preprocess.py
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import os
import numpy as np
import scipy.io as sio
# import cv2
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from biosppy.signals import ecg
from tqdm import tqdm
ANO_RATIO=0 # add ANO_RATIO(e.g. 0.1%) anomalous to training data
LEFT=140
RIGHT=180
DATA_DIR="./dataset/source/" # source mit-bih data
SAVE_DIR="./dataset/preprocessed/ano0/"
PATIENTS=[100,101,103,105,106,108,109,
111,112,113,114,115,116,117,118,119,
121,122,123,124,
200,201,202,203,205,207,208,209,
210,212,213,214,215,219,
220,221,222,223,228,230,231,232,233,234] #remove 102 104 207 217
'''
As recommended by the AAMI, the records with paced beats(/) were not considered,
namely 102, 104, 107, and 217.
'''
N={"N","L","R"}
S={"a", "J", "A", "S", "j", "e"}
V={"V","E"}
F={"F"}
Q={"/", "f", "Q"}
BEAT=N.union(S,V,F,Q)
ABNORMAL_BEAT=S.union(V,F,Q)
def bisearch(key, array):
'''
search value which is most closed to key
:param key:
:param array:
:return:
'''
lo = 0
hi = len(array)-1
while lo <= hi:
mid = lo + int((hi - lo) / 2)
if key <array[mid]:
hi = mid - 1
elif key>array[mid] :
lo = mid + 1
else:
return array[mid]
if hi<0:
return array[0]
if lo>=len(array):
return array[-1]
return array[hi] if (key-array[hi])<(array[lo]-key) else array[lo]
def processPatient(patient):
normal_samples = []
abnormal_samples = []
samples=[]
record = sio.loadmat(os.path.join(DATA_DIR, str(patient) + ".mat"))
annotation = sio.loadmat(os.path.join(DATA_DIR, str(patient) + "ann.mat"))
if patient==114: # patient 114 record's mlii in lead B
sig=record["signal"][:,1]
sig2=record["signal"][:,0]
else: # others' in lead A
sig = record["signal"][:,0]
sig2=record["signal"][:, 1]
assert len(sig)==len(sig2)
sig_out=ecg.ecg(signal=sig, sampling_rate=360., show=False)
sig=sig_out["filtered"]
sig2=ecg.ecg(signal=sig2, sampling_rate=360., show=False)["filtered"]
r_peaks=sig_out["rpeaks"]
ts = record["tm"]
ann_types = annotation["type"]
ann_signal_idx = annotation["ann"]
for ann_idx, ann_type in enumerate(ann_types):
if ann_type in BEAT:
sig_idx=ann_signal_idx[ann_idx][0]
if sig_idx-LEFT>=0 and sig_idx+RIGHT<len(sig):
if ann_type in N:
closed_rpeak_idx=bisearch(sig_idx,r_peaks)
if abs(closed_rpeak_idx-sig_idx)<10:
# normal_samples.append((sig[sig_idx-LEFT:sig_idx+RIGHT],ann_type))
samples.append(([sig[sig_idx-LEFT:sig_idx+RIGHT],sig2[sig_idx-LEFT:sig_idx+RIGHT]],'N',ann_type))
else:
# abnormal_samples.append((sig[sig_idx-LEFT:sig_idx+RIGHT],ann_type))
AAMI_label=""
if ann_type in S:
AAMI_label = "S"
elif ann_type in V:
AAMI_label = "V"
elif ann_type in F:
AAMI_label = "F"
elif ann_type in Q:
AAMI_label="Q"
else:
raise Exception("annonation type error")
assert AAMI_label!=""
samples.append(([sig[sig_idx - LEFT:sig_idx + RIGHT],sig2[sig_idx-LEFT:sig_idx+RIGHT]], AAMI_label, ann_type))
return np.array(samples)
def process():
Full_samples=[]
N_samples=[]
V_samples=[]
S_samples=[]
F_samples=[]
Q_samples=[]
print("Read from records")
for patient in tqdm(PATIENTS):
samples=processPatient(patient)
for sample in samples:
Full_samples.append(sample)
if sample[1]=="N":
N_samples.append(sample[0])
elif sample[1]=="S":
S_samples.append(sample[0])
elif sample[1]=="V":
V_samples.append(sample[0])
elif sample[1]=="F":
F_samples.append(sample[0])
elif sample[1]=="Q":
Q_samples.append(sample[0])
else:
raise Exception("sample AAMI type error, input type: {}".format(sample[1]))
Full_samples=np.array(Full_samples)
N_samples=np.array(N_samples)
V_samples=np.array(V_samples)
S_samples=np.array(S_samples)
F_samples=np.array(F_samples)
Q_samples=np.array(Q_samples)
np.random.shuffle(N_samples)
np.random.shuffle(V_samples)
np.random.shuffle(S_samples)
np.random.shuffle(F_samples)
np.random.shuffle(Q_samples)
if ANO_RATIO >0:
print("before shapes")
print("N \t:{}".format(N_samples.shape))
print("V \t:{}".format(V_samples.shape))
print("S \t:{}".format(S_samples.shape))
print("F \t:{}".format(F_samples.shape))
print("Q \t:{}".format(Q_samples.shape))
N_size=N_samples.shape[0]
V_size=V_samples.shape[0]
S_size=S_samples.shape[0]
F_size=F_samples.shape[0]
Q_size=Q_samples.shape[0]
ANO_SIZE=V_size+S_size+F_size+Q_size
ano_size=int(ANO_RATIO/(1+ANO_RATIO)*N_size)
V_ano_size=int(ano_size*V_size/ANO_SIZE)
S_ano_size = int(ano_size * S_size / ANO_SIZE)
F_ano_size= int(ano_size * F_size / ANO_SIZE)
Q_ano_size = int(ano_size * Q_size / ANO_SIZE)
if Q_ano_size==0:
Q_ano_size=1
N_samples=np.concatenate([N_samples,V_samples[-V_ano_size:]])
V_samples=V_samples[:-V_ano_size]
N_samples = np.concatenate([N_samples, S_samples[-S_ano_size:]])
S_samples = S_samples[:-S_ano_size]
N_samples = np.concatenate([N_samples, F_samples[-F_ano_size:]])
F_samples = F_samples[:-F_ano_size]
N_samples = np.concatenate([N_samples, Q_samples[-Q_ano_size:]])
Q_samples = Q_samples[:-Q_ano_size]
print("#########")
print("N \t:{}".format(N_samples.shape))
print("V \t:{}".format(V_samples.shape))
print("S \t:{}".format(S_samples.shape))
print("F \t:{}".format(F_samples.shape))
print("Q \t:{}".format(Q_samples.shape))
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR)
np.save(os.path.join(SAVE_DIR, "full_samples.npy"), Full_samples)
np.save(os.path.join(SAVE_DIR, "N_samples.npy"), N_samples)
np.save(os.path.join(SAVE_DIR, "V_samples.npy"), V_samples)
np.save(os.path.join(SAVE_DIR, "S_samples.npy"), S_samples)
np.save(os.path.join(SAVE_DIR, "F_samples.npy"), F_samples)
np.save(os.path.join(SAVE_DIR, "Q_samples.npy"), Q_samples)
print("Finshed !!!")
if __name__ == '__main__':
process()