Finished converting to physio inputs

Author: bbfrederick

physioqc/metrics/chest_belt.py

@@ -9,12 +9,12 @@
 
 from .. import references
 from ..due import due
-from .utils import hamming, physio_or_numpy
+from .utils import hamming
 
 
 def envelopedetect(data, upperpass=0.05, order=5):
     """
-
+    Detects the amplitude envelope of a pseudoperiodic waveform (respiration, cardiac, etc.)
     Parameters
     ----------
     data
@@ -55,30 +55,80 @@ def envelopedetect(data, upperpass=0.05, order=5):
     return einferior, esuperior
 
 
+def respiratorysqi(rawresp, debug=False):
+    """
+    Calculate the breath by breath quality of the respiratory signal
+    Parameters
+    ----------
+    rawresp: physio_like
+        The raw respiratory signal
+    debug: bool
+        Print debug information and plot intermediate results
+
+    Returns
+    -------
+    breathlist: list
+        List of "breathinfo" dictionaries for each detected breath.  Each breathinfo dictionary contains:
+            "startime", "centertime", and "endtime" of each detected breath in seconds from the start of the waveform,
+            and "correlation" - the Pearson correlation of that breath waveform with the average waveform.
+
+    """
+    targetfs = 25.0
+    prefilterlimits = [0.01, 2.0]
+    seglength = 12.0
+    segstep = 2.0
+    envelopelpffreq = 0.05
+    slidingfilterpctwidth = 10.0
+
+    return romanosqi(
+        rawresp,
+        targetfs=targetfs,
+        prefilterlimits=prefilterlimits,
+        envelopelpffreq=envelopelpffreq,
+        slidingfilterpctwidth=slidingfilterpctwidth,
+        seglength=seglength,
+        segstep=segstep,
+        debug=debug,
+    )
+
+
 @due.dcite(references.ROMANO_2023)
-def respiratorysqi(
-    rawresp, Fs, envelopelpffreq=0.075, slidingfilterpctwidth=10.0, debug=False
+def romanosqi(
+    rawresp,
+    targetfs=25.0,
+    prefilterlimits=(0.01, 2.0),
+    envelopelpffreq=0.05,
+    slidingfilterpctwidth=10.0,
+    seglength=12.0,
+    segstep=2.0,
+    debug=False,
 ):
     """
     Implementation of Romano's method from A Signal Quality Index for Improving the Estimation of
     Breath-by-Breath Respiratory Rate During Sport and Exercise,
     IEEE SENSORS JOURNAL, VOL. 23, NO. 24, 15 DECEMBER 2023
 
     NB: In part A, Romano does not specify the upper pass frequency for the respiratory envelope filter.
-        0.075Hz looks pretty good.
+        0.05Hz looks pretty good for respiration.
         In part B, the width of the sliding window bandpass filter is not specified.  We use a range of +/- 5% of the
         center frequency.
 
     Parameters
     ----------
-    rawresp: ndarray
+    rawresp: physio_like
         The raw respiration signal
-    Fs: float
-        The sampling frequency of the respiration signal in Hz
+    targetfs: float
+        Sample rate for internal calculations
+    prefilterlimits: tuple
+        Lower and upper frequency limits for prefiltering the input signal
     envelopelpffreq: float
         Cutoff frequency of the RMS envelope filter in Hz
     slidingfilterpctwidth: float
         Width of the sliding window bandpass filter in percent of the center frequency
+    seglength: float
+        Length of the window for measuring the waveform center frequency in seconds
+    segstep: float
+        Step size of the window for measuring the waveform center frequency in seconds
     debug: bool
         Print debug information and plot intermediate results
 
@@ -87,20 +137,17 @@ def respiratorysqi(
     breathlist: list
         List of "breathinfo" dictionaries for each detected breath.  Each breathinfo dictionary contains:
             "startime", "centertime", and "endtime" of each detected breath in seconds from the start of the waveform,
-            and "correlation" - the Pierson correlation of that breath waveform with the average waveform.
+            and "correlation" - the Pearson correlation of that breath waveform with the average waveform.
     """
-    # rawresp = physio_or_numpy(rawresp)
 
     # get the sample frequency down to around 25 Hz for respiratory waveforms
-    targetfs = 25.0
-    rawresp = Physio(rawresp, fs=Fs)
     rawresp = operations.interpolate_physio(rawresp, target_fs=targetfs)
 
     # A. Signal Preprocessing
     # Apply third order Butterworth bandpass, 0.01-2Hz
     prefiltered = operations.filter_physio(
         rawresp,
-        [0.01, 2.0],
+        prefilterlimits,
         "bandpass",
         order=3,
     )
@@ -147,10 +194,8 @@ def respiratorysqi(
         )
         plt.show()
 
-    # B. Detection of breaths in sliding window
-    seglength = 12.0
+    # B. Detection of peaks in sliding window
     segsamples = int(seglength * targetfs)
-    segstep = 2.0
     stepsamples = int(segstep * targetfs)
     totaltclength = len(rawresp)
     numsegs = int((totaltclength - segsamples) // stepsamples)
@@ -298,19 +343,17 @@ def plotbreathqualities(breathlist, totaltime=None):
     plt.show()
 
 
-def plotbreathwaveformwithquality(waveform, breathlist, Fs, plottype="rectangle"):
+def plotbreathwaveformwithquality(data, breathlist, plottype="rectangle"):
     """
     Make an informational plot of the respiratory waveform with the quantifiability of each detected breath as a
     color overlay.
 
     Parameters
     ----------
-    waveform: ndarray
+    data: physio_like
         The respiratory waveform
     breathlist: list
         A list of breathinfo dictionaries output from respiratorysqi
-    Fs: float
-        The sampling frequency of the respiratory waveform in Hz
     plottype: str
         The type of plot to make.  If plottype is rectangle (default), overlay a colored rectangle to show the
         quantifiability of each detected breath.  If anything else, use the waveform line color to indicate the
@@ -319,6 +362,9 @@ def plotbreathwaveformwithquality(waveform, breathlist, Fs, plottype="rectangle"
     -------
 
     """
+    waveform = data.data
+    Fs = data.fs
+
     # now plot the respiratory waveform, color coded for quality
 
     # set up the color codes

simpleresptest

@@ -1,7 +1,7 @@
 #!/usr/bin/env python
 
 import numpy as np
-import peakdet.io as pk_io
+from peakdet import Physio
 
 import physioqc.metrics.chest_belt as chest_belt
 import physioqc.metrics.utils as utils
@@ -11,14 +11,14 @@ samplerate, starttime, columns, data, compression, columnsource = utils.readbids
     colspec="respiratory_effort",
 )
 
-rawresp = np.nan_to_num(data.flatten())
+rawresp = Physio(np.nan_to_num(data.flatten()), fs=samplerate)
 
-print(rawresp)
+print(rawresp.data)
 
-breathlist = chest_belt.respiratorysqi(rawresp, samplerate, debug=True)
+breathlist = chest_belt.respiratorysqi(rawresp, debug=True)
 chest_belt.plotbreathqualities(breathlist, totaltime=(len(rawresp) / samplerate))
-chest_belt.plotbreathwaveformwithquality(rawresp, breathlist, samplerate)
+chest_belt.plotbreathwaveformwithquality(rawresp, breathlist)
 chest_belt.plotbreathwaveformwithquality(
-    rawresp, breathlist, samplerate, plottype="other"
+    rawresp, breathlist, plottype="other"
 )
 chest_belt.summarizebreaths(breathlist)