166 lines
5.9 KiB
Python
166 lines
5.9 KiB
Python
import wfdb
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import os
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import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import numpy as np
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import pickle
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#project_dir = "C:/Users/Nils/Documents/0000MASTER/IM1/DSA/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0"
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# Directories and file paths
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# --------------------------------------------------------------------------------
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# Specify the directory where the WFDB records are stored
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project_dir = 'C:/Users/felix/OneDrive/Studium/Master MDS/1 Semester/DSA/physionet/large_12_ecg_data/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0'
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data_dir = project_dir + '/WFDBRecords'
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path_diag_lookup = "C:/Users/felix/OneDrive/Studium/Master MDS/1 Semester/DSA/physionet/large_12_ecg_data/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/ConditionNames_SNOMED-CT.csv"
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#path_diag_lookup = project_dir + '/ConditionNames_SNOMED-CT.csv'
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#project_dir +'/ConditionNames_SNOMED-CT.csv'
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# --------------------------------------------------------------------------------
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def get_diagnosis_ids(record):
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# Get the diagnosis
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diagnosis = record.comments[2]
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# clean the diagnosis
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diagnosis = diagnosis.replace('Dx: ', '')
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list_diagnosis = [int(x.strip()) for x in diagnosis.split(',')]
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return list_diagnosis
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def get_diagnosis_name(diagnosis):
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# get the diagnosis name from the lookup table
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name = [diagnosis_lookup[diagnosis_lookup['Snomed_CT'] == x]['Full Name'].to_string(index=False) for x in diagnosis]
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return name
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def filter_signal_df_on_diag(df_dict, diagnosis_dict, filter_codes_df):
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# Create a list with filter codes and add 0 for padding
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filter_cod_li = list(filter_codes_df['Snomed_CT']) + [0]
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# Filter the diagnosis dictionary based on the filter codes
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filter_dict_diag = {k: v for k, v in diagnosis_dict.items() if all(i in filter_cod_li for i in v)}
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# Filter the df_dict based on the filtered_dict_diag
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filtered_df_dict = {i: df.loc[df.index.isin(filter_dict_diag.keys())] for i, df in df_dict.items()}
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return filtered_df_dict
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# --------------------------------------------------------------------------------
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# Explore the data
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# --------------------------------------------------------------------------------
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# Read the diagnosis lookup table
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diagnosis_lookup = pd.read_csv(path_diag_lookup)
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#print(diagnosis_lookup.head())
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# Filter data based on the diagnosis
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# ----------------------------------------------
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"""
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SB, Sinusbradykardie
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AFIB, Vorhofflimmern und Vorhofflattern (AFL)
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GSVT, supraventrikulärer Tachykardie, Vorhoftachykardie, AV-Knoten-Reentry-Tachykardie, AV-Reentry-Tachykardie, Vorhofschrittmacher
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SR Sinusrhythmus und Sinusunregelmäßigkeiten
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(Vorhofschrittmacher = 713422000)
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"""
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categories = {
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'SB': [426177001],
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'AFIB': [164889003, 164890007],
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'GSVT': [426761007, 713422000, 233896004, 233897008, 713422000],
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'SR': [426783006, 427393009]
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}
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#diag_dict = {k: 0 for k in categories.keys()}
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diag_dict = {k: [] for k in categories.keys()}
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# Create a counter for the number of records
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counter = 0
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max_counter = 100#100_000
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# Loop through the records
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for dir_th in os.listdir(data_dir):
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path_to_1000_records = data_dir + '/' + dir_th
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for dir_hd in os.listdir(path_to_1000_records):
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path_to_100_records = path_to_1000_records + '/' + dir_hd
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for record_name in os.listdir(path_to_100_records):
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# check if .hea is in the record_name
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if '.hea' not in record_name:
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continue
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# Remove the .hea extension from record_name
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record_name = record_name.replace('.hea', '')
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try:
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# Read the record
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record = wfdb.rdrecord(path_to_100_records + '/' + record_name)
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# Get the diagnosis
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diagnosis = np.array(get_diagnosis_ids(record))
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# check if diagnosis is a subset of one of the categories
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for category_name, category_codes in categories.items():
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#if set(diagnosis).issubset(set(category_codes)):
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# if any of the diagnosis codes is in the category_codes
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if any(i in category_codes for i in diagnosis):
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# Increment the counter for the category
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#diag_dict[category_name] += 1
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# Add record to the category
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diag_dict[category_name].append(record)
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break
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# Increment the counter
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counter += 1
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counter_bool = counter >= max_counter
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# Break the loop if we have read max_counter records
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if counter % 100 == 0:
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print(f"Read {counter} records")
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if counter_bool:
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break
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except Exception as e:
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print(f"Failed to read record {record_name} due to ValueError")
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if counter_bool:
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break
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if counter_bool:
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break
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"""
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if any(i in category_codes for i in diagnosis):
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ID: SB, Count: 16559
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ID: AFIB, Count: 9839
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ID: GSVT, Count: 948
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ID: SR, Count: 9720
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break
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Der Counter gibt an ob eine Diagnose in einer Kategorie ist
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---------------------------------------------------------------------------------------------------------------------
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set(diagnosis).issubset(set(category_codes)):
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ID: SB, Count: 8909
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ID: AFIB, Count: 1905
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ID: GSVT, Count: 431
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ID: SR, Count: 7299
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break
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Der Counter gibt an ob alle Diagnosen in einer Kategorie sind
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"""
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# for id, count in diag_dict.items():
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# print(f"ID: {id}, Count: {count}")
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# write to pickle
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for cat_name, records in diag_dict.items():
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print(f"Writing {cat_name} to pickle with {len(records)} records")
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# if path not exists create it
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if not os.path.exists('./data'):
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os.makedirs('./data')
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with open(f'./data/{cat_name}.pkl', 'wb') as f:
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pickle.dump(records, f)
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