def peaks_hr(sig, peak_inds, fs, title, figsize=(20,10), saveto=None):
#这个函数是用来画出信号峰值和心律
#计算心律
hrs=processing.compute_hr(sig_len=sig.shape[0], qrs_inds=peak_inds, fs=fs)
N=sig.shape[0]
fig, ax_left=plt.subplots(figsize=figsize)
ax_right=ax_left.twinx()
ax_left.plot(sig, color='#3979f0', label='Signal')
ax_left.plot(peak_inds, sig[peak_inds], 'rx', marker='x', color='#8b0000', label='Peak', markersize=12)#画出标记
ax_right.plot(np.arange(N), hrs, label='Heart rate', color='m', linewidth=2)#画出心律，y轴在右边
ax_left.set_title(title)
ax_left.set_xlabel('Time (ms)')
ax_left.set_ylabel('ECG (mV)', color='#3979f0')
ax_right.set_ylabel('Heart rate (bpm)', color='m')
#设置颜色使得和线条颜色一致
ax_left.tick_params('y', colors='#3979f0')
ax_right.tick_params('y', colors='m')
if saveto is not None:
plt.savefig(saveto, dpi=600)
plt.show()
def filter_ecg(ecg, fs, get_heart_rate=False, cutoff_frequencies=[0.5, 100.0]):
nyq_f = fs / 2
w_low = cutoff_frequencies[0] / (math.pi * nyq_f)
w_high = cutoff_frequencies[1] / (math.pi * nyq_f)
filt_order = 4
b, a = scipy.signal.butter(filt_order, [w_low, w_high], btype='bandpass')
processed_ecg = scipy.signal.filtfilt(b, a, ecg)
if get_heart_rate:
qrs_inds = processing.gqrs_detect(sig=ecg, fs=fs)
hr = processing.compute_hr(sig_len=len(ecg), qrs_inds=qrs_inds, fs=fs)
return (processed_ecg, hr)
else:
return (processed_ecg)
def calculateHeartRate(sample_to, xqrs, fs):
'''
Calculates the heart rate of signal uptil the point specified
Args:
sample_to (int): end sample of signal
xqrs (signal): used for qrs detection
fs (int): frequency of signal
Returns:
heart_rate (int): heart beat of person
'''
heart_rate_list = processing.compute_hr(sample_to, xqrs.qrs_inds, fs)
heart_rate = heart_rate_list[-1]
return heart_rate
def peaks_hr(sig, peak_inds, fs, title, figsize=(20, 10), saveto=None):
"Plot a signal with its peaks and heart rate"
# Calculate heart rate
hrs = processing.compute_hr(sig_len=sig.shape[0],
qrs_inds=peak_inds,
fs=fs)
N = sig.shape[0]
fig, ax_left = plt.subplots(figsize=figsize)
ax_right = ax_left.twinx()
ax_left.plot(sig, color='#3979f0', label='Signal')
ax_left.plot(peak_inds,
sig[peak_inds],
'rx',
marker='x',
color='#8b0000',
label='Peak',
markersize=12)
ax_right.plot(np.arange(N),
hrs,
label='Heart rate',
color='m',
linewidth=2)
ax_left.set_title(title)
ax_left.set_xlabel('Time (ms)')
ax_left.set_ylabel('ECG (mV)', color='#3979f0')
ax_right.set_ylabel('Heart rate (bpm)', color='m')
# Make the y-axis label, ticks and tick labels match the line color.
ax_left.tick_params('y', colors='#3979f0')
ax_right.tick_params('y', colors='m')
if saveto is not None:
plt.savefig(saveto, dpi=600)
plt.show()
import imutils
import time
import dlib
import cv2
# 1. Read signal
record = wfdb.rdrecord('mit-bih-arrhythmia-database-1.0.0/108', channels=[0])
# 2. Detect QRS locations
qrs_locs = processing.gqrs_detect(record.p_signal[:, 0], fs=record.fs)
# 5. Length of signal
len = record.sig_len
# 6. Compute heart rate
heart_rate = processing.compute_hr(len, qrs_locs, fs=record.fs)
# 7. Remove Nan values from array
heart_rate = heart_rate[~np.isnan(heart_rate)]
# 8. Calculate RR from heart rate
# RR-interval is shorter and wider for awake subject than drowsy subject.
RR = 60 / heart_rate
# From hrv analysis get frequency features
dict = hrvanalysis.extract_features.get_frequency_domain_features(RR)
itr = 0
length = RR.size - 1000
def ExtractFeatures(dataset, size, minThreshold):
# Features
qAmplitudes = []
rAmplitudes = []
qrsDurations = []
rrIntervals = []
heartRatesDuringHeartBeat = []
minSize = sys.maxsize
signals = dataset['signal']
labels = dataset['label']
for i, sig in enumerate(signals[:size]):
print(str(i) + "/" + str(len(signals)) + " ...")
qrsInds = processing.gqrs_detect(sig=sig.astype('float64'), fs=300)
heartRates = processing.compute_hr(sig_len=sig.shape[0],
fs=300,
qrs_inds=sorted(qrsInds))
rPoints, sPoints, qPoints = QRS_util.ECG_QRS_detect(
sig, 300, True, False)
lenHb = sys.maxsize
lenSigQrs = sys.maxsize
lenHr = sys.maxsize
lenQ = sys.maxsize
lenR = sys.maxsize
if (len(heartRates) > 0 and len(rPoints) > 0):
# Adding features for each signal.
heartRatesDuringHeartBeat.append(np.array(heartRates[rPoints]))
lenHr = len(heartRates[rPoints])
if (len(qPoints) > 0):
qAmplitudes.append(np.array(sig[qPoints]))
lenQ = len(sig[qPoints])
if (len(rPoints) > 0):
rAmplitudes.append(np.array(sig[rPoints]))
lenR = len(sig[rPoints])
if (len(qPoints) > 0 and len(rPoints) > 0):
sigQrsDuration = [
sPoints[i] - qPoints[i] for i in range(len(qPoints) - 1)
]
qrsDurations.append(np.array(sigQrsDuration))
lenSigQrs = len(sigQrsDuration)
hbIntervals = [
rPoints[i + 1] - rPoints[i] for i in range(len(rPoints) - 1)
]
rrIntervals.append(np.array(hbIntervals))
lenHb = len(hbIntervals)
minIter = min(lenHb, lenQ, lenR, lenSigQrs, lenHr)
if minIter < minThreshold:
# Rollback
del heartRatesDuringHeartBeat[-1]
del qAmplitudes[-1]
del rAmplitudes[-1]
del qrsDurations[-1]
del rrIntervals[-1]
elif minIter < minSize:
minSize = minIter
qAmplitudes = np.array(
[np.array(x[:minSize], dtype='int64') for x in qAmplitudes])
rAmplitudes = np.array(
[np.array(x[:minSize], dtype='int64') for x in rAmplitudes])
rrIntervals = np.array(
[np.array(x[:minSize], dtype='int64') for x in rrIntervals])
qrsDurations = np.array(
[np.array(x[:minSize], dtype='int64') for x in qrsDurations])
heartRatesDuringHeartBeat = np.array([
np.array(x[:minSize], dtype='int64') for x in heartRatesDuringHeartBeat
])
qAmplitudes = np.stack(qAmplitudes, axis=0)
rAmplitudes = np.stack(rAmplitudes, axis=0)
heartRatesDuringHeartBeat = np.stack(heartRatesDuringHeartBeat, axis=0)
rrIntervals = np.stack(rrIntervals, axis=0)
qrsDurations = np.stack(qrsDurations, axis=0)
return qAmplitudes, rAmplitudes, heartRatesDuringHeartBeat, rrIntervals, qrsDurations, labels[:
size], minSize