295 lines
11 KiB
Python
295 lines
11 KiB
Python
from numpy import diff, argwhere, argmax, where, delete, insert, mean, array, full, nan
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from numpy import min as np_min
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from numpy import max as np_max
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from torch import FloatTensor, no_grad, load
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from torch import device as torch_device
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from torch.cuda import is_available, empty_cache
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from torch.nn.functional import sigmoid
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from func.BCGDataset import BCG_Operation
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from func.Deep_Model import Unet,Fivelayer_Lstm_Unet,Fivelayer_Unet,Sixlayer_Unet
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def evaluate(test_data, model,fs,useCPU):
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orgBCG = test_data
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operation = BCG_Operation()
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# 降采样
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orgBCG = operation.down_sample(orgBCG, down_radio=int(fs//100)).copy() #一开始没加.copy()会报错，后来加了就没事了，结果没影响
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# plt.figure()
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# plt.plot(orgBCG)
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# plt.show()
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orgBCG = orgBCG.reshape(-1, 1000)
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# test dataset
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orgData = FloatTensor(orgBCG).unsqueeze(1)
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# predict
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if useCPU == True:
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gpu = False
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device = torch_device("cpu")
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else:
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gpu = is_available()
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device = torch_device("cuda" if is_available() else "cpu")
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# if gpu:
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# orgData = orgData.cuda()
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# model.cuda()
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orgData = orgData.to(device)
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model = model.to(device)
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with no_grad():
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y_hat = model(orgData)
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y_prob = sigmoid(y_hat)
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beat = (y_prob>0.5).float().view(-1).cpu().data.numpy()
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beat_diff = diff(beat)
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up_index = argwhere(beat_diff==1)
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down_index = argwhere(beat_diff==-1)
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return beat,up_index,down_index,y_prob
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def find_TPeak(data,peaks,th=50):
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"""
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找出真实的J峰或R峰
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:param data: BCG或ECG数据
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:param peaks: 初步峰值（从label中导出的location_R）
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:param th: 范围阈值
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:return: 真实峰值
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"""
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return_peak = []
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for peak in peaks:
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if peak>len(data):continue
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min_win,max_win = max(0,int(peak-th)),min(len(data),int(peak+th))
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return_peak.append(argmax(data[min_win:max_win])+min_win)
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return return_peak
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def new_calculate_beat(y,predict,th=0.5,up=10,th1=100,th2=45): #通过预测计算回原来J峰的坐标 输入：y_prob,predict=ture,up*10,降采样多少就乘多少
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"""
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加上不应期算法，消除误判的峰
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:param y: 预测输出值或者标签值（label）
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:param predict: ture or false
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:param up: 降采样为多少就多少
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:return: 预测的J峰位置
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"""
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if predict:
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beat = where(y>th,1,0)
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else:
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beat = y
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beat_diff = diff(beat) #一阶差分
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up_index = argwhere(beat_diff == 1).reshape(-1)
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down_index = argwhere(beat_diff == -1).reshape(-1)
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# print(up_index,down_index)
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# print(y)
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# print(y[up_index[4]+1:down_index[4]+1])
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if len(up_index)==0:
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return [0]
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if up_index[0] > down_index[0]:
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down_index = delete(down_index, 0)
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if up_index[-1] > down_index[-1]:
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up_index = delete(up_index, -1)
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"""
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加上若大于130点都没有一个心跳时，降低阈值重新判决一次，一般降到0.3就可以了；； 但是对于体动片段降低阈值可能又会造成误判，而且出现体动的话会被丢弃，间隔时间也长
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"""
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# print("初始：",up_index.shape,down_index.shape)
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i = 0
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lenth1 = len(up_index)
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while i < len(up_index)-1:
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if abs(up_index[i+1]-up_index[i]) > th1:
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re_prob = y[down_index[i]+15:up_index[i+1]-15] #原本按正常应该是两个都+1的，但是由于Unet输出低于0.6时，把阈值调小后会在附近一两个点也变为1，会影响判断
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# print(re_prob.shape)
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beat1 = where(re_prob > 0.1, 1, 0)
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# print(beat1)
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if sum(beat1) != 0 and beat1[0] != 1 and beat1[-1] != 1:
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insert_up_index,insert_down_index = add_beat(re_prob,th=0.1)
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# print(insert_up_index,insert_down_index,i)
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if len(insert_up_index) > 1:
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l = i+1
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for u,d in zip(insert_up_index,insert_down_index):
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up_index = insert(up_index,l,u+down_index[i]+1+15) #np.insert(arr, obj, values, axis) arr原始数组，可一可多，obj插入元素位置，values是插入内容，axis是按行按列插入。
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down_index = insert(down_index,l,d+down_index[i]+1+15)
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l = l+1
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# print('l=', l)
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elif len(insert_up_index) == 1:
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# print(i)
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up_index = insert(up_index,i+1,down_index[i]+insert_up_index+1+15)
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down_index = insert(down_index,i+1,down_index[i]+insert_down_index+1+15)
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i = i + len(insert_up_index) + 1
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else:
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i = i+1
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continue
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else:
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i = i+1
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# print("最终：",up_index.shape,down_index.shape)
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"""
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添加不应期
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"""
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new_up_index = up_index
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new_down_index = down_index
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flag = 0
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i = 0
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lenth = len(up_index)
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while i < lenth:
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if abs(up_index[i+1]-up_index[i]) < th2:
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prob_forward = y[up_index[i]+1:down_index[i]+1]
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prob_backward = y[up_index[i+1]+1:down_index[i+1]+1]
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forward_score = 0
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back_score = 0
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forward_count = down_index[i] - up_index[i]
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back_count = down_index[i+1] - up_index[i+1]
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forward_max = np_max(prob_forward)
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back_max = np_max(prob_backward)
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forward_min = np_min(prob_forward)
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back_min = np_min(prob_backward)
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forward_average = mean(prob_forward)
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back_average = mean(prob_backward)
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if forward_count > back_count:
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forward_score = forward_score + 1
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else:back_score = back_score + 1
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if forward_max > back_max:
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forward_score = forward_score + 1
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else:back_score = back_score + 1
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if forward_min < back_min:
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forward_score = forward_score + 1
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else:back_score = back_score + 1
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if forward_average > back_average:
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forward_score = forward_score + 1
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else:back_score = back_score + 1
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if forward_score >=3:
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up_index = delete(up_index, i+1)
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down_index = delete(down_index, i+1)
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flag = 1
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elif back_score >=3:
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up_index = delete(up_index, i)
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down_index = delete(down_index, i)
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flag = 1
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elif forward_score == back_score:
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if forward_average > back_average:
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up_index = delete(up_index, i + 1)
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down_index = delete(down_index, i + 1)
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flag = 1
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else:
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up_index = delete(up_index, i)
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down_index = delete(down_index, i)
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flag = 1
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if flag == 1:
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i = i
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flag = 0
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else: i = i+1
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else:i = i + 1
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if i > len(up_index)-2:
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break
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# elif abs(up_index[i+1]-up_index[i]) > 120:
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# print("全部处理之后",up_index.shape,down_index.shape)
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predict_J = (up_index.reshape(-1) + down_index.reshape(-1)) // 2*up
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# predict_J = predict_J.astype(int)
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return predict_J
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def add_beat(y,th=0.2): #通过预测计算回原来J峰的坐标 输入：y_prob,predict=ture,up*10,降采样多少就乘多少
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"""
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:param y: 预测输出值或者标签值（label）
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:param predict: ture or false
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:param up: 降采样为多少就多少
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:return: 预测的J峰位置
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"""
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beat1 = where(y>th,1,0)
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beat_diff1 = diff(beat1) #一阶差分
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add_up_index = argwhere(beat_diff1 == 1).reshape(-1)
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add_down_index = argwhere(beat_diff1 == -1).reshape(-1)
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# print(beat1)
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# print(add_up_index,add_down_index)
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if len(add_up_index) > 0:
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if add_up_index[0] > add_down_index[0]:
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add_down_index = delete(add_down_index, 0)
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if add_up_index[-1] > add_down_index[-1]:
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add_up_index = delete(add_up_index, -1)
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return add_up_index, add_down_index
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else:
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return 0
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def calculate_beat(y,predict,th=0.5,up=10): #通过预测计算回原来J峰的坐标 输入：y_prob,predict=ture,up*10,降采样多少就乘多少
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"""
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:param y: 预测输出值或者标签值（label）
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:param predict: ture or false
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:param up: 降采样为多少就多少
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:return: 预测的J峰位置
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"""
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if predict:
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beat = where(y>th,1,0)
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else:
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beat = y
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beat_diff = diff(beat) #一阶差分
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up_index = argwhere(beat_diff == 1).reshape(-1)
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down_index = argwhere(beat_diff == -1).reshape(-1)
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if len(up_index)==0:
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return [0]
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if up_index[0] > down_index[0]:
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down_index = delete(down_index, 0)
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if up_index[-1] > down_index[-1]:
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up_index = delete(up_index, -1)
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predict_J = (up_index.reshape(-1) + down_index.reshape(-1)) // 2*up
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# predict_J = predict_J.astype(int)
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return predict_J
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def preprocess(raw_bcg, fs, low_cut, high_cut, amp_value):
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bcg_data = raw_bcg[:len(raw_bcg) // (fs * 10) * fs * 10]
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preprocessing = BCG_Operation(sample_rate=fs)
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bcg = preprocessing.Butterworth(bcg_data, "bandpass", low_cut=low_cut, high_cut=high_cut, order=3) * amp_value
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return bcg
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def Jpeak_Detection(model_name, model_path, bcg_data, fs, interval_high, interval_low, peaks_value, useCPU):
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model_name = get_model_name(str(model_name))
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if model_name == "Fivelayer_Unet":
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model = Fivelayer_Unet()
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elif model_name == "Fivelayer_Lstm_Unet":
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model = Fivelayer_Lstm_Unet()
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elif model_name == "Sixlayer_Unet":
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model = Sixlayer_Unet()
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elif model_name == "U_net":
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model = Unet()
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else:
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raise Exception
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model.load_state_dict(load(model_path, map_location=torch_device('cpu')))
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model.eval()
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# J峰预测
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beat, up_index, down_index, y_prob = evaluate(bcg_data, model=model, fs=fs, useCPU=useCPU)
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y_prob = y_prob.cpu().reshape(-1).data.numpy()
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predict_J = new_calculate_beat(y_prob, 1, th=0.6, up=fs // 100, th1=interval_high, th2=interval_low)
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predict_J = find_TPeak(bcg_data, predict_J, th=int(peaks_value * fs / 1000))
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predict_J = array(predict_J)
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Interval = full(len(bcg_data), nan)
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for i in range(len(predict_J) - 1):
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Interval[predict_J[i]: predict_J[i + 1]] = predict_J[i + 1] - predict_J[i]
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empty_cache()
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return predict_J, Interval
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def get_model_name(input_string):
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# 找到最后一个 "_" 的位置
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last_underscore_index = input_string.rfind('_')
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# 如果没有找到 "_"
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if last_underscore_index == -1:
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return input_string # 返回整个字符串
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# 返回最后一个 "_" 之前的部分
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return input_string[:last_underscore_index] |