def get_peaks_ecg(subject, inputs, outputs, recompute, logpath):
"""Detect R-peaks in ECG.
1. Detect R-peaks
2. autocorrect artifacts in R-peaks detection.
"""
save_path = Path(
individualize_path(outputs["save_path"], subject, expand_name=True))
if save_path.exists() and not recompute: # only recompute if requested
print(f"Not re-computing {save_path}")
return
physio_path = next(
Path(".").glob(individualize_path(inputs["physio_path"], subject)))
ecg = np.ravel(pd.read_csv(physio_path, sep="\t", header=None))
# Detect R-peaks.
peaks = ecg_peaks(ecg, ECG_SFREQ_DECIMATED)
# Correct artifacts in peak detection.
peaks_corrected = correct_peaks(peaks, ECG_SFREQ_DECIMATED, iterative=True)
# Save peaks as samples.
pd.Series(peaks_corrected).to_csv(save_path,
sep="\t",
header=False,
index=False,
float_format="%.4f")
if not logpath:
return
fig, ax = plt.subplots(nrows=1, ncols=1)
sec = np.linspace(0, len(ecg) / ECG_SFREQ_DECIMATED, len(ecg))
ax.plot(sec, ecg)
ax.scatter(sec[peaks],
ecg[peaks],
zorder=3,
c="r",
marker="+",
s=300,
label="uncorrected R-peaks")
ax.scatter(sec[peaks_corrected],
ecg[peaks_corrected],
zorder=4,
c="g",
marker="x",
s=300,
label="corrected R-peaks")
ax.set_xlabel("seconds")
ax.legend(loc="upper right")
fig.savefig(logpath, dpi=200)
plt.close(fig)
for record, annotation in zip(records, annotations):
try:
ecg = np.ravel(pd.read_csv(record, sep=" ", usecols=[1],
header=None))
except FileNotFoundError:
print(f"no ECG available for {record}")
continue
try:
manupeaks = np.ravel(pd.read_csv(annotation, header=None))
except FileNotFoundError:
print(f"no annotations available for {annotation}")
continue
algopeaks = ecg_peaks(ecg, sfreq)
if algopeaks.size > 1:
comparitor = compare_annotations(manupeaks, algopeaks, tolerance)
tp = comparitor.tp
fp = comparitor.fp
fn = comparitor.fn
# plt.figure()
# plt.plot(ecg)
# plt.scatter(manupeaks, ecg[manupeaks], c="m")
# plt.scatter(algopeaks, ecg[algopeaks], c='g', marker='X', s=150)
sensitivity.append(float(tp) / (tp + fn))
precision.append(float(tp) / (tp + fp))
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from biopeaks import heart
from matplotlib.patches import FancyArrowPatch
from matplotlib import rc
# make all text bold
rc('font', weight='bold')
sfreq = 100
signal = np.ravel(pd.read_csv("ecg_snippet.tsv", sep="\t"))
peaks = heart.ecg_peaks(signal, sfreq)
period, _ = heart.heart_period(peaks, sfreq, signal.size)
minsignal = min(signal)
maxsignal = max(signal)
fig, (ax0, ax1) = plt.subplots(nrows=2, ncols=1, figsize=(7.2, 4.45))
fig.subplots_adjust(hspace=.05)
ax0td = ax0.transData
ax1td = ax1.transData
figtf = fig.transFigure
figtf_inv = figtf.inverted()
def test_ecg_peaks(ecg_data):
test_extrema = ecg_peaks(ecg_data["signal"], ecg_data["sfreq"])
assert np.allclose(np.sum(test_extrema), 202504458, atol=5)