def load_data(len_packet=25):
# LABELED DATASET
dataset = np.load('./data/morfology.npy', allow_pickle=True).item()
list_ecg = dataset['signal']
list_peaks = dataset['peaks']
list_labels = dataset['label']
list_of_packets = []
for (sig, peak, label) in zip(list_ecg, list_peaks, list_labels):
sig = np.subtract(sig, np.mean(sig))
if label == 2:
label = 'qRs_4'
if label == 14:
label = 'QS_5'
if label == 10:
label = 'rRs_8'
if label == 5:
label = 'RS_9'
if label not in [12, 11, 1, 18, 6, 15, -1]:
list_of_packets.append(fb.LabeledPacket(np.array(sig),
peak, label))
real_m_model = fb.ECG.build_from_packets(list_of_packets)
list_ecg = real_m_model.build_packets(len_packet)
list_ecg = real_m_model.packets
labels = []
for packet in list_ecg:
labels.append(packet.label)
return list_ecg, labels
def main():
dataset = np.load('./data/morfology.npy').item()
n = 600
list_ecg = dataset['signal'][:n]
list_peaks = dataset['peaks'][:n]
list_labels = dataset['label'][:n]
list_of_packets = []
for (sig, peak, label) in zip(list_ecg, list_peaks, list_labels):
sig == np.subtract(sig, np.mean(sig))
list_of_packets.append(fb.LabeledPacket(np.array(sig), peak, label))
my_ecg = fb.ECG.build_from_packets(list_of_packets)
len_packet = 35
list_ecg = my_ecg.build_packets(len_packet)
list_ecg = my_ecg.packets
return list_ecg
def load_new_data(len_packet=25):
# LABELED DATASET
dataset = loadmat('data/morfologies_2019.mat')
list_ecg = dataset['cell_data']['beats']
list_peaks = dataset['cell_data']['peaks']
list_labels = dataset['cell_data']['morfology']
list_of_packets = []
for (sig, peak, label) in zip(list_ecg, list_peaks, list_labels):
sig = np.subtract(sig, np.mean(sig))
list_of_packets.append(fb.LabeledPacket(np.array(sig), peak, label))
real_m_model = fb.ECG.build_from_packets(list_of_packets)
list_ecg = real_m_model.build_packets(len_packet)
list_ecg = real_m_model.packets
return list_ecg, list_labels
b, a = butter_bandpass(lowcut, highcut, fs, order=order, analog=analog)
y = lfilter(b, a, data)
return y
dataset = np.load('./data/morfology.npy').item()
n = 600
fs = 360
list_ecg = dataset['signal'][:n]
list_peaks = dataset['peaks'][:n]
list_labels = dataset['label'][:n]
list_labels, b = pd.factorize(list_labels)
list_of_packets = []
for (sig, peak, label) in zip(list_ecg, list_peaks, list_labels):
sig == np.subtract(sig, np.mean(sig))
list_of_packets.append(fb.LabeledPacket(np.array(sig), peak, label))
my_ecg = fb.ECG.build_from_packets(list_of_packets)
len_packet = 35
list_ecg = my_ecg.build_packets(len_packet)
list_ecg = [x.input_signal for x in my_ecg.packets]
df = pd.DataFrame(list_ecg).values
df = df.T
#filters = dict()
#filters['T'] = (.1, .20)
#filters['P'] = (.15, .30)
#filters['QRS'] = (.3, .5)
#filters['ALL'] = (.70, .9)
#
#analog_filters = dict()
def main():
dataset = np.load('./data/morfology.npy').item()
n = 60
list_ecg = dataset['signal'][:n]
list_peaks = dataset['peaks'][:n]
list_labels = dataset['label'][:n]
# # Wavelet Transform delineation
# for s in list_ecg:
# s = np.array(s)
# sig = signal.resample(s, 1000)
# Pwav, QRS, Twav = wav.signalDelineation(sig,1000)
# if(len(QRS)>0):
# QRS=QRS[0]
# plt.plot(sig[QRS[0]:QRS[-1]])
# plt.show()
# plt.close()
list_of_packets = []
for (sig, peak, label) in zip(list_ecg, list_peaks, list_labels):
sig == np.subtract(sig, np.mean(sig))
list_of_packets.append(fb.LabeledPacket(np.array(sig), peak, label))
my_ecg = fb.ECG.build_from_packets(list_of_packets)
len_packet = 35
list_ecg = my_ecg.build_packets(len_packet)
list_ecg = my_ecg.packets
# ecg_signal = electrocardiogram()
## ecg_signal = ecg_signal - np.mean(ecg_signal)
(ts, filtered, rpeaks, templates_ts, templates, heart_rate_ts,
heart_rate) = ecg.ecg(signal=a, sampling_rate=960, show=False)
# my_ecg = fb.ECG(input_signal=filtered, indicators=rpeaks,
# is_filtered=False)
# list_ecg = my_ecg.build_packets(25, fb.DefaultPacket)
#
# pbounds = {'a': (3, 10), 'b': (30, 100),
# 'c': (3, 10), 'd': (30, 100),
# 'e': (10, 60),'f': (10, 60)}
#
# norm_pbounds = {'a': (0.1, 0.3), 'b': (30, 100),
# 'c': (0.1, 0.3), 'd': (30, 100),
# 'e': (1, 50),'f': (1, 50)}
# parameter = dict()
# parameter['values'] = list(zip(np.linspace(1, 10, 15),
# np.linspace(1, 10, 15)))
# parameter['lenght'] = list(zip(np.linspace(len_packet*0.6, len_packet*6, 10),
# np.linspace(len_packet*0.6, len_packet*6, 10)))
# parameter['cut'] = list(zip(np.linspace(0, 60, 7),
# np.linspace(0, 60, 7)))
#
#
#
# l = []
# for i in np.linspace(1, 10, 10):
# for j in np.linspace(1, 10, 10):
# l.append((i, j))
#
# r_parameter = dict()
# r_parameter['values'] = l
# r_parameter['lenght'] = list(zip(np.linspace(len_packet*0.6, len_packet*6, 5),
# np.linspace(len_packet*0.6, len_packet*6, 5)))
# r_parameter['cut'] = list(zip(np.linspace(0, 60, 7),
# np.linspace(0, 60, 7)))
#
# template_gaussian = fb.Gaussian(parameter)
# template_mexican = fb.MexicanHat(parameter)
# template_rayleigh = fb.Rayleigh(r_parameter)
# template_left_rayleigh = fb.LeftInverseRayleigh(parameter)
# template_right_rayleigh = fb.RightInverseRayleigh(parameter)
#
# list_of_templates = [template_gaussian, template_mexican,
# template_rayleigh, template_left_rayleigh,
# template_right_rayleigh]
list_of_templates = utils.get_default_templates(len_packet)
# list_of_templates = [template_rayleigh]
lst_of_packets = utils.mp_signal_apply(utils.consume_packet_helper,
list_ecg, list_of_templates)
# lst_of_packets = utils.serial_signal_apply(utils.consume_packet,
# list_ecg, list_of_templates)
my_ecg.packets = lst_of_packets
return my_ecg