‘lowpass’ or ‘highpass’, an integer specifying the lower or upper bound of the filter (in Hz). If method is ‘bandpass’ or ‘bandstop’, a list specifying the lower and upper bound of the filter (in Hz). method ({'lowpass', 'highpass', 'bandpass', 'bandstop'}) – The type of filter to apply to data order (int, optional) – Order of filter to be applied. Default: 3
Returns:	filtered – Filtered input data
Return type:	`peakdet.Physio`

peakdet.operations.peakfind_physio(data, *, thresh=0.2, dist=None)[source]¶

Performs peak and trough detection on data

Parameters:	data (Physio_like) – Input data in which to find peaks thresh (float [0,1], optional) – Relative height threshold a data point must surpass to be classified as a peak. Default: 0.2 dist (int, optional) – Distance in indices that peaks must be separated by in data. If None, this is estimated. Default: None
Returns:	peaks – Input data with detected peaks and troughs
Return type:	`peakdet.Physio`

peakdet.operations.plot_physio(data, *, ax=None)[source]¶

Plots data and associated peaks / troughs

Parameters:	data (Physio_like) – Physiological data to plot ax (`matplotlib.axes.Axes`, optional) – Axis on which to plot data. If None, a new axis is created. Default: None
Returns:	ax – Axis with plotted data
Return type:	`matplotlib.axes.Axes`

peakdet.operations.edit_physio(data)[source]¶

Opens interactive plot with data to permit manual editing of time series

Parameters:	data (Physio_like) – Physiological data to be edited
Returns:	edited – Input data with manual edits
Return type:	`peakdet.Physio`

Saving data¶

peakdet.save_physio(fname, data)[source]¶

Saves data to fname

Parameters:	fname (str) – Path to output file; .phys will be appended if necessary data (Physio_like) – Data to be saved to file
Returns:	fname – Full filepath to saved output
Return type:	str

peakdet.save_history(file, data)[source]¶

Saves history of physiological data to file

Saved file can be replayed with peakdet.load_history

Parameters:	file (str) – Path to output file; .json will be appended if necessary data (Physio_like) – Data with history to be saved to file
Returns:	file – Full filepath to saved output
Return type:	str

Derived metrics¶

class peakdet.HRV(data)[source]¶

Class for calculating various HRV statistics

Parameters:	data (Physio_like) – Physiological data object with detected peaks and troughs

rrint¶

R-R intervals derived from data (sometimes referred to as N-N intervals in derived metrics)

Type:	`numpy.ndarray`

rrtime¶

Time stamps of rrint

Type:	`numpy.ndarray`

avgnn¶

Average heart rate (N-N interval)

Type:	float

sdnn¶

Standard deviation of heart rate (N-N intervals)

Type:	float

rmssd¶

Root mean square of successive differences

Type:	float

sdsd¶

Standard deviation of successive differences

Type:	float

nn50¶

Number of N-N intervals greater than 50ms

Type:	float

pnn50¶

Percent of N-N intervals greater than 50ms

Type:	float

nn20¶

Number of N-N intervals greater than 20ms

Type:	float

pnn20¶

Percent of N-N intervals greater than 20ms

Type:	float

hf¶

High-frequency power of R-R intervals, summed across 0.15-0.40 Hz

Type:	float

hf_log¶

Log of hf

Type:	float

lf¶

Low-frequency power of R-R intervals, summed across 0.04-0.15 Hz

Type:	float

lf_log¶

Log of lf

Type:	float

vlf¶

Very low frequency power of R-R intervals, summed across 0-0.04 Hz

Type:	float

vlf_log¶

Log of vlf

Type:	float

lftohf¶

Ratio of lf over hf

Type:	float

hf_peak¶

Peak frequency in hf band (0.15-0.40 Hz)

Type:	float

lf_peak¶

Peak frequency in lf band (0.04-0.15 Hz)

Type:	float

Notes

Uses scipy.signal.welch for calculation of frequency-based statistics