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edb.py
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edb.py
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# -*- coding: utf-8 -*-
import matplotlib.pyplot as plt
import numpy as np
from scipy.fftpack import rfft, rfftfreq
from scipy.signal import stft
import wfdb
import json
from wfdb.processing import find_local_peaks
from sklearn.externals import joblib
from sklearn.preprocessing import MinMaxScaler
import pywt
from utils import *
atr_list = generate_atr_list('./edb_records')
with open('./edb_st_episodes.json') as infile:
st_dict = json.load(infile)
point_mode = 'all' # 'partial' or 'all'
# key = 'N1' # 'ST0-', 'ST1-', 'N0', 'N1'
c_st = 0
c_n = 0
train_dict = {}
for atr in atr_list:
sig_st_dict = load_st_signal(atr, st_dict)
sig_n_dict = get_normal_part(atr, st_dict)
sig_dict = dict(sig_st_dict, **sig_n_dict)
print("++++++Current record {}++++++".format(atr))
print(sig_dict)
# sig_st_dict = load_st_signal('e0606', st_dict)
# sig_n_dict = get_normal_part('e0606', st_dict)
# sig_dict = dict(sig_st_dict, **sig_n_dict)
train_dict[atr] = {}
for key in sig_dict.keys():
if sig_dict[key]:
print("+++Current Type {} and Current length {}+++".format(key, len(sig_dict[key][0])))
sig = sig_dict[key]
# Removal of baseline wandering using wavelet
DenoisingSignal = remove_baseline(sig, 'bior2.6', False)
try:
"""
Binary spline wavelet filtering, level: 4,
low-pass filter:[1/4 3/4 3/4 1/4], high-pass filter:[-1/4 -3/4 3/4 1/4]
"""
points = 10000 if point_mode == 'partial' else DenoisingSignal.shape[0]
level = 4
swa = np.zeros((4, points), dtype=np.float32)
swd = np.zeros((4, points), dtype=np.float32)
signal = DenoisingSignal[0:points]
swa, swd = binary_spline_wavelet_filter(swa, swd, signal, points, 4, False)
"""calculate the positive and negative maximal value pairs"""
w_peak = get_maximal_value_pairs(swd, points, level, False)
interval2 = np.zeros_like(signal)
intervalqs = np.zeros_like(signal)
Mj1 = w_peak[0, :]
Mj3 = w_peak[2, :]
Mj4 = w_peak[3, :]
"""determine if it is inverted, use Mj4"""
_, _, diff_inv = get_difference(w_peak, points, 4)
print(np.where(np.asarray(diff_inv) == 2)[0].shape[0], np.nonzero(np.asarray(diff_inv))[0].shape[0])
if np.where(np.asarray(diff_inv) == 2)[0].shape[0] > 0.5 * np.nonzero(np.asarray(diff_inv))[0].shape[0]:
print("Inverted signal! Skip it!")
continue
interval, loca, diff = get_difference(w_peak, points, 3)
loca2 = np.where(np.array(diff) == -2)[0]
interval2[loca[loca2]] = interval[loca[loca2]]
interval2[loca[loca2 + 1]] = interval[loca[loca2 + 1]]
intervalqs[0:points - 10] = interval2[10:points]
"""Q, R, S, onset, offset detection for each QRS complex"""
Q_located, R_located, S_located, onset_located, offset_located = qrs_delineation(w_peak, points, interval2)
R_R_interval = int(np.mean(R_located[1:] - R_located[:-1]))
print(" mean distance between two successive R peaks:", R_R_interval)
"""find T peaks and pop error points"""
T_located, pop_list_t = find_t_peak(signal, R_R_interval, offset_located)
R_located = np.delete(R_located, pop_list_t)
S_located = np.delete(S_located, pop_list_t)
Q_located = np.delete(Q_located, pop_list_t)
onset_located = np.delete(onset_located, pop_list_t)
offset_located = np.delete(offset_located, pop_list_t)
"""find P peaks and pop error points"""
P_located, pop_list_p = find_p_peak(signal, R_R_interval, onset_located)
R_located = np.delete(R_located, pop_list_p)
S_located = np.delete(S_located, pop_list_p)
Q_located = np.delete(Q_located, pop_list_p)
onset_located = np.delete(onset_located, pop_list_p)
offset_located = np.delete(offset_located, pop_list_p)
T_located = np.delete(T_located, pop_list_p)
# calculate the reference voltage to find F points
mean_idx_diff_1, mean_idx_diff_2, reference_voltage = get_reference_voltage(signal, R_located,
onset_located,
offset_located)
"""find F points and and pop error points"""
F_located, F_located_val, pop_list_f = find_f_point(signal, offset_located, T_located, reference_voltage)
if np.where(F_located_val == 1)[0].shape[0] == F_located.shape[0]:
print("All abnormal F points! Drop it!")
continue
R_located = np.delete(R_located, pop_list_f)
S_located = np.delete(S_located, pop_list_f)
Q_located = np.delete(Q_located, pop_list_f)
onset_located = np.delete(onset_located, pop_list_f)
offset_located = np.delete(offset_located, pop_list_f)
T_located = np.delete(T_located, pop_list_f)
P_located = np.delete(P_located, pop_list_f)
"""Plot the delineation"""
print(R_located.shape, S_located.shape, Q_located.shape, onset_located.shape, offset_located.shape,
T_located.shape, P_located.shape, F_located.shape)
plt.figure(6)
plt.plot(signal)
plt.plot(R_located, signal[R_located], 'rx', marker='x', color='r', label='R', markersize=8)
plt.plot(onset_located, signal[onset_located], 'rx', marker='o', color='k', label='Onset', markersize=6)
plt.plot(offset_located, signal[offset_located], 'rx', marker='o', color='k', label='Offset', markersize=6)
plt.plot(P_located, signal[P_located], 'rx', marker='v', color='g', label='P', markersize=6)
plt.plot(T_located, signal[T_located], 'rx', marker='v', color='g', label='T', markersize=6)
plt.plot(F_located, signal[F_located], 'rx', marker='^', color='c', label='F', markersize=8)
plt.xlabel('Sample')
plt.ylabel('Voltage V')
plt.legend(loc='best')
# plt.show()
"""features from paper Ischemia episode detection......"""
x_1, x_2, x_3 = generate_features(signal, R_located, T_located, F_located, F_located_val,
mean_idx_diff_1, mean_idx_diff_2, reference_voltage)
"""features from paper ECG features and methods......"""
"""morphological features based on QRSPT delineation"""
qrs_i = S_located - Q_located
qt_i = T_located - Q_located
pq_i = Q_located - P_located
jt_i = T_located - offset_located
qrs_v = signal[R_located]
t_v = signal[T_located]
if offset_located[-1] + 5 < signal.shape[0]:
st20_v = signal[offset_located + 5] # 20ms * 1000 / 250 = 5
else:
st20_v = signal[offset_located[:-1] + 5]
st20_v = np.concatenate((st20_v, signal[-1].reshape([1, ])), axis=0)
"""morphological features without QRSPT delineation"""
qrs_argmax = []
qrs_argmin = []
for i in range(R_located.shape[0]):
qrs_argmax.append(np.argmax(signal[R_located[i] - int(R_R_interval / 6):
R_located[i] + int(R_R_interval / 6 * 5)]) +
R_located[i] - int(R_R_interval / 6))
qrs_argmin.append(np.argmin(signal[R_located[i] - int(R_R_interval / 6):
R_located[i] + int(R_R_interval / 6 * 5)]) +
R_located[i] - int(R_R_interval / 6))
qrs_argmax = np.asarray(qrs_argmax).reshape([len(qrs_argmax), 1])
qrs_argmin = np.asarray(qrs_argmin).reshape([len(qrs_argmin), 1])
qrs_max = signal[qrs_argmax]
qrs_min = signal[qrs_argmin]
qrs_max_min = qrs_max - qrs_min
qrs_argmax_argmin = qrs_argmax - qrs_argmin
qrs_a = []
qrst_a = []
pqrst_a = []
for i in range(R_located.shape[0]):
qrs_a.append(np.sum(signal[R_located[i] - int(R_R_interval / 12):
R_located[i] + int(R_R_interval / 12)]) * int(R_R_interval / 6))
qrst_a.append(np.sum(signal[R_located[i] - int(R_R_interval / 12):
R_located[i] + int(R_R_interval / 4)]) * int(R_R_interval / 3))
pqrst_a.append(np.sum(signal[R_located[i] - int(R_R_interval / 6):
R_located[i] + int(R_R_interval / 6 * 5)]) * R_R_interval)
qrs_a = np.asarray(qrs_a).reshape([len(qrs_a), 1])
qrst_a = np.asarray(qrst_a).reshape([len(qrst_a), 1])
pqrst_a = np.asarray(pqrst_a).reshape([len(pqrst_a), 1])
qrs_qrst_ratio = qrs_a / qrst_a
qrs_pqrst_ratio = qrs_a / pqrst_a
qrst_pqrst_ratio = qrst_a / pqrst_a
"""spectral features without QRSPT delineation"""
fft_f1 = []
fft_f2 = []
fft_f3 = []
stft_mean = []
stft_median = []
stft_max = []
for i in range(R_located.shape[0]):
"""FFT"""
each_beat = signal[R_located[i] - int(R_R_interval / 6):
R_located[i] + int(R_R_interval / 6 * 5) + 1]
fftv = rfft(each_beat)
freq = rfftfreq(R_R_interval, 1. / 500)
mask = freq > 0
fft_theo = 2.0 * np.abs(fftv / R_R_interval)
# plt.figure(i)
# ax1 = plt.subplot2grid((2, 1), (0, 0))
# ax1.plot(each_beat, 'r')
# ax1.set_title("Original signal in time domain")
# ax2 = plt.subplot2grid((2, 1), (1, 0))
# ax2.plot(freq[mask], fft_theo[mask], 'g')
# ax2.set_title("Spectrum")
# plt.show()
fft_f1.append(np.sum(fft_theo[:35]))
fft_f2.append(np.sum(fft_theo[35:90]))
fft_f3.append(np.sum(fft_theo[125:250]))
"""STFT"""
f, t, Zxx = stft(each_beat, 500, nperseg=R_R_interval)
Zxx = np.abs(Zxx)
stft_mean.append(np.mean(Zxx))
stft_median.append(np.median(Zxx))
stft_max.append(np.max(Zxx))
# plt.pcolormesh(t, f, np.abs(Zxx))
# plt.title('STFT Magnitude')
# plt.ylabel('Frequency [Hz]')
# plt.xlabel('Time [sec]')
# plt.show()
fft_f1 = np.asarray(fft_f1).reshape([len(fft_f1), 1])
fft_f2 = np.asarray(fft_f2).reshape([len(fft_f2), 1])
fft_f3 = np.asarray(fft_f3).reshape([len(fft_f3), 1])
stft_mean = np.asarray(stft_mean).reshape([len(stft_mean), 1])
stft_median = np.asarray(stft_median).reshape([len(stft_median), 1])
stft_max = np.asarray(stft_max).reshape([len(stft_max), 1])
"""averaging, divide by 5"""
# qrs_il = []
# qt_il = []
# pq_il = []
# jt_il = []
# qrs_vl = []
# t_vl = []
# st20_vl = []
# qrs_argmaxl = []
# qrs_argminl = []
# qrs_maxl = []
# qrs_minl = []
# qrs_max_minl = []
# qrs_argmax_argminl = []
# qrs_al = []
# qrst_al = []
# pqrst_al = []
# qrs_qrst_ratiol = []
# qrs_pqrst_ratiol = []
# qrst_pqrst_ratiol = []
# fft_f1l = []
# fft_f2l = []
# fft_f3l = []
# stft_meanl = []
# stft_medianl = []
# stft_maxl = []
# nbeat = R_located.shape[0]
#
# for i in range(int(nbeat / 5)):
# qrs_il.append(np.sum(qrs_i[5 * i:5 * i + 5]) / 5)
# qt_il.append(np.sum(qt_i[5 * i:5 * i + 5]) / 5)
# pq_il.append(np.sum(pq_i[5 * i:5 * i + 5]) / 5)
# jt_il.append(np.sum(jt_i[5 * i:5 * i + 5]) / 5)
# qrs_vl.append(np.sum(qrs_v[5 * i:5 * i + 5]) / 5)
# t_vl.append(np.sum(t_v[5 * i:5 * i + 5]) / 5)
# st20_vl.append(np.sum(st20_v[5 * i:5 * i + 5]) / 5)
# qrs_argmaxl.append(np.sum(qrs_argmax[5 * i:5 * i + 5]) / 5)
# qrs_argminl.append(np.sum(qrs_argmin[5 * i:5 * i + 5]) / 5)
# qrs_maxl.append(np.sum(qrs_max[5 * i:5 * i + 5]) / 5)
# qrs_minl.append(np.sum(qrs_min[5 * i:5 * i + 5]) / 5)
# qrs_max_minl.append(np.sum(qrs_max_min[5 * i:5 * i + 5]) / 5)
# qrs_argmax_argminl.append(np.sum(qrs_argmax_argmin[5 * i:5 * i + 5]) / 5)
# qrs_al.append(np.sum(qrs_a[5 * i:5 * i + 5]) / 5)
# qrst_al.append(np.sum(qrst_a[5 * i:5 * i + 5]) / 5)
# pqrst_al.append(np.sum(pqrst_a[5 * i:5 * i + 5]) / 5)
# qrs_qrst_ratiol.append(np.sum(qrs_qrst_ratio[5 * i:5 * i + 5]) / 5)
# qrs_pqrst_ratiol.append(np.sum(qrs_pqrst_ratio[5 * i:5 * i + 5]) / 5)
# qrst_pqrst_ratiol.append(np.sum(qrst_pqrst_ratio[5 * i:5 * i + 5]) / 5)
# fft_f1l.append(np.sum(fft_f1[5 * i:5 * i + 5]) / 5)
# fft_f2l.append(np.sum(fft_f2[5 * i:5 * i + 5]) / 5)
# fft_f3l.append(np.sum(fft_f3[5 * i:5 * i + 5]) / 5)
# stft_meanl.append(np.sum(stft_mean[5 * i:5 * i + 5]) / 5)
# stft_medianl.append(np.sum(stft_median[5 * i:5 * i + 5]) / 5)
# stft_maxl.append(np.sum(stft_max[5 * i:5 * i + 5]) / 5)
#
# if nbeat % 5 != 0:
# qrs_il.append(np.sum(qrs_i[int(nbeat / 5) * 5 - len(qrs_i):]) /
# (len(qrs_i) - int(nbeat / 5) * 5))
# qt_il.append(np.sum(qt_i[int(nbeat / 5) * 5 - len(qt_i):]) /
# (len(qt_i) - int(nbeat / 5) * 5))
# pq_il.append(np.sum(pq_i[int(nbeat / 5) * 5 - len(pq_i):]) /
# (len(pq_i) - int(nbeat / 5) * 5))
# jt_il.append(np.sum(jt_i[int(nbeat / 5) * 5 - len(jt_i):]) /
# (len(jt_i) - int(nbeat / 5) * 5))
# qrs_vl.append(np.sum(qrs_v[int(nbeat / 5) * 5 - len(qrs_v):]) /
# (len(qrs_v) - int(nbeat / 5) * 5))
# t_vl.append(np.sum(t_v[int(nbeat / 5) * 5 - len(t_v):]) /
# (len(t_v) - int(nbeat / 5) * 5))
# st20_vl.append(np.sum(st20_v[int(nbeat / 5) * 5 - len(st20_v):]) /
# (len(st20_v) - int(nbeat / 5) * 5))
# qrs_argmaxl.append(np.sum(qrs_argmax[int(nbeat / 5) * 5 - len(qrs_argmax):]) /
# (len(qrs_argmax) - int(nbeat / 5) * 5))
# qrs_argminl.append(np.sum(qrs_argmin[int(nbeat / 5) * 5 - len(qrs_argmin):]) /
# (len(qrs_argmin) - int(nbeat / 5) * 5))
# qrs_maxl.append(np.sum(qrs_max[int(nbeat / 5) * 5 - len(qrs_max):]) /
# (len(qrs_max) - int(nbeat / 5) * 5))
# qrs_minl.append(np.sum(qrs_min[int(nbeat / 5) * 5 - len(qrs_min):]) /
# (len(qrs_min) - int(nbeat / 5) * 5))
# qrs_max_minl.append(np.sum(qrs_max_min[int(nbeat / 5) * 5 - len(qrs_max_min):]) /
# (len(qrs_max_min) - int(nbeat / 5) * 5))
# qrs_argmax_argminl.append(np.sum(qrs_argmax_argmin[int(nbeat / 5) * 5 - len(qrs_argmax_argmin):]) /
# (len(qrs_argmax_argmin) - int(nbeat / 5) * 5))
# qrs_al.append(np.sum(qrs_a[int(nbeat / 5) * 5 - len(qrs_a):]) /
# (len(qrs_a) - int(nbeat / 5) * 5))
# qrst_al.append(np.sum(qrst_a[int(nbeat / 5) * 5 - len(qrst_a):]) /
# (len(qrst_a) - int(nbeat / 5) * 5))
# pqrst_al.append(np.sum(pqrst_a[int(nbeat / 5) * 5 - len(pqrst_a):]) /
# (len(pqrst_a) - int(nbeat / 5) * 5))
# qrs_qrst_ratiol.append(np.sum(qrs_qrst_ratio[int(nbeat / 5) * 5 - len(qrs_qrst_ratio):]) /
# (len(qrs_qrst_ratio) - int(nbeat / 5) * 5))
# qrs_pqrst_ratiol.append(np.sum(qrs_pqrst_ratio[int(nbeat / 5) * 5 - len(qrs_pqrst_ratio):]) /
# (len(qrs_pqrst_ratio) - int(nbeat / 5) * 5))
# qrst_pqrst_ratiol.append(np.sum(qrst_pqrst_ratio[int(nbeat / 5) * 5 - len(qrst_pqrst_ratio):]) /
# (len(qrst_pqrst_ratio) - int(nbeat / 5) * 5))
# fft_f1l.append(np.sum(fft_f1[int(nbeat / 5) * 5 - len(fft_f1):]) /
# (len(fft_f1) - int(nbeat / 5) * 5))
# fft_f2l.append(np.sum(fft_f2[int(nbeat / 5) * 5 - len(fft_f2):]) /
# (len(fft_f2) - int(nbeat / 5) * 5))
# fft_f3l.append(np.sum(fft_f3[int(nbeat / 5) * 5 - len(fft_f3):]) /
# (len(fft_f3) - int(nbeat / 5) * 5))
# stft_meanl.append(np.sum(stft_mean[int(nbeat / 5) * 5 - len(stft_mean):]) /
# (len(stft_mean) - int(nbeat / 5) * 5))
# stft_medianl.append(np.sum(stft_median[int(nbeat / 5) * 5 - len(stft_median):]) /
# (len(stft_median) - int(nbeat / 5) * 5))
# stft_maxl.append(np.sum(stft_max[int(nbeat / 5) * 5 - len(stft_max):]) /
# (len(stft_max) - int(nbeat / 5) * 5))
#
# qrs_il = np.asarray(qrs_il).reshape([len(qrs_il), 1])
# qt_il = np.asarray(qt_il).reshape([len(qt_il), 1])
# pq_il = np.asarray(pq_il).reshape([len(pq_il), 1])
# jt_il = np.asarray(jt_il).reshape([len(jt_il), 1])
# qrs_vl = np.asarray(qrs_vl).reshape([len(qrs_vl), 1])
# t_vl = np.asarray(t_vl).reshape([len(t_vl), 1])
# st20_vl = np.asarray(st20_vl).reshape([len(st20_vl), 1])
# qrs_argmaxl = np.asarray(qrs_argmaxl).reshape([len(qrs_argmaxl), 1])
# qrs_argminl = np.asarray(qrs_argminl).reshape([len(qrs_argminl), 1])
# qrs_maxl = np.asarray(qrs_maxl).reshape([len(qrs_maxl), 1])
# qrs_minl = np.asarray(qrs_minl).reshape([len(qrs_minl), 1])
# qrs_max_minl = np.asarray(qrs_max_minl).reshape([len(qrs_max_minl), 1])
# qrs_argmax_argminl = np.asarray(qrs_argmax_argminl).reshape([len(qrs_argmax_argminl), 1])
# qrs_al = np.asarray(qrs_al).reshape([len(qrs_al), 1])
# qrst_al = np.asarray(qrst_al).reshape([len(qrst_al), 1])
# pqrst_al = np.asarray(pqrst_al).reshape([len(pqrst_al), 1])
# qrs_qrst_ratiol = np.asarray(qrs_qrst_ratiol).reshape([len(qrs_qrst_ratiol), 1])
# qrs_pqrst_ratiol = np.asarray(qrs_pqrst_ratiol).reshape([len(qrs_pqrst_ratiol), 1])
# qrst_pqrst_ratiol = np.asarray(qrst_pqrst_ratiol).reshape([len(qrst_pqrst_ratiol), 1])
# fft_f1l = np.asarray(fft_f1l).reshape([len(fft_f1l), 1])
# fft_f2l = np.asarray(fft_f2l).reshape([len(fft_f2l), 1])
# fft_f3l = np.asarray(fft_f3l).reshape([len(fft_f3l), 1])
# stft_meanl = np.asarray(stft_meanl).reshape([len(stft_meanl), 1])
# stft_medianl = np.asarray(stft_medianl).reshape([len(stft_medianl), 1])
# stft_maxl = np.asarray(stft_maxl).reshape([len(stft_maxl), 1])
#
# labell = np.ones([x_1.shape[0], 1]) if key in ['ST0-', 'ST1-',
# 'ST0+', 'ST1+'] else np.zeros([x_1.shape[0], 1])
# averaging by 5
# a = np.concatenate((x_1, x_2, x_3, qrs_il, qt_il, pq_il, jt_il, qrs_vl, t_vl, st20_vl, qrs_argmaxl,
# qrs_argminl, qrs_maxl, qrs_minl, qrs_max_minl, qrs_argmax_argminl, qrs_al,
# qrst_al, pqrst_al, qrs_qrst_ratiol, qrs_pqrst_ratiol, qrst_pqrst_ratiol,
# fft_f1l, fft_f2l, fft_f3l, stft_meanl, stft_medianl, stft_maxl, labell), axis=1)
# no averaging
"""no averaging"""
label = np.ones([pq_i.shape[0], 1]) if key in ['ST0-', 'ST1-',
'ST0+', 'ST1+'] else np.zeros([pq_i.shape[0], 1])
qrs_i = qrs_i.reshape([qrs_i.shape[0], 1])
qt_i = qt_i.reshape([qt_i.shape[0], 1])
pq_i = pq_i.reshape([pq_i.shape[0], 1])
jt_i = jt_i.reshape([jt_i.shape[0], 1])
qrs_v = qrs_v.reshape([qrs_v.shape[0], 1])
t_v = t_v.reshape([t_v.shape[0], 1])
st20_v = st20_v.reshape([st20_v.shape[0], 1])
qrs_max = qrs_max.reshape([qrs_max.shape[0], 1])
qrs_min = qrs_min.reshape([qrs_min.shape[0], 1])
qrs_max_min = qrs_max_min.reshape([qrs_max_min.shape[0], 1])
qrs_argmax_argmin = qrs_argmax_argmin.reshape([qrs_argmax_argmin.shape[0], 1])
qrs_qrst_ratio = qrs_qrst_ratio.reshape([qrs_qrst_ratio.shape[0], 1])
qrs_pqrst_ratio = qrs_pqrst_ratio.reshape([qrs_pqrst_ratio.shape[0], 1])
qrst_pqrst_ratio = qrst_pqrst_ratio.reshape([qrst_pqrst_ratio.shape[0], 1])
# b = np.concatenate((x_1, x_2, x_3, qrs_i, qt_i, pq_i, jt_i, qrs_v, t_v, st20_v, qrs_argmax,
# qrs_argmin, qrs_max, qrs_min, qrs_max_min, qrs_argmax_argmin, qrs_a,
# qrst_a, pqrst_a, qrs_qrst_ratio, qrs_pqrst_ratio, qrst_pqrst_ratio,
# fft_f1, fft_f2, fft_f3, stft_mean, stft_median, stft_max, label), axis=1)
# selected features
b = np.concatenate((x_2, x_3, pq_i, qrs_v, st20_v, qrs_argmin, qrs_min, qrs_max_min, qrst_a,
fft_f1, fft_f2, fft_f3, stft_mean, stft_median, stft_max, label), axis=1)
if key in ['ST0-', 'ST1-', 'ST0+', 'ST1+']:
c_st += b.shape[0]
else:
c_n += b.shape[0]
print(" Generated numpy array has shape:", b.shape)
train_dict[atr][key] = b.tolist()
# np.save('./trainingsets/{}_{}.npy'.format('e0606', key), b)
# X = b[:, :15]
# y = b[:, 15]
# clf = joblib.load('./clf_model/rf_clf_2018_11_08.model')
# mms = MinMaxScaler()
# X = mms.fit_transform(X)
# prediction = clf.predict(X)
# score = clf.score(X, y)
# for i in range(R_located.shape[0]):
# p = 'ST' if prediction[i] == 1 else 'N'
# if key in ['ST0-', 'ST1-', 'ST0+', 'ST1+']:
# plt.text(R_located[i], signal[R_located[i]], "{}_{}\nP_{}".format(i, 'ST', p))
# else:
# plt.text(R_located[i], signal[R_located[i]], "{}_{}\nP_{}".format(i, 'N', p))
# plt.title('QRSPT Delineation with accuracy {}'.format(score))
# plt.show()
except:
print("---Record {}, Type {} failed---".format(atr, key))
print("All finished! Total ST samples {} and normal samples {}".format(c_st, c_n))
with open('./trainingsets/edb_train_sets.json', 'w') as outfile:
json.dump(train_dict, outfile)
# delete the redundancy points and compensate the missing points for Mj3
# num2 = 1
# while num2 != 0:
# num2 = 0
# R = np.nonzero(countR)[0]
# R_R = R[1:] - R[:-1]
# R_R_mean = np.mean(R_R)
# for i in range(1, R.shape[0]):
# if R[i] - R[i - 1] <= 0.4 * R_R_mean:
# num2 += 1
# if signal[R[i]] > signal[R[i - 1]]:
# countR[R[i - 1]] = 0
# else:
# countR[R[i]] = 0
#
# num1 = 2
# while num1 > 0:
# k = 0
# num1 -= 1
# R = np.nonzero(countR)[0]
# R_R = R[1:] - R[:-1]
# R_R_mean = np.mean(R_R)
# for i in range(1, R.shape[0]):
# if R[i] - R[i - 1] > 1.6 * R_R_mean:
# Mj_adjust = w_peak[3, R[i - 1] + 80:R[i] - 80]
# points2 = (R[i] - 80) - (R[i - 1] + 80) + 1
#
# adjust_pos = Mj_adjust * (Mj_adjust > 0)
# adjust_pos = (adjust_pos > thpos_i / 4)
# adjust_neg = Mj_adjust * (Mj_adjust < 0)
# adjust_neg = -1 * (adjust_neg < thneg_i / 5)
# interval4 = adjust_pos + adjust_neg
# loca3 = np.nonzero(interval4)[0]
# diff2 = interval4[loca3[:-1]] - interval4[loca3[1:]]
#
# loca4 = np.where(diff2 == -2)[0]
# interval3 = np.zeros(points2)
# for j in range(loca4.shape[0]):
# interval3[loca3[loca4[j]]] = interval4[loca3[loca4[j]]]
# interval3[loca3[loca4[j] + 1]] = interval4[loca3[loca4[j] + 1]]
# mark4 = 0
# mark5 = 0
# mark6 = 0
#
# while k < points2 - 1:
# if interval3[k] == -1:
# mark4 = k
# k += 1
# while k < points2 - 1 and interval3[k] == 0:
# k += 1
# mark5 = k
# mark6 = int(round((abs(Mj_adjust[mark5]) * (mark4 + 1) + (mark5 + 1) * abs(Mj_adjust[mark4])) /
# (abs(Mj_adjust[mark5]) + abs(Mj_adjust[mark4]))))
# countR[R[i - 1] + 80 + mark6 - 10] = 1
# k += 60
# k += 1
# """find P and T peaks, P and T waves are more salient in Mj4"""
# Mj4_pos = Mj4 * (Mj4 > 0)
# Mj4_thpos = (max(Mj4_pos[0:round(points / 4)]) +
# max(Mj4_pos[round(points / 4):2 * round(points / 4)]) +
# max(Mj4_pos[2 * round(points / 4):3 * round(points / 4)]) +
# max(Mj4_pos[3 * round(points / 4):4 * round(points / 4)])) / 4
# Mj4_pos = 1 * (Mj4_pos > Mj4_thpos / 3)
# Mj4_neg = Mj4 * (Mj4 < 0)
# Mj4_thneg = (min(Mj4_neg[0:round(points / 4)]) +
# min(Mj4_neg[round(points / 4):2 * round(points / 4)]) +
# min(Mj4_neg[2 * round(points / 4):3 * round(points / 4)]) +
# min(Mj4_neg[3 * round(points / 4):4 * round(points / 4)])) / 4
# Mj4_neg = -1 * (Mj4_neg < Mj4_thneg / 4)
# Mj4_interval = Mj4_pos + Mj4_neg
# Mj4_loca = np.nonzero(Mj4_interval)[0]
# Mj4_interval2 = np.zeros(points)
# Mj4_diff = []
# for i in range(0, Mj4_loca.shape[0] - 1):
# if abs(Mj4_loca[i] - Mj4_loca[i + 1]) < 80:
# Mj4_diff.append(Mj4_interval[Mj4_loca[i]] - Mj4_interval[Mj4_loca[i + 1]])
# else:
# Mj4_diff.append(0)
# Mj4_diff = np.array(Mj4_diff)
# Mj4_loca2 = np.where(Mj4_diff == -2)[0]
# Mj4_interval2[Mj4_loca[Mj4_loca2]] = Mj4_interval[Mj4_loca[Mj4_loca2]]
# Mj4_interval2[Mj4_loca[Mj4_loca2 + 1]] = Mj4_interval[Mj4_loca[Mj4_loca2 + 1]]
#
# mark7 = 0
# mark8 = 0
# mark9 = 0
# Mj4_countR = np.zeros(1)
# Mj4_countQ = np.zeros(1)
# Mj4_countS = np.zeros(1)
# l = 0
# flag = 0
# while l < points - 1:
# if Mj4_interval2[l] == -1:
# mark7 = l
# l += 1
# while l < points - 1 and Mj4_interval2[l] == 0:
# l += 1
# mark8 = l
# mark9 = int(round((abs(Mj4[mark8]) * (mark7 + 1) + (mark8 + 1) * abs(Mj4[mark7])) /
# (abs(Mj4[mark8]) + abs(Mj4[mark7]))))
# Mj4_countR = np.concatenate((Mj4_countR, np.zeros(mark9 - 13 - Mj4_countR.shape[0]), np.ones(1)))
# # Mj4_countQ = np.concatenate((Mj4_countQ, np.zeros(mark7 - 12 - Mj4_countQ.shape[0]), -1 * np.ones(1)))
# # Mj4_countS = np.concatenate((Mj4_countS, np.zeros(mark8 - 12 - Mj4_countS.shape[0]), -1 * np.ones(1)))
# flag = 1
#
# kqs = mark9 - 13
# mark_q = 0
# while kqs > 0 and mark_q < 1:
# if Mj4[kqs] != 0:
# mark_q += 1
# kqs -= 1
# Mj4_countQ = np.concatenate((Mj4_countQ, np.zeros(kqs - Mj4_countQ.shape[0]), -1 * np.ones(1)))
#
# kqs = mark9 - 13
# mark_s = 0
# while kqs < points - 1 and mark_s < 1:
# if Mj4[kqs] != 0:
# mark_s += 1
# kqs += 1
# Mj4_countS = np.concatenate((Mj4_countS, np.zeros(kqs - Mj4_countS.shape[0]), -1 * np.ones(1)))
#
# if flag == 1:
# l += 100
# flag = 0
# else:
# l += 1
#
# # plt.figure(4)
# # plt.plot(Mj4_interval2)
# # plt.plot(Mj4_countR, 'r')
# # plt.plot(Mj4_countQ, 'k')
# # plt.plot(Mj4_countS, 'k')
# # plt.show()
#
# """delete the redundancy points and compensate the missing points for Mj4"""
# num4 = 1
# while num4 != 0:
# num4 = 0
# R = np.nonzero(Mj4_countR)[0]
# R_R = R[1:] - R[:-1]
# R_R_mean = np.mean(R_R)
# for i in range(1, R.shape[0]):
# if R[i] - R[i - 1] <= 0.4 * R_R_mean:
# num4 += 1
# if signal[R[i]] > signal[R[i - 1]]:
# Mj4_countR[R[i - 1]] = 0
# else:
# Mj4_countR[R[i]] = 0
#
# num3 = 2
# while num3 > 0:
# k = 0
# num3 -= 1
# R = np.nonzero(Mj4_countR)[0]
# R_R = R[1:] - R[:-1]
# R_R_mean = np.mean(R_R)
# for i in range(1, R.shape[0]):
# if R[i] - R[i - 1] > 1.6 * R_R_mean:
# Mj4_adjust = w_peak[4, R[i - 1] + 80:R[i] - 80]
# points2 = (R[i] - 80) - (R[i - 1] + 80) + 1
#
# adjust_pos = Mj4_adjust * (Mj4_adjust > 0)
# adjust_pos = (adjust_pos > Mj4_thpos / 4)
# adjust_neg = Mj4_adjust * (Mj4_adjust < 0)
# adjust_neg = -1 * (adjust_neg < Mj4_thneg / 5)
# Mj4_interval4 = adjust_pos + adjust_neg
# Mj4_loca3 = np.nonzero(Mj4_interval4)[0]
# Mj4_diff2 = Mj4_interval4[Mj4_loca3[:-1]] - Mj4_interval4[Mj4_loca3[1:]]
#
# Mj4_loca4 = np.where(Mj4_diff2 == -2)[0]
# Mj4_interval3 = np.zeros(points2)
# for j in range(Mj4_loca4.shape[0]):
# Mj4_interval3[Mj4_loca3[Mj4_loca4[j]]] = Mj4_interval4[Mj4_loca3[Mj4_loca4[j]]]
# Mj4_interval3[Mj4_loca3[Mj4_loca4[j] + 1]] = Mj4_interval4[Mj4_loca3[Mj4_loca4[j] + 1]]
# mark4 = 0
# mark5 = 0
# mark6 = 0
#
# while k < points2 - 1:
# if Mj4_interval3[k] == -1:
# mark4 = k
# k += 1
# while k < points2 - 1 and Mj4_interval3[k] == 0:
# k += 1
# mark5 = k
# mark6 = int(round((abs(Mj4_adjust[mark5]) * (mark4 + 1) + (mark5 + 1) * abs(Mj4_adjust[mark4])) /
# (abs(Mj4_adjust[mark5]) + abs(Mj4_adjust[mark4]))))
# Mj4_countR[R[i - 1] + 80 + mark6 - 10] = 1
# k += 60
# k += 1
#
# # plt.figure(5)
# # plt.plot(signal)
# # plt.plot(Mj4_countR, 'r')
# # plt.plot(Mj4_countQ, 'k')
# # plt.plot(Mj4_countS, 'k')
# # plt.show()
#
# R_located = np.nonzero(Mj4_countR)[0]
# Q_located = np.nonzero(Mj4_countQ)[0]
# S_located = np.nonzero(Mj4_countS)[0]
# Mj4_countP = np.zeros(1)
# Mj4_countT = np.zeros(1)
# countP = np.zeros(1)
# countP_begin = np.zeros(1)
# countP_end = np.zeros(1)
# countT = np.zeros(1)
# countT_begin = np.zeros(1)
# countT_end = np.zeros(1)
#
# """detect P wave"""
# window_size = 100
# mark10 = 0
# for i in range(1, R_located.shape[0]):
# flag = 0
# mark10 = 0
# R_R_interval = R_located[i] - R_located[i - 1]
# for j in range(0, int(R_R_interval * 2 / 3), 5):
# window_end = Q_located[i] - j
# window_begin = window_end - window_size
# if window_begin < R_located[i - 1] + R_R_interval / 3:
# break
# window_max = np.max(Mj4[window_begin:window_end])
# window_max_idx = np.argmax(Mj4[window_begin:window_end])
# window_min = np.min(Mj4[window_begin:window_end])
# window_min_idx = np.argmin(Mj4[window_begin:window_end])
# if window_min_idx < window_max_idx and (window_max_idx - window_min_idx) < window_size * 2 / 3 \
# and window_max > 0.01 and window_min < -0.1:
# flag = 1
# mark10 = int(round((window_max_idx + window_min_idx) / 2 + window_begin))
# Mj4_countP = np.concatenate((Mj4_countP, np.zeros(mark10 - Mj4_countP.shape[0]), np.ones(1)))
# countP = np.concatenate((countP, np.zeros(mark10 - countP.shape[0]), -1 * np.ones(1)))
# countP_begin = np.concatenate((countP_begin, np.zeros(window_begin + window_min_idx -
# countP_begin.shape[0]), -1 * np.ones(1)))
# countP_end = np.concatenate((countP_end, np.zeros(window_begin + window_max_idx - countP_end.shape[0]),
# -1 * np.ones(1)))
# if flag == 1:
# break
# if mark10 == 0 and flag == 0:
# mark10 = int(round(R_located[i] - R_R_interval / 3))
# countP = np.concatenate((countP, np.zeros(mark10 - countP.shape[0]), -1 * np.ones(1)))
#
# """detect T wave"""
# window_size_Q = 90
# mark11 = 0
# for i in range(0, R_located.shape[0] - 1):
# flag = 0
# mark11 = 0
# R_R_interval = R_located[i + 1] - R_located[i]
# for j in range(0, int(R_R_interval * 2 / 3), 5):
# window_begin = S_located[i] + j
# window_end = window_begin + window_size_Q
# if window_end > R_located[i + 1] - R_R_interval / 4:
# break
# window_max = np.max(Mj4[window_begin:window_end])
# window_max_idx = np.argmax(Mj4[window_begin:window_end])
# window_min = np.min(Mj4[window_begin:window_end])
# window_min_idx = np.argmin(Mj4[window_begin:window_end])
# if window_min_idx < window_max_idx and (window_max_idx - window_min_idx) < window_size_Q \
# and window_max > 0.1 and window_min < -0.1:
# flag = 1
# mark11 = int(round((window_max_idx + window_min_idx) / 2 + window_begin))
# Mj4_countT = np.concatenate((Mj4_countT, np.zeros(mark11 - Mj4_countT.shape[0]), np.ones(1)))
# countT = np.concatenate((countT, np.zeros(mark11 - countT.shape[0]), -1 * np.ones(1)))
# countT_begin = np.concatenate((countT_begin, np.zeros(window_begin + window_min_idx -
# countT_begin.shape[0]), -1 * np.ones(1)))
# countT_end = np.concatenate((countT_end, np.zeros(window_begin + window_max_idx - countT_end.shape[0]),
# -1 * np.ones(1)))
# if flag == 1:
# break
# if mark11 == 0 and flag == 0:
# mark11 = int(round(R_located[i] - R_R_interval / 3))
# countT = np.concatenate((countT, np.zeros(mark11 - countT.shape[0]), -2 * np.ones(1)))
#
# # plt.figure(3)
# # plt.plot(signal)
# # plt.plot(countR, 'r')
# # plt.plot(countQ, 'k')
# # plt.plot(countS, 'g')
# # # plt.plot(countP, 'r')
# # plt.plot(countT, 'r')
# # for i in range(Rnum):
# # if R_results[i] == 0:
# # break
# # plt.plot(R_results[i], signal[R_results[i]], marker='o', color='g')
# # plt.show()