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import numpy as np
from scipy.signal import find_peaks, stft, lfilter, butter, welch
from plotly.subplots import make_subplots
from plotly.colors import n_colors
import plotly.graph_objects as go
from biosppy.signals import ecg
class ECGsignal:
"""
Manage (multi-channel, row-wise) BVP signals
"""
verb = False # verbose (True)
nFFT = 4*4096 # freq. resolution for STFTs
step = 1 # step in seconds
minHz = .75 # 39 BPM - min freq.
maxHz = 4. # 240 BPM - max freq.
def __init__(self, data, fs, startTime=0):
#self.data = data
if len(data.shape) == 1:
self.data = data.reshape(1,-1) # 2D array raw-wise
self.fs = fs # sample rate
self.startTime = startTime
def getBPM(self, winsize=5):
"""
Compute the ECG signal by biosppy library
"""
# TODO: to handle all channels
data = self.data[0,:]
out = ecg.ecg(signal=data, sampling_rate=self.fs, show=False)
self.times = out['heart_rate_ts']
self.bpm = out['heart_rate']
self.peaksIdX = out['rpeaks']
return self.bpm, self.times
def autocorr(self):
from statsmodels.graphics.tsaplots import plot_acf, plot_pacf
# TODO: to handle all channels
x = self.data[0,:]
plot_acf(x)
plt.show()
plot_pacf(x)
plt.show()
def plot(self):
"""
Plot the the ECG signals (one channels)
"""
# TODO: to handle all channels
data = self.data[0,:]
N = len(data)
times = np.linspace(self.startTime, N/self.fs, num=N, endpoint=False)
# -- plot the channel
fig = make_subplots(rows=1, cols=1)
fig.add_trace(go.Scatter(x=times, y=data, name='ECG'), row=1, col=1)
fig.add_trace(go.Scatter(x=self.peaksIdX/self.fs, y=data[self.peaksIdX], mode='markers', name='peaks'), row=1, col=1)
fig.update_layout(height=600, width=800)
fig.show() |