def calculate_hr(signal_data, timestamps=None):
sampling_rate = 47.63
if timestamps is not None:
sampling_rate = hp.get_samplerate_mstimer(timestamps)
try:
wd, m = hp.process(signal_data, sample_rate=sampling_rate)
hr_bpm = m["bpm"]
except:
hr_bpm = 75.0
if np.isnan(hr_bpm):
hr_bpm = 75.0
return hr_bpm
else:
# We are working with predicted HR:
# need to filter and do other stuff.. lets see
signal_data = hp.filter_signal(signal_data,
cutoff=[0.7, 2.5],
sample_rate=sampling_rate,
order=6,
filtertype='bandpass')
try:
wd, m = hp.process(signal_data,
sample_rate=sampling_rate,
high_precision=True,
clean_rr=True)
hr_bpm = m["bpm"]
except:
print(
"BadSignal received (could not be filtered) using def HR value = 75bpm"
)
hr_bpm = 75.0
return hr_bpm
def filter_and_resample_truth_signal(signal_df, resampling_size):
# Signal should be bandpass filtered to remove noise outside of expected HR frequency range.
# But we are using CLEANER_PPG signals which are considered filtered.
orignal_sample_rate = hp.get_samplerate_mstimer(signal_df["Time"].values)
# filtered = hp.filter_signal(signal_df["Signal"].values, [0.7, 2.5], sample_rate=sample_rate,
# order=3, filtertype='bandpass')
resampled_signal = signal.resample(signal_df["Signal"].values,
resampling_size,
t=signal_df["Time"].values)
# we'll need to add resampled[1]
return resampled_signal[0], resampled_signal[1]
def medicion_ritmo_cardiaco():
# Comencemos con nuestro primer ejemplo
# Señal capturada con un pulsómetro con la técnica
# de fotoplestimografía (PPG).
data, timer = hp.load_exampledata(0)
sample_rate = 100
plt.figure()
plt.plot(data)
plt.title("Ritmo cardíaco: Ejemplo 1")
plt.legend(['ritmo cardíaco'])
plt.ylabel('pulsos')
plt.xlabel('muestras')
plt.show()
# Utilizaremos la libreria scipy para detectar picos
# https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.find_peaks.html
sample_rate = 100
peaks, _ = find_peaks(data, distance=50)
# Plotear los picos
plt.figure()
plt.plot(data)
plt.plot(peaks, data[peaks], "x")
plt.title("Ritmo cardíaco: Ejemplo 1")
plt.legend(['ritmo cardíaco', 'pulsos'])
plt.ylabel('pulsos')
plt.xlabel('muestras')
mplcursors.cursor(multiple=True)
plt.show()
# Calcular el ritmo cardíaco como la diferencia
# de tiempo entre los picos
delta_time_rate = np.diff(peaks)
delta_time = delta_time_rate / sample_rate
promedio = np.mean(delta_time)
pulsaciones_por_minuto = promedio * 60
print("Pulsaciones", pulsaciones_por_minuto)
# Ahora veamos un ejemplo más real de uso, en donde se puede
# ver el ruido introducido por la colocación del instrumento
# y los movimientos del paciente.
data, timer = hp.load_exampledata(1)
sample_rate = hp.get_samplerate_mstimer(timer)
plt.figure()
peaks, _ = find_peaks(data, distance=sample_rate / 2.0)
plt.plot(data)
plt.plot(peaks, data[peaks], "x")
plt.title("Ritmo cardíaco: Ejemplo 2")
plt.legend(['ritmo cardíaco', 'pulsos'])
plt.ylabel('pulsos')
plt.xlabel('muestras')
mplcursors.cursor(multiple=True)
plt.show()
delta_time = np.diff(peaks) / 100
promedio = np.mean(delta_time)
pulsaciones_por_minuto = promedio * 60
print("Pulsaciones", pulsaciones_por_minuto)
# Buscar picos que prevalezcan superiores a su entorno por más del tiempo
# definido (medio ciclo de la señal) --> "prominencia"
peaks, properties = find_peaks(data, prominence=1, width=sample_rate / 2.0)
plt.figure()
plt.plot(data)
plt.plot(peaks, data[peaks], "x")
plt.vlines(x=peaks,
ymin=data[peaks] - properties["prominences"],
ymax=data[peaks],
color="C1")
plt.hlines(y=properties["width_heights"],
xmin=properties["left_ips"],
xmax=properties["right_ips"],
color="C1")
mplcursors.cursor(multiple=True)
plt.show()
# Veamos ahora el potencial de heartpy
# Autor: Paul van Gent
# https://github.com/paulvangentcom/heartrate_analysis_python/blob/master/examples/1_regular_PPG/Analysing_a_PPG_signal.ipynb
# Ejemplo 1
data, timer = hp.load_exampledata(0)
sample_rate = 100
wd, m = hp.process(data, sample_rate=sample_rate)
hp.plotter(wd, m)
print("Pulsaciones", m['bpm'])
# Ejemplo 2
data, timer = hp.load_exampledata(1)
sample_rate = hp.get_samplerate_mstimer(timer)
wd, m = hp.process(data, sample_rate=sample_rate)
hp.plotter(wd, m)
print("Pulsaciones", m['bpm'])
import heartpy as hp
import numpy
import csv
# convert txt file to csv
#txt_file_address = 'E:\\Lithic\\data\\heart\\heart-bill.txt'
txt_file_address = 'HR_2019-09-22T14-19-17.txt'
csv_file_address = txt_file_address.replace('txt','csv')
in_txt = csv.reader(open(txt_file_address, "r"), delimiter = ',')
out_csv = csv.writer(open(csv_file_address, 'w'))
out_csv.writerows(in_txt)
print(csv_file_address)
hrdata = hp.get_data(csv_file_address, column_name='Analog1_chB')
timerdata = hp.get_data(csv_file_address, column_name='Timestamp')
working_data, measures = hp.process(hrdata, hp.get_samplerate_mstimer(timerdata))
hp.plotter(working_data, measures, title='good plot')
import serial
import numpy as np
import heartpy as hp
import matplotlib
import csv
import pprint
from hrvanalysis import *
file = "samples/example.csv"
print("\nLoading sample data from", file + "\n")
data = np.loadtxt(file, delimiter=",", skiprows=(1), usecols=[1])
timer = np.loadtxt(file, delimiter=",", skiprows=(1), usecols=[0])
timer = timer*1000
fp = hp.get_samplerate_mstimer(timer)
print("Detected sampling frequency of", str(round(fp, 2)), "hertz.\n")
print("Preprocessing ECG data.\n")
data = hp.preprocess_ecg(data, fp)
print("Processing resulting data through heartpy.\n")
working_data, measures = hp.process(
data, fp*2, reject_segmentwise=True, calc_freq=True)
print("Resulting measures (heartpy):\n")
pprint.pprint(measures)
print("\nProcessing heartpy's data using hrvanalysis.\n")
rrintervals = working_data["RR_list_cor"]
rrintervalswo = remove_outliers(rr_intervals=rrintervals,
low_rri=300, high_rri=2000)
intervals = interpolate_nan_values(rr_intervals=rrintervalswo,
interpolation_method="linear")