def get_list_of_intervals(signals_af):
"""Getting RR signals and returning list of intervals"""
# Wavelet filtration
numbers_of_signals = len(signals_af[0, :])
numbers_of_samples = len(signals_af[:, 0])
for i in range(numbers_of_signals):
if numbers_of_samples % 2 == 1:
signals_af = signals_af[:numbers_of_samples - 1, :]
signals_af[:, i] = wavelet_analysis.filter_signal(signals_af[:, i],
wavelet="db6",
highcut=False)
# Finding R-waves
analyzerQRS = bothChannelsQRSDetector.BothChannelsQRSDetector()
combined_rr = analyzerQRS.compare(signals_af,
record_file=file_path,
plotting=False,
qrs_reading=False)
# Creating list of isoelectrics lines
rr_ia = RRIntervalsAnalyser.RRIntervalsAnalyser(analyzerQRS)
list_of_intervals_chann0, rr_distances_chann0 = rr_ia.get_intervals(
combined_rr, channel_no=0, time_margin=0.1)
list_of_intervals_chann1, rr_distances_chann1 = rr_ia.get_intervals(
combined_rr, channel_no=1, time_margin=0.1)
return (list_of_intervals_chann0, list_of_intervals_chann1)
database="ptb")
# PARAMS
record_atrial_fibrillation.set_frequency(250)
record_normal.set_frequency(1000)
start = 1094000 # 1417000
interval = 20 #164
# SIGNALS
signals_af = record_atrial_fibrillation.get_signals(start, interval)
# RESAMPLING
# signals_norm = signal.resample(signals_norm, num=len(signals_norm[:,0])/4)
# QRS Analysis and RR Analysis
analyzerQRS = bothChannelsQRSDetector.BothChannelsQRSDetector()
combined_rr = analyzerQRS.compare(signals_af, record_file=path_af)
rr_ia = RRIntervalsAnalyser.RRIntervalsAnalyser(analyzerQRS)
list_of_intervals_chann0, rr_distances_chann0 = rr_ia.get_intervals(
combined_rr, channel_no=0, time_margin=0.1)
list_of_intervals_chann1, rr_distances_chann1 = rr_ia.get_intervals(
combined_rr, channel_no=1, time_margin=0.1)
list_of_isoelectrics_chann0 = rr_ia.get_list_of_intervals_isoelectric_line(
list_of_intervals_chann0, interval_samples=100)
list_of_isoelectrics_chann1 = rr_ia.get_list_of_intervals_isoelectric_line(
list_of_intervals_chann1, interval_samples=100)
pca = dimensionAnalyzer.PCADimensionAnalyser()
ica = dimensionAnalyzer.ICADimensionAnalyzer()
patient_id = patient_id.replace("\n", "")
subdir_patientname, record_no = patient_id.split("/")
if subdir_patientname == "learning-set":
filepath = path.join("downloads", "af_term", subdir_patientname,
record_no)
record = wfdb.rdsamp(filepath)
n = calculate_number_of_samples(time_of_record, fs=128)
signals, fields = wfdb.srdsamp(filepath,
sampfrom=record.siglen - n - 1,
sampto=record.siglen - 1,
channels=None,
pbdir=None)
# GETTING RR INTERVALS
rr_detector = bothChannelsQRSDetector.BothChannelsQRSDetector()
combined_rr = rr_detector.compare(signals=signals[:, 0:2],
sampling_ratio=128)
rr_ia = RRIntervalsAnalyser.RRIntervalsAnalyser(
sampling_ratio=128, signals=signals[:, 0:2])
a = rr_ia.get_intervals(combined_rr, channel_no=0, time_margin=0.1)
list_of_intervals_chann0, rr_distances_chann0 = rr_ia.get_intervals(
combined_rr, channel_no=0, time_margin=0.1)
list_of_intervals_chann1, rr_distances_chann1 = rr_ia.get_intervals(
combined_rr, channel_no=1, time_margin=0.1)
# SAVE TO DICT WITH PATIENT INFO
patient_name = record_no
data_for_patient = {
"channel0": list_of_intervals_chann0,
"channel1": list_of_intervals_chann1