def plot_with_detected_peaks(x, clazz):
total = 7
x = ecg.ecg(x, sampling_rate=300, show=False)['templates']
m = [np.median(col) for col in x.T]
dists = [np.sum(np.square(s-m)) for s in x]
pmin = np.argmin(dists)
x = x[pmin]
p = x[:45]
qrs = x[45:80]
t = x[80:]
plt.subplot(total, 1, 1)
plt.ylabel(clazz)
plt.plot(x)
plt.subplot(total, 1, 2)
plt.ylabel(clazz)
plt.plot(p)
plt.subplot(total, 1, 3)
plt.ylabel("P DFT")
ff = fft(p)[:len(p) // 2]
plt.plot(ff, 'r')
plt.subplot(total, 1, 4)
plt.ylabel(clazz)
plt.plot(qrs)
plt.subplot(total, 1, 5)
plt.ylabel("QRS DFT")
ff = fft(qrs)[:len(qrs) // 2]
plt.plot(ff, 'r')
plt.subplot(total, 1, 6)
plt.ylabel(clazz)
plt.plot(t)
plt.subplot(total, 1, 7)
plt.ylabel("T DFT")
ff = fft(t)[:len(t) // 2]
plt.plot(ff, 'r')
plt.show()
def plot_wavelet(row, clazz):
res = ecg(row, sampling_rate=300, show=False)
# x = preprocessing.normalize_ecg(row)
# x = low_pass_filtering(x)
# x = high_pass_filtering(x)
x = res['templates'][0]
print(pywt.wavelist('db'))
a, d1, d2, d3, d4 = wavedec(x, 'db4', level=4)
print(clazz)
print('Mean', np.mean(d1))
print('Std', np.std(d1))
print('Mean', np.mean(d2))
print('Std', np.std(d2))
total = 6
plt.subplot(total, 1, 1)
plt.plot(range(len(x)), x, 'g-')
plt.ylabel("ECG:" + clazz)
plt.subplot(total, 1, 2)
plt.plot(range(len(a)), a, 'g-')
plt.ylabel("Wavelet")
plt.subplot(total, 1, 3)
plt.plot(range(len(d1)), d1, 'g-')
plt.ylabel("D1")
plt.subplot(total, 1, 4)
plt.plot(range(len(d2)), d2, 'g-')
plt.ylabel("D2")
plt.subplot(total, 1, 5)
plt.plot(range(len(d3)), d3, 'g-')
plt.ylabel("D3")
plt.subplot(total, 1, 6)
plt.plot(range(len(d4)), d4, 'g-')
plt.ylabel("D4")
plt.show()
def get_features_dict(x):
"""
ts : array
Signal time axis reference (seconds).
filtered : array
Filtered ECG signal.
rpeaks : array
R-peak location indices.
templates_ts : array
Templates time axis reference (seconds).
templates : array
Extracted heartbeat templates.
heart_rate_ts : array
Heart rate time axis reference (seconds).
heart_rate : array
Instantaneous heart rate (bpm).
:param x:
:return:
"""
[ts, fts, rpeaks, tts, thb, hrts,
hr] = ecg.ecg(signal=x, sampling_rate=loader.FREQUENCY, show=False)
"""
Returns:
ts (array) – Signal time axis reference (seconds).
filtered (array) – Filtered ECG signal.
rpeaks (array) – R-peak location indices.
templates_ts (array) – Templates time axis reference (seconds).
templates (array) – Extracted heartbeat templates.
heart_rate_ts (array) – Heart rate time axis reference (seconds).
heart_rate (array) – Instantaneous heart rate (bpm).
"""
fx = dict()
fx.update(heart_rate_features(hr))
fx.update(frequency_powers(x, n_power_features=60))
fx.update(add_suffix(frequency_powers(fts), "fil"))
fx.update(frequency_powers_summary(fts))
fx.update(heart_beats_features2(thb))
fx.update(fft_features(heartbeats.median_heartbeat(thb)))
# fx.update(heart_beats_features3(thb))
fx.update(r_features(fts, rpeaks))
fx['PRbyST'] = fx['PR_interval'] * fx['ST_interval']
fx['P_form'] = fx['P_kurt'] * fx['P_skew']
fx['T_form'] = fx['T_kurt'] * fx['T_skew']
"""
from features.template_statistics import TemplateStatistics
# Get features
template_statistics = TemplateStatistics(
ts=ts,
signal_raw=x,
signal_filtered=fts,
rpeaks=rpeaks,
templates_ts=tts,
templates=np.array(thb).T,
fs=loader.FREQUENCY,
template_before=0.25,
template_after=0.4
)
template_statistics.calculate_template_statistics()
# Update feature dictionary
fx.update(template_statistics.get_template_statistics())
"""
for key, value in fx.items():
if np.math.isnan(value):
value = 0
fx[key] = value
return fx
for item in misclassified:
print(item[2], 'is of class', item[0], 'but was classified as',
item[1])
print(classification_report(ytrue, ypred))
matrix = confusion_matrix(ytrue, ypred)
print(matrix)
for row in matrix:
amax = sum(row)
if amax > 0:
for i in range(len(row)):
row[i] = row[i] * 100.0 / amax
print(matrix)
print("Plotting misclassified")
base_dir = "../outputs/misclassified"
mkdir(base_dir)
misclassified = sorted(misclassified, key=lambda t: t[2])
for item in misclassified:
print("Saving to " + item[2])
row = loader.load_data_from_file(item[2])
[ts, fts, rpeaks, tts, thb, hrts,
hr] = ecg.ecg(signal=row, sampling_rate=loader.FREQUENCY, show=False)
__draw_to_file(
base_dir + "/" + item[2] + "_" + item[0] + "_" + item[1] + ".png",
tts, thb)