def heart_beats_features2(thb):
means = np.array([col.mean() for col in thb.T])
stds = np.array([col.std() for col in thb.T])
PQ = means[:int(0.15 * loader.FREQUENCY)]
ST = means[int(0.25 * loader.FREQUENCY):]
r_pos = int(0.2 * loader.FREQUENCY)
a = np.zeros(15)
p_pos = np.argmax(PQ)
t_pos = np.argmax(ST)
a[0] = r_pos - p_pos
a[1] = PQ[p_pos]
a[2] = PQ[p_pos] / means[r_pos]
a[3] = t_pos
a[4] = ST[t_pos]
a[5] = ST[t_pos] / means[r_pos]
a[6] = PQ[p_pos] / ST[t_pos]
a[7] = skew(PQ)
a[8] = kurtosis(PQ)
a[9] = skew(ST)
a[10] = kurtosis(ST)
a[11] = calcActivity(means)
a[12] = calcMobility(means)
a[13] = calcComplexity(means)
a[14] = np.mean(stds)
return a
def heart_beats_features2(thb, rpeaks):
if len(thb) == 0:
thb = np.zeros((1, int(0.6 * loader.FREQUENCY)), dtype=np.int32)
diff_rpeak = np.median(np.diff(rpeaks))
means = heartbeats.median_heartbeat(thb)
r_pos = int(0.2 * loader.FREQUENCY)
if diff_rpeak < 350:
tmp1 = 100 - int(diff_rpeak / 3)
tmp2 = 100 + int(2 * diff_rpeak / 3)
for i in range(len(means)):
if i < tmp1:
means[i] = 0
elif i > tmp2:
means[i] = 0
freq_cof = diff_rpeak / 350 * loader.FREQUENCY
else:
freq_cof = loader.FREQUENCY
a = dict()
r_list = []
p_list = []
q_list = []
s_list = []
t_list = []
for i in range(np.shape(thb)[0]):
r_pos = int(0.2 * loader.FREQUENCY)
template = thb[i, :]
PQ = template[r_pos - int(0.2 * freq_cof):r_pos - int(0.05 * freq_cof)]
ST = template[r_pos + int(0.05 * freq_cof):r_pos + int(0.4 * freq_cof)]
QR = template[r_pos - int(0.07 * freq_cof):r_pos]
RS = template[r_pos:r_pos + int(0.07 * freq_cof)]
q_list.append(np.min(QR))
s_list.append(np.min(RS))
p_list.append(np.max(PQ))
t_list.append(np.max(ST))
r_list.append(template[r_pos])
a['T_P_max'] = np.max(p_list)
a['T_P_min'] = np.min(p_list)
a['T_P_std'] = np.std(p_list)
a['T_Q_max'] = np.max(q_list)
a['T_Q_min'] = np.min(q_list)
a['T_Q_std'] = np.std(q_list)
a['T_R_max'] = np.max(r_list)
a['T_R_min'] = np.min(r_list)
a['T_R_std'] = np.std(r_list)
a['T_S_max'] = np.max(s_list)
a['T_S_min'] = np.min(s_list)
a['T_S_std'] = np.std(s_list)
a['T_T_max'] = np.max(t_list)
a['T_T_min'] = np.min(t_list)
a['T_T_std'] = np.std(t_list)
# PQ = means[:int(0.15 * loader.FREQUENCY)]
PQ = means[r_pos - int(0.2 * freq_cof):r_pos - int(0.05 * freq_cof)]
#ST = means[int(0.25 * loader.FREQUENCY):]
#ST = means[r_pos + int(0.05 * freq_cof):r_pos + int(0.4 * freq_cof)]
#QR = means[int(0.13 * loader.FREQUENCY):r_pos]
QR = means[r_pos - int(0.07 * freq_cof):r_pos]
RS = means[r_pos:r_pos + int(0.07 * freq_cof)]
#q_pos = int(0.13 * loader.FREQUENCY) + np.argmin(QR)
q_pos = r_pos - len(QR) + np.argmin(QR)
s_pos = r_pos + np.argmin(RS)
ST = means[s_pos + int(0.08 * freq_cof):r_pos + int(0.35 * freq_cof)]
p_pos = np.argmax(PQ)
t_pos = np.argmax(ST)
if t_pos / len(ST) < 0.2 or t_pos / len(ST) > 0.8:
t_pos = np.argmin(ST)
if t_pos / len(ST) < 0.2 or t_pos / len(ST) > 0.8:
t_pos = int(0.12 * freq_cof)
t_pos = t_pos + s_pos + int(0.08 * freq_cof)
t_begin = max(s_pos + int(0.08 * freq_cof), t_pos - int(0.12 * freq_cof))
st_begin = max(s_pos + int(0.035 * freq_cof),
t_begin - int(0.06 * freq_cof))
#t_begin = int(0.035 * freq_cof) + s_pos
#t_end = int(0.24 * freq_cof) + t_begin
t_end = min(len(means) - 1, int(t_pos + int(0.12 * freq_cof)))
#t_wave = ST[max(0, t_pos - int(0.08 * freq_cof)):min(len(ST), t_pos + int(0.08 * freq_cof))]
t_wave = means[t_begin:t_end]
p_wave = PQ[max(0, p_pos -
int(0.06 * freq_cof)):min(len(PQ), p_pos +
int(0.06 * freq_cof))]
st_wave = means[st_begin:t_begin]
p_pos = p_pos + r_pos - int(0.2 * freq_cof)
QRS = means[q_pos:s_pos]
#
a['PR_interval'] = (r_pos - p_pos) / 500
a['PQ_interval'] = (p_pos - q_pos) / 500
a['RT_interval'] = (t_pos - r_pos) / 500
a['PT_interval'] = (t_pos - p_pos) / 500
a['ST_interval'] = (t_pos - s_pos) / 500
a['T_slope1'] = (means[t_pos] - means[t_begin]) / len(t_wave)
a['T_slope2'] = (means[t_pos] - means[t_end]) / len(t_wave)
a['ST_slope'] = (means[t_begin] - means[st_begin]) / len(st_wave)
a['RS_interval'] = (r_pos - q_pos) / 500
a['QR_interval'] = (q_pos - r_pos) / 500
a['R_time'] = len(p_wave) / 500
a['T_time'] = len(t_wave) / 500
a['t_activity'] = calcActivity(t_wave)
a['t_mobility'] = calcMobility(t_wave)
a['t_complexity'] = calcComplexity(t_wave)
a['p_activity'] = calcActivity(p_wave)
a['p_mobility'] = calcMobility(p_wave)
a['p_complexity'] = calcComplexity(p_wave)
#
a['P_max'] = means[p_pos]
a['P_min'] = min(p_wave)
a['P_to_R'] = means[p_pos] / means[r_pos]
a['P_to_Q'] = means[p_pos] - means[q_pos]
a['T_am'] = means[t_pos]
a['T_max'] = max(t_wave)
a['T_min'] = min(t_wave)
a['T_to_R'] = means[t_pos] / means[r_pos]
a['T_to_S'] = means[t_pos] / means[s_pos]
a['P_to_T'] = means[p_pos] / means[t_pos]
a['P_skew'] = skew(p_wave)
a['P_kurt'] = kurtosis(p_wave)
a['T_skew'] = skew(t_wave)
a['T_kurt'] = kurtosis(t_wave)
a['activity'] = calcActivity(means)
a['mobility'] = calcMobility(means)
a['complexity'] = calcComplexity(means)
qrs_min = abs(min(QRS))
qrs_max = abs(max(QRS))
qrs_abs = max(qrs_min, qrs_max)
sign = -1 if qrs_min > qrs_max else 1
a['QRS_diff'] = sign * abs(qrs_min / qrs_abs)
a['QS_diff'] = abs(means[s_pos] - means[q_pos])
a['QRS_kurt'] = kurtosis(QRS)
a['QRS_skew'] = skew(QRS)
a['QRS_minmax'] = qrs_max - qrs_min
a['T_wave_time'] = len(t_wave) / 500
a['P_wave_time'] = len(p_wave) / 500
a['Temp_Median_p_5'] = np.percentile(means, 5)
a['Temp_Median_p_25'] = np.percentile(means, 25)
a['Temp_Median_p_75'] = np.percentile(means, 75)
a['Temp_Median_p_95'] = np.percentile(means, 95)
a['T_Median_p_5'] = np.percentile(t_wave, 5)
a['T_Median_p_25'] = np.percentile(t_wave, 25)
a['T_Median_p_75'] = np.percentile(t_wave, 75)
a['T_Median_p_95'] = np.percentile(t_wave, 95)
a.update(fft_features(means))
return a
def heart_beats_features2(thb):
means = heartbeats.median_heartbeat(thb)
stds = np.array([np.std(col) for col in thb.T])
r_pos = int(0.2 * loader.FREQUENCY)
PQ = means[:int(0.15 * loader.FREQUENCY)]
ST = means[int(0.25 * loader.FREQUENCY):]
QR = means[int(0.13 * loader.FREQUENCY):r_pos]
RS = means[r_pos:int(0.27 * loader.FREQUENCY)]
q_pos = int(0.13 * loader.FREQUENCY) + np.argmin(QR)
s_pos = r_pos + np.argmin(RS)
p_pos = np.argmax(PQ)
t_pos = np.argmax(ST)
t_wave = ST[max(0, t_pos - int(0.08 * loader.FREQUENCY)
):min(len(ST), t_pos + int(0.08 * loader.FREQUENCY))]
p_wave = PQ[max(0, p_pos - int(0.06 * loader.FREQUENCY)
):min(len(PQ), p_pos + int(0.06 * loader.FREQUENCY))]
r_plus = sum(1 if b[r_pos] > 0 else 0 for b in thb)
r_minus = len(thb) - r_plus
QRS = means[q_pos:s_pos]
a = dict()
a['PR_interval'] = r_pos - p_pos
a['P_max'] = PQ[p_pos]
a['P_to_R'] = PQ[p_pos] / means[r_pos]
a['P_to_Q'] = PQ[p_pos] - means[q_pos]
a['ST_interval'] = t_pos
a['T_max'] = ST[t_pos]
a['R_plus'] = r_plus / max(1, len(thb))
a['R_minus'] = r_minus / max(1, len(thb))
a['T_to_R'] = ST[t_pos] / means[r_pos]
a['T_to_S'] = ST[t_pos] - means[s_pos]
a['P_to_T'] = PQ[p_pos] / ST[t_pos]
a['P_skew'] = skew(p_wave)
a['P_kurt'] = kurtosis(p_wave)
a['T_skew'] = skew(t_wave)
a['T_kurt'] = kurtosis(t_wave)
a['activity'] = calcActivity(means)
a['mobility'] = calcMobility(means)
a['complexity'] = calcComplexity(means)
a['QRS_len'] = s_pos - q_pos
qrs_min = abs(min(QRS))
qrs_max = abs(max(QRS))
qrs_abs = max(qrs_min, qrs_max)
sign = -1 if qrs_min > qrs_max else 1
a['QRS_diff'] = sign * abs(qrs_min / qrs_abs)
a['QS_diff'] = abs(means[s_pos] - means[q_pos])
a['QRS_kurt'] = kurtosis(QRS)
a['QRS_skew'] = skew(QRS)
a['QRS_minmax'] = qrs_max - qrs_min
a['P_std'] = np.mean(stds[:q_pos])
a['T_std'] = np.mean(stds[s_pos:])
return a
def heart_beats_features(thb, diff_rpeak):
means = heartbeats.median_heartbeat(thb)
r_pos = int(0.2 * loader.FREQUENCY)
if diff_rpeak < 350:
tmp1 = 100 - int(diff_rpeak / 3)
tmp2 = 100 + int(2 * diff_rpeak / 3)
for i in range(len(means)):
if i < tmp1:
means[i] = 0
elif i > tmp2:
means[i] = 0
freq_cof = diff_rpeak / 350 * loader.FREQUENCY
else:
freq_cof = loader.FREQUENCY
# PQ = means[:int(0.15 * loader.FREQUENCY)]
PQ = means[r_pos - int(0.2 * freq_cof):r_pos - int(0.05 * freq_cof)]
#ST = means[int(0.25 * loader.FREQUENCY):]
#ST = means[r_pos + int(0.05 * freq_cof):r_pos + int(0.4 * freq_cof)]
#QR = means[int(0.13 * loader.FREQUENCY):r_pos]
QR = means[r_pos - int(0.07 * freq_cof):r_pos]
RS = means[r_pos:r_pos + int(0.07 * freq_cof)]
#q_pos = int(0.13 * loader.FREQUENCY) + np.argmin(QR)
q_pos = r_pos - len(QR) + np.argmin(QR)
s_pos = r_pos + np.argmin(RS)
ST = means[s_pos + int(0.08 * freq_cof):r_pos + int(0.35 * freq_cof)]
p_pos = np.argmax(PQ)
t_pos = np.argmax(ST)
if t_pos / len(ST) < 0.2 or t_pos / len(ST) > 0.8:
t_pos = np.argmin(ST)
if t_pos / len(ST) < 0.2 or t_pos / len(ST) > 0.8:
t_pos = 0.12 * freq_cof
t_pos = t_pos + s_pos + int(0.08 * freq_cof)
t_begin = max(s_pos + int(0.08 * freq_cof), t_pos - int(0.12 * freq_cof))
st_begin = max(s_pos + int(0.035 * freq_cof),
t_begin - int(0.06 * freq_cof))
#t_begin = int(0.035 * freq_cof) + s_pos
#t_end = int(0.24 * freq_cof) + t_begin
t_end = min(len(means), int(t_pos + int(0.12 * freq_cof)))
#t_wave = ST[max(0, t_pos - int(0.08 * freq_cof)):min(len(ST), t_pos + int(0.08 * freq_cof))]
t_wave = means[t_begin:t_end]
p_wave = PQ[max(0, p_pos -
int(0.06 * freq_cof)):min(len(PQ), p_pos +
int(0.06 * freq_cof))]
st_wave = means[st_begin:t_begin]
p_pos = p_pos + r_pos - int(0.2 * freq_cof)
#r_plus = sum(1 if b[r_pos] > 0 else 0 for b in thb)
#r_minus = len(thb) - r_plus
QRS = means[q_pos:s_pos]
import matplotlib.pyplot as plt
plt.figure()
plt.plot(means)
plt.axvline(p_pos, color='red')
plt.axvline(q_pos, color='green')
plt.axvline(r_pos, color='blue')
plt.axvline(s_pos, color='black')
plt.axvline(t_pos, color='yellow')
plt.axvline(t_begin, color='orange')
plt.axvline(t_end, color='purple')
plt.axvline(st_begin, color='gray')
#plt.axvline(t_min_pos, color='greenyellow')
plt.show()
a = dict()
a['PR_interval'] = r_pos - p_pos
a['P_max'] = means[p_pos]
a['P_to_R'] = means[p_pos] / means[r_pos]
a['P_to_Q'] = means[p_pos] - means[q_pos]
a['ST_interval'] = t_pos
a['T_max'] = means[t_pos]
#a['R_plus'] = r_plus / max(1, len(thb))
#a['R_minus'] = r_minus / max(1, len(thb))
a['T_to_R'] = means[t_pos] / means[r_pos]
a['T_to_S'] = means[t_pos] - means[s_pos]
a['P_to_T'] = means[p_pos] / means[t_pos]
a['P_skew'] = skew(p_wave)
a['P_kurt'] = kurtosis(p_wave)
a['T_skew'] = skew(t_wave)
a['T_kurt'] = kurtosis(t_wave)
a['activity'] = calcActivity(means)
a['mobility'] = calcMobility(means)
a['complexity'] = calcComplexity(means)
a['QRS_len'] = s_pos - q_pos
qrs_min = abs(min(QRS))
qrs_max = abs(max(QRS))
qrs_abs = max(qrs_min, qrs_max)
sign = -1 if qrs_min > qrs_max else 1
a['QRS_diff'] = sign * abs(qrs_min / qrs_abs)
a['QS_diff'] = abs(means[s_pos] - means[q_pos])
a['QRS_kurt'] = kurtosis(QRS)
a['QRS_skew'] = skew(QRS)
a['QRS_minmax'] = qrs_max - qrs_min
#a['P_std'] = np.mean(stds[:q_pos])
#a['T_std'] = np.mean(stds[s_pos:])
a['Temp_Median_p_5'] = np.percentile(means, 5)
a['Temp_Median_p_25'] = np.percentile(means, 25)
a['Temp_Median_p_75'] = np.percentile(means, 75)
a['Temp_Median_p_95'] = np.percentile(means, 95)
return a
def heart_beats_features2(thb, rpeaks):
if len(thb) == 0:
thb = np.zeros((1, int(0.6 * loader.FREQUENCY)), dtype=np.int32)
means = heartbeats.median_heartbeat(thb)
diff_rpeak = np.median(np.diff(rpeaks))
freq_cof = loader.FREQUENCY
if diff_rpeak < 280:
tmp1 = 100 - int(diff_rpeak / 3)
tmp2 = 100 + int(2 * diff_rpeak / 3)
for i in range(len(means)):
if i < tmp1:
means[i] = 0
elif i > tmp2:
means[i] = 0
freq_cof = diff_rpeak / 300 * loader.FREQUENCY
a = dict()
r_list = []
p_list = []
q_list = []
s_list = []
t_list = []
for i in range(np.shape(thb)[0]):
r_pos = int(0.2 * loader.FREQUENCY)
template = thb[i, :]
PQ = template[r_pos - int(0.2 * freq_cof):r_pos - int(0.05 * freq_cof)]
ST = template[r_pos + int(0.05 * freq_cof):r_pos + int(0.4 * freq_cof)]
QR = template[r_pos - int(0.07 * freq_cof):r_pos]
RS = template[r_pos:r_pos + int(0.07 * freq_cof)]
q_list.append(np.min(QR))
s_list.append(np.min(RS))
p_list.append(np.max(PQ))
t_list.append(np.max(ST))
r_list.append(template[r_pos])
a['T_P_max'] = np.max(p_list)
a['T_P_min'] = np.min(p_list)
a['T_P_std'] = np.std(p_list)
a['T_Q_max'] = np.max(q_list)
a['T_Q_min'] = np.min(q_list)
a['T_Q_std'] = np.std(q_list)
a['T_R_max'] = np.max(r_list)
a['T_R_min'] = np.min(r_list)
a['T_R_std'] = np.std(r_list)
a['T_S_max'] = np.max(s_list)
a['T_S_min'] = np.min(s_list)
a['T_S_std'] = np.std(s_list)
a['T_T_max'] = np.max(t_list)
a['T_T_min'] = np.min(t_list)
a['T_T_std'] = np.std(t_list)
stds = np.array([np.std(col) for col in thb.T])
r_pos = int(0.2 * loader.FREQUENCY)
PQ = means[r_pos - int(0.2 * freq_cof):r_pos - int(0.05 * freq_cof)]
#ST = means[int(0.25 * loader.FREQUENCY):]
ST = means[r_pos + int(0.05 * freq_cof):r_pos + int(0.4 * freq_cof)]
#QR = means[int(0.13 * loader.FREQUENCY):r_pos]
QR = means[r_pos - int(0.07 * freq_cof):r_pos]
RS = means[r_pos:r_pos + int(0.07 * freq_cof)]
#q_pos = int(0.13 * loader.FREQUENCY) + np.argmin(QR)
q_pos = r_pos - len(QR) + np.argmin(QR)
s_pos = r_pos + np.argmin(RS)
p_pos = np.argmax(PQ)
t_pos = np.argmax(ST)
t_wave = ST[max(0, t_pos -
int(0.08 * freq_cof)):min(len(ST), t_pos +
int(0.08 * freq_cof))]
p_wave = PQ[max(0, p_pos -
int(0.06 * freq_cof)):min(len(PQ), p_pos +
int(0.06 * freq_cof))]
#r_plus = sum(1 if b[r_pos] > 0 else 0 for b in thb)
#r_minus = len(thb) - r_plus
QRS = means[q_pos:s_pos]
a['PR_interval'] = r_pos - p_pos
a['P_max'] = PQ[p_pos]
a['P_min'] = min(PQ)
a['P_to_R'] = PQ[p_pos] / means[r_pos]
a['P_to_Q'] = PQ[p_pos] - means[q_pos]
a['ST_interval'] = t_pos
a['T_max'] = ST[t_pos]
a['T_min'] = min(ST)
a['T_to_R'] = ST[t_pos] / means[r_pos]
a['T_to_S'] = ST[t_pos] - means[s_pos]
a['P_to_T'] = PQ[p_pos] / ST[t_pos]
a['P_skew'] = skew(p_wave)
a['P_kurt'] = kurtosis(p_wave)
a['T_skew'] = skew(t_wave)
a['T_kurt'] = kurtosis(t_wave)
a['activity'] = calcActivity(means)
a['mobility'] = calcMobility(means)
a['complexity'] = calcComplexity(means)
a['QRS_len'] = s_pos - q_pos
qrs_min = abs(min(QRS))
qrs_max = abs(max(QRS))
qrs_abs = max(qrs_min, qrs_max)
sign = -1 if qrs_min > qrs_max else 1
a['QRS_diff'] = sign * abs(qrs_min / qrs_abs)
a['QS_diff'] = abs(means[s_pos] - means[q_pos])
a['QRS_kurt'] = kurtosis(QRS)
a['QRS_skew'] = skew(QRS)
a['QRS_minmax'] = qrs_max - qrs_min
a['P_std'] = np.mean(stds[:q_pos])
a['T_std'] = np.mean(stds[s_pos:])
a['T_wave_time'] = len(t_wave) / 500
a['P_wave_time'] = len(p_wave) / 500
a['Temp_Median_p_5'] = np.percentile(means, 5)
a['Temp_Median_p_25'] = np.percentile(means, 25)
a['Temp_Median_p_75'] = np.percentile(means, 75)
a['Temp_Median_p_95'] = np.percentile(means, 95)
a.update(fft_features(means))
return a