PTB-XL处理程序

ECG处理程序


#data_process = (X_train[5,:,11] - np.roll(data_process,10))/10 #差分
#print(data_process)
import matplotlib.pyplot as plt
from scipy.signal import medfilt
def Slope_condition(data_process, location, Scope, Threshold):for i in range(Scope,0,-1):# 逐渐缩小范围找斜率满足要求的点if location-i>=0 and location+i<=999 :Slope_left = (data_process[location]-data_process[location-i])*100 # 100Hz, 计算左斜率Slope_right = (data_process[location+i]-data_process[location])*100  # 100Hz, 计算右斜率if Slope_left*Slope_right<(Threshold/(Scope-i+1)):return 1else :continuereturn 0 #这样都找不到，那就没有def R_location(data_process, P_N, threshold, baseline):Xlim = []Ylim = []# print(data_process.shape)if P_N == "+":candidate = np.where((data_process-baseline)>=threshold)if P_N == "-":candidate = np.where((data_process-baseline)<=threshold)candidate = candidate[0]# print(candidate)# print(len(candidate))if len(candidate)==0:return Xlim, YlimData_max = data_process[candidate[0]]#print(Data_max)max_index = candidate[0]#print(max_index)for i in candidate:# print(i)if max_index != i-1 and max_index != i :  # 有间断再输出，并且进行下一段比较S_C = Slope_condition(data_process, max_index, 5, -500) #斜率状态if len(Xlim)==0:if S_C :Xlim.append(max_index)Ylim.append(Data_max)else :if S_C and max_index-Xlim[-1]>40: # 一般心率不会超过250/minXlim.append(max_index)Ylim.append(Data_max)# print(Data_max, max_index)max_index = iData_max = data_process[i]else :if Data_max < data_process[i] and P_N == "+" :  # 比较Data_max = data_process[i]max_index = iif Data_max > data_process[i] and P_N == "-" :  # 比较Data_max = data_process[i]max_index = i#print(data_process[i])#print(i)if len(Xlim)==0:return Xlim, Ylimif max_index != 999 and max_index-Xlim[-1]>40 and Slope_condition(data_process, max_index, 5, -500):Xlim.append(max_index)Ylim.append(Data_max)return Xlim, Ylimdef R_location_supplement(data_process,  P_N, Xlim_S, Scope):Xlim = []Ylim = []for i in Xlim_S:Data_max = data_process[i-Scope]max_index = i-Scopefor j in range(i-Scope+1, i+Scope):if j>=1000 :breakif j<=0 :continueif Data_max < data_process[j] and P_N == "+" : # 在一个范围内找最大值 Data_max = data_process[j]max_index = jif Data_max > data_process[j] and P_N == "-" :Data_max = data_process[j]max_index = jXlim.append(max_index)Ylim.append(Data_max)#Xlim.append(i)#Ylim.append(data_process[i])return Xlim, Ylimdef R_location_Sup(data_person, threshold, high):for i in [11,10,9,5,2,1,0]:data_process = data_person[:, i]Baseline = medfilt(data_person[:, i],51)Xlim, Ylim = R_location(data_process, P_N="+", threshold = high, baseline = Baseline)if len(Xlim) >= threshold : #默认心跳频率高于48次/minbreakif len(Xlim) < threshold :Xlim, Ylim = R_location_Sup(data_person=data_person, threshold=threshold, high = high/2)return Xlim, Ylimdef Q_location(data_process,location, Scope):Q_min = data_process[location]Q_l = locationfor i in range(location, location-Scope, -1):if i>=0 :if data_process[i] < Q_min :  #向左在一定范围内找最小（判定条件）Q_min = data_process[i]Q_l = ielse :breakreturn Q_l, Q_mindef S_location(data_process,location, Scope):S_min = data_process[location]S_l = locationfor i in range(location, location+Scope, 1):if i<=999 :if data_process[i] < S_min :  #向右在一定范围内找最小（判定条件）S_min = data_process[i]S_l = ielse :breakreturn S_l, S_mindef Q_S_location(data_process, Xlim_S, Scope):Q_Xlim = []Q_Ylim = []S_Xlim = []S_Ylim = []for i in Xlim_S:Q_X,Q_Y = Q_location(data_process, i, Scope)S_X,S_Y = S_location(data_process, i, Scope)Q_Xlim.append(Q_X)Q_Ylim.append(Q_Y)S_Xlim.append(S_X)S_Ylim.append(S_Y)return Q_Xlim, Q_Ylim, S_Xlim, S_Ylimdef base_leave(data_process, location, Scope, PT, baseline):T_max = abs(data_process[location]-baseline[location])T_l = locationif PT == 'P':range_start = locationrange_end = location+Scopeif PT == 'T':range_start = location-Scoperange_end = location# print(distance)for i in range(range_start, range_end):if i>=1000 :breakif i<0 :continueif abs(data_process[i]-baseline[i]) > T_max :  #  找到离基线最远的点T_max = abs(data_process[i]-baseline[i])T_l = i#if Slope_condition(data_process, T_l, 1, -0) == 0 and (range_end-range_start) > 4:#T_l, data_process[T_l] = base_leave(data_process=data_process, location=location, Scope = Scope, PT=PT, baseline=baseline, distance = distance+1)return T_l, data_process[T_l]def mm_where(data_process, range_start, range_end, P_N = 'max'):T_MM = data_process[range_start]T_l = range_startif P_N == 'max':for i in range(range_start, range_end):if i>=1000 :breakif i<0 :continueif data_process[i] > T_MM :T_MM = data_process[i]T_l = iif P_N == 'min':for i in range(range_start, range_end):if i>=1000 :breakif i<0 :continueif data_process[i] < T_MM :T_MM = data_process[i]T_l = ireturn T_l, T_MMdef PT_location_supplement(data_process, T_Xlim, Scope, PT, baseline):Xlim = []Ylim = []count = 0for i in T_Xlim:count = count+1T_l, T_MM = base_leave(data_process, i, int(Scope[count-1]), PT = PT, baseline = baseline)  # 以在一定范围内离基线最远为判定条件Xlim.append(T_l)Ylim.append(T_MM)return Xlim, Ylimdef Space(Xlim):# 循环左移Xlim_start = np.delete(Xlim, len(Xlim)-1)Xlim_end = np.roll(Xlim, -1)Xlim_end = np.delete(Xlim_end, len(Xlim_end)-1)space = Xlim_end-Xlim_startspace = np.r_[Xlim[0], space, 1000-Xlim[-1]]space.astype(int)return spacedef Space_P(Q_Xlim, TFR_Xlim):Xlim_start = np.r_[0, TFR_Xlim]Xlim_end = np.r_[Q_Xlim, 999]space = Xlim_start - Xlim_endspace.astype(int)return spacedef foot_find(data_process, location, Scope, direction, Baseline):if direction == 'right':S = locationE = location+ScopeD = 1if direction == 'left':S = locationE = location-ScopeD = -1for i in range(S,E,D):if (i+1)>=999:breakif i<0 :continuehigh_R = data_process[i+1]-Baseline[i+1]high = data_process[i]-Baseline[i]if (high*high_R)<0:if(abs(high)<abs(high_R)):return i, data_process[i]if(abs(high)>abs(high_R)):return i+1, data_process[i+1]return i, data_process[i]def TF_location(data_process, Xlim, Scope, direction, Baseline):TFXlim = []TFYlim = []for i in Xlim:TFX, TFY = foot_find(data_process=data_process, location=i, Scope=Scope, direction=direction, Baseline=Baseline)TFXlim.append(TFX)TFYlim.append(TFY)return TFXlim, TFYlimdef PT_location(data_process, Xlim_S, Scope, PT, baseline):space = Space(Xlim_S)#print(space)space.astype(int)if PT == 'P':space = np.delete(space, len(space)-1) #需要左边的间距middle = Xlim_S - space//2  # 获得左边中间位置if PT == 'T':space = np.delete(space, 0)  #需要右边的间距middle = Xlim_S + space//2  # 获得右边中间位置middle[middle > 999] = 999  # 去掉索引超出范围的点middle[middle < 0] = 0middle = middle.astype(int)#print(middle,"middle")#print(Scope)PT_Xlim, PT_Ylim = PT_location_supplement(data_process, middle, Scope=space//2-Scope, PT = PT, baseline = baseline)return PT_Xlim, PT_Ylimdef P_location(data_process, Q_Xlim, TFR_Xlim, Scope, Baseline):P_Xlim = []P_Ylim = []space = Space_P(Q_Xlim, TFR_Xlim)-Scopefor i in range(len(TFR_Xlim)):P_X, P_Y = base_leave(data_process=data_process, location=TFR_Xlim[i], Scope=space[i], PT='P', baseline=Baseline)P_Xlim.append(P_X)P_Ylim.append(P_Y)return P_Xlim, P_Ylim
count = 0
for k in range(0,1):count = count+1if y_train.values[k] ==['NORM']:print(y_train.values[k],k)"""data_person = X_train[k, :, :]# data_process = data_person[:, 0]Xlim, Ylim = R_location_Sup(data_person, threshold=8, high=0.05)# print(Xlim)#T_Xlim, T_Ylim = T_location(data_person[:, index], Xlim, 3)for i in range(1, 12):if i in [9, 10, 11]:Xlim_S, Ylim_S = R_location_supplement(data_person[:, i], P_N="+", Xlim_S=Xlim, Scope=10)else:Xlim_S, Ylim_S = R_location_supplement(data_person[:, i], P_N="+", Xlim_S=Xlim, Scope=5)# print(Xlim_S)Q_Xlim, Q_Ylim, S_Xlim, S_Ylim = Q_S_location(data_person[:, i], Xlim_S, Scope=10)Baseline = medfilt(data_person[:, i], 11)T_Xlim, T_Ylim = PT_location(data_person[:, i], S_Xlim, 5, 'T', Baseline)TFR_Xlim, TFR_Ylim = TF_location(data_person[:, i], T_Xlim, 50, 'right', Baseline)TFL_Xlim, TFL_Ylim = TF_location(data_person[:, i], T_Xlim, 50, 'left', Baseline)P_Xlim, P_Ylim = PT_location(data_person[:, i], Q_Xlim, 5, 'P', Baseline)# print(type(y_train))# print(y_train.index)plt.figure(figsize=(15, 24))plt.subplot(12, 1, i+1)plt.plot(P_Xlim, P_Ylim, 'ko')plt.plot(T_Xlim, T_Ylim, 'bo')plt.plot(Q_Xlim, Q_Ylim, 'go')plt.plot(S_Xlim, S_Ylim, 'yo')plt.plot(Xlim_S, Ylim_S, 'ro')plt.plot(data_person[:, i])plt.plot(Baseline)plt.grid()plt.show()"""
print(count)
# plt.savefig("1.jpg")

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