First Commit.
This commit is contained in:
Yorusora
9
.gitignore
vendored
9
.gitignore
vendored
@ -168,3 +168,12 @@ cython_debug/
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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#.idea/
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HYS/Edf/*
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HYS/event/*
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HYS/Output/*
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HYS/stage/*
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ZDWY/Edf/*
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ZDWY/event/*
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ZDWY/Output/*
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ZDWY/stage/*
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.idea/*
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148
HYS/ReadEDF.py
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148
HYS/ReadEDF.py
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@ -0,0 +1,148 @@
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import os
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import pyedflib
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import numpy as np
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import pandas as pd
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import shutil
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from scipy.signal import resample
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def writefile(filename,data,style='w'):
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"""
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函数说明：
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在指定地点创建一个文件，写入data数据
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:param filename: 需写入的文件地址和名称
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:param data: 需写入的数据
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:return: 无返回
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"""
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data = data.astype('str') #将data数组的数据转换成字符串
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with open(filename,style) as f :
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for str in data :
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f.write(str+'\n')
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return 0
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def event_handle(df):
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if df.columns[0].strip() == 'Event type':
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df['Event type'] = df['Event type'].replace('μ-arousal', 'u-arousal')
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elif df.columns[0].strip() == '事件类型':
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column_mapping = {
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"事件类型\r\r": "Event type",
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"睡眠期\r\r": "Stage",
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"时间\r\r": "Time",
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"屏\r": "Epoch",
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"日期\r\r": "Date",
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"持续时间\r\r": "Duration",
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"体位记录.": "Body Position",
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"判定\r\r": "Validation"
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} # 列名映射
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df = df.rename(columns=column_mapping) # 重命名列
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target_columns = [
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"Event type", "Stage", "Time", "Epoch", "Date", "Duration",
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"HR bef.", "HR extr.", "HR delta", "O2 bef.", "O2 min.",
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"O2 delta", "Body Position", "Validation", "Unnamed: 14"
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] # 添加目标文件中要求的其他列，并赋值为None
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df = df[target_columns] # 按目标列顺序排序
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df['Event type'] = df['Event type'].replace('腿动', 'Leg Movement')
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df['Event type'] = df['Event type'].replace('低通气', 'Hypopnea')
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df['Event type'] = df['Event type'].replace('混合性暂停', 'Mixed apnea')
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df['Event type'] = df['Event type'].replace('中枢性暂停', 'Central apnea')
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df['Event type'] = df['Event type'].replace('阻塞性暂停', 'Obstructive apnea')
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df['Event type'] = df['Event type'].replace('氧减', 'Desaturation')
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df['Event type'] = df['Event type'].replace('长RR间距', 'Long RR')
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df['Event type'] = df['Event type'].replace('心率上升', 'Heart Rate Rise')
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df['Event type'] = df['Event type'].replace('心率下降', 'Heart Rate Drop')
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df['Event type'] = df['Event type'].replace('PTT下降', 'PTT drop')
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df['Event type'] = df['Event type'].replace('鼾声', 'Snore')
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df['Event type'] = df['Event type'].replace('微觉醒', 'u-arousal')
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else:
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assert (df.columns[0].strip() == '事件类型')
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return df
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def upsample_resample(data,data_fs,target_fs):
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up_ratio = target_fs / data_fs
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target_num = int(len(data) * up_ratio)
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# 执行重采样
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if up_ratio > 1:
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data_up = resample(data, target_num)
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target_fs = int(target_fs)
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else:
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print("上采样倍数不大于1，不进行上采样操作")
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data_up = data
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target_fs = int(data_fs)
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return data_up,target_fs
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def read_edf(edfpath,stagepath,eventpath,outpath):
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list = np.array([f for f in os.listdir(edfpath) if f.endswith('.edf')])
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for sample_num in list:
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print("样本编号：", sample_num)
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output_sample = outpath + sample_num.split('.')[0][-4:] + '/'
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if not os.path.exists(output_sample):
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os.makedirs(output_sample)
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##读取edf文件数据
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fpath = edfpath + sample_num
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shutil.copy(fpath, output_sample)
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with pyedflib.EdfReader(fpath) as file:
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##读取导出的睡眠分期标签csv文件
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df = pd.read_csv(stagepath + sample_num.split('.')[0] + '_Stage.csv')
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stage_values = df.values[:,0].reshape(-1)
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##读edf文件信息
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start_time = file.getStartdatetime()
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sec = file.getFileDuration()
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day_start = start_time.strftime("%Y") + "/" + str(start_time.month) + "/" + str(start_time.day)
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sec_start = start_time.strftime("%H") + ":" + start_time.strftime("%M") + ":" + start_time.strftime("%S")
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# sec_start = str(start_time.hour) + ":" + str(f"{start_time.minute:02d}") + ":" + str(f"{start_time.second:02d}")
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assert (df.columns[3] == day_start)
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assert (df.columns[4] == sec_start)
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print("edf文件与睡眠分期标签开始时间一致！！！！！！！")
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##读取Event列表
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df1 = pd.read_csv(eventpath + sample_num.split('.')[0] + '_Event.csv',header=0,encoding='GBK')
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df1 = event_handle(df1)
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df1.to_csv(output_sample + 'SA Label_Raw.csv', index=False, encoding='GBK')
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signal_num = file.signals_in_file
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signal_label = file.getSignalLabels()
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min_length = int(np.min([sec,df.shape[0]]))
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print("裁剪为最小长度:",min_length)
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flag = 0
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for i, index in enumerate(signal_label):
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signal = file.readSignal(i)
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sample_frequency = int(file.getSampleFrequency(i))
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data_org = signal[0:min_length * sample_frequency]
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if (index== 'Flow Patient') :
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if flag == 0:
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writefile(output_sample + 'Flow T_Raw_' + str(sample_frequency) + '.txt', data_org)
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flag = 1
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else:
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writefile(output_sample + 'Flow P_Raw_' + str(sample_frequency) + '.txt', data_org)
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else:
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writefile(output_sample + index + '_Raw_' + str(sample_frequency) + '.txt', data_org)
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with open(os.path.join(output_sample, "StartTime_Raw.txt"), "a") as text_file:
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text_file.write(start_time.strftime("%Y-%m-%d %H:%M:%S\n"))
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text_file.write("{:g}\n".format(int(sec)))
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##裁剪对齐睡眠分期标签
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stage_values = stage_values.astype(int)
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stage_values = np.where(stage_values == 11, 'W', stage_values)
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stage_values = np.where(stage_values == '12', 'R', stage_values)
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stage_values = np.where(stage_values == '13', 'N1', stage_values)
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stage_values = np.where(stage_values == '14', 'N2', stage_values)
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stage = np.where(stage_values == '15', 'N3', stage_values)
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writefile(output_sample + '5_class_Raw_1.txt',stage[0:min_length])
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if __name__ == '__main__':
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edf_dir = 'E:\DB\\testdata\HYS\PSG_Origin/Edf/'
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stage_dir = 'E:\DB\\testdata\HYS\PSG_Origin/Stage/'
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event_dir = 'E:\DB\\testdata\HYS\PSG_Origin/Event/'
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output_dir = 'E:\DB\\testdata\HYS\PSG_Text/'
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read_edf(edf_dir,stage_dir,event_dir,output_dir)
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print("finished")
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143
ZDWY/ZDWY_ReadEDF.py
Normal file
143
ZDWY/ZDWY_ReadEDF.py
Normal file
@ -0,0 +1,143 @@
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import os
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import pyedflib
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import numpy as np
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import pandas as pd
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import shutil
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from scipy.signal import resample
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def writefile(filename,data,style='w'):
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"""
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函数说明：
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在指定地点创建一个文件，写入data数据
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:param filename: 需写入的文件地址和名称
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:param data: 需写入的数据
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:return: 无返回
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"""
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data = data.astype('str') #将data数组的数据转换成字符串
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with open(filename,style) as f :
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for str in data :
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f.write(str+'\n')
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return 0
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def upsample_resample(data,data_fs,target_fs):
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up_ratio = target_fs / data_fs
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target_num = int(len(data) * up_ratio)
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# 执行重采样
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if up_ratio > 1:
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data_up = resample(data, target_num)
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target_fs = int(target_fs)
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else:
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print("上采样倍数不大于1，不进行上采样操作")
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data_up = data
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target_fs = int(data_fs)
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return data_up,target_fs
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def event_handle(df):
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df['类型'] = df['类型'].replace('腿部运动', 'Leg Movement')
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df['类型'] = df['类型'].replace('低通气', 'Hypopnea')
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df['类型'] = df['类型'].replace('混合型呼吸暂停', 'Mixed apnea')
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df['类型'] = df['类型'].replace('中枢性呼吸暂停', 'Central apnea')
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df['类型'] = df['类型'].replace('阻塞性呼吸暂停', 'Obstructive apnea')
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df['类型'] = df['类型'].replace('相对氧减', 'Desaturation')
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df['类型'] = df['类型'].replace('长 RR', 'Long RR')
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df['类型'] = df['类型'].replace('心率上升', 'Heart Rate Rise')
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df['类型'] = df['类型'].replace('心率下降', 'Heart Rate Drop')
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df['类型'] = df['类型'].replace('PTT 下降', 'PTT drop')
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df['类型'] = df['类型'].replace('打鼾', 'Snore')
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df['类型'] = df['类型'].replace('觉醒', 'u-arousal')
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df['睡眠期'] = df['睡眠期'].replace('WK', 'W')
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df['睡眠期'] = df['睡眠期'].replace('REM', 'R')
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column_mapping = {
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"类型": "Event type",
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"睡眠期": "Stage",
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"时间": "Time",
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"时期": "Epoch",
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"持续时间": "Duration",
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"体位": "Body Position",
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"验证": "Validation"
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} # 列名映射
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df = df.rename(columns=column_mapping) # 重命名列
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target_columns = [
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"Event type", "Stage", "Time", "Epoch", "Date", "Duration",
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"HR bef.", "HR extr.", "HR delta", "O2 bef.", "O2 min.",
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"O2 delta", "Body Position", "Validation", "Unnamed: 14"
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] # 添加目标文件中要求的其他列，并赋值为None
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for col in target_columns: # 添加缺失的列并用None填充
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if col not in df.columns:
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df[col] = None
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df = df[target_columns] # 按目标列顺序排序
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return df
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def read_edf(edfpath,stagepath,eventpath,outpath):
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list = np.array([f for f in os.listdir(edfpath) if f.endswith('.edf')])
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for sample_num in list:
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print("样本编号：", sample_num)
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output_sample = outpath + sample_num.split('.')[0] + '/'
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if not os.path.exists(output_sample):
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os.makedirs(output_sample)
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##读取edf文件数据
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fpath = edfpath + sample_num
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shutil.copy(fpath, output_sample)
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with pyedflib.EdfReader(fpath) as file:
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##读取导出的睡眠分期标签csv文件
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df = pd.read_csv(stagepath + sample_num.split('.')[0] + '_Stage.csv')
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stage_values = df.values[:,2].reshape(-1)
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##读edf文件信息
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start_time = file.getStartdatetime()
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sec = file.getFileDuration()
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sec_start = start_time.strftime("%H") + ":" + start_time.strftime("%M") + ":" + start_time.strftime("%S")
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assert (df.values[0,1] == sec_start)
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print("edf文件与睡眠分期标签开始时间一致！！！！！！！")
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##读取Event列表
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df1 = pd.read_csv(eventpath + sample_num.split('.')[0] + '_Event.csv', header=0, encoding='utf-8')
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df1 = event_handle(df1)
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df1.to_csv(output_sample + 'SA Label_Raw.csv', index=False, encoding='GBK')
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signal_num = file.signals_in_file
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signal_label = file.getSignalLabels()
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min_length = int(np.min([sec,df.shape[0]*30]))
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print("裁剪为最小长度:",min_length)
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flag = 0
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for i, index in enumerate(signal_label):
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signal = file.readSignal(i)
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sample_frequency = int(file.getSampleFrequency(i))
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data_org = signal[0:min_length * sample_frequency]
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if (index== 'Flow Patient') :
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|
if flag == 0:
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writefile(output_sample + 'Flow T_Raw_' + str(sample_frequency) + '.txt', data_org)
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flag = 1
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else:
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writefile(output_sample + 'Flow P_Raw_' + str(sample_frequency) + '.txt', data_org)
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else:
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writefile(output_sample + index + '_Raw_' + str(sample_frequency) + '.txt', data_org)
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with open(os.path.join(output_sample, "StartTime_Raw.txt"), "a") as text_file:
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text_file.write(start_time.strftime("%Y-%m-%d %H:%M:%S\n"))
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text_file.write("{:g}\n".format(int(sec)))
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|
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##裁剪对齐睡眠分期标签
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stage_values = np.where(stage_values == 'WK', 'W', stage_values)
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stage_values = np.where(stage_values == 'REM', 'R', stage_values)
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stage_values = np.where(stage_values == 'N1', 'N1', stage_values)
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stage_values = np.where(stage_values == 'N2', 'N2', stage_values)
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stage = np.where(stage_values == 'N3', 'N3', stage_values)
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stage = stage.repeat(30)
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writefile(output_sample + '5_class_Raw_1.txt',stage[0:min_length])
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if __name__ == '__main__':
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edf_dir = 'E:\DB\\testdata\ZD5Y\PSG_Origin/Edf/'
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stage_dir = 'E:\DB\\testdata\ZD5Y\PSG_Origin/Stage/'
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|
event_dir = 'E:\DB\\testdata\ZD5Y\PSG_Origin/Event/'
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output_dir = 'E:\DB\\testdata\ZD5Y\PSG_Text/'
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|
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read_edf(edf_dir,stage_dir,event_dir,output_dir)
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||||||
|
|
||||||
|
print("finished")
|
||||||
5
requirements.txt
Normal file
5
requirements.txt
Normal file
@ -0,0 +1,5 @@
|
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|
mne==1.9.0
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|
numpy==2.2.5
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|
pandas==2.2.3
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|
pyEDFlib==0.1.38
|
||||||
|
xlwt==1.3.0
|
||||||
Reference in New Issue
Block a user