README.md

@@ -2,12 +2,8 @@
 
 This project was developed through the Data Science and Analytics course at the Mannheim University of Applied Sciences. A data science cycle was taught theoretically on the basis of lectures and implemented practically in the project. 
 
-<<<<<<< HEAD
-# Analysis of cardiovascular diseases using ECG data
-=======
 
 ## Analysis of cardiovascular diseases using ECG data
->>>>>>> bc4897dacb83ed2c3722b7b5d66b2fff23fdcb6a
 
 
 ## Table of Contents

scripts/generate_data.py

@@ -1,35 +1,49 @@
+"""
+This script reads the WFDB records and extracts the diagnosis information from the comments.
+The diagnosis information is then used to classify the records into categories.
+The categories are defined by the diagnosis codes in the comments.
+The records are then saved to pickle files based on the categories.
+"""
+
 import wfdb
 import os
-import pickle
-import bz2
 import numpy as np
-import pandas as pd
+import pickle
 
-# Funktionen zum Bearbeiten der Daten
+# Directories and file paths
+# --------------------------------------------------------------------------------
+# NOTE: Specify the directory where the WFDB records are stored
+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'
+data_dir = project_dir + '/WFDBRecords'
+path_diag_lookup = project_dir + "/ConditionNames_SNOMED-CT.csv"
+ # --------------------------------------------------------------------------------
+# Functions
 def get_diagnosis_ids(record):
+    """
+    Extracts diagnosis IDs from a record and returns them as a list.
+    Args:
+        record (object): The record object containing the diagnosis information.
+    Returns:
+        list: A list of diagnosis IDs extracted from the record.
+    """
+    # Get the diagnosis
     diagnosis = record.comments[2]
+    # clean the diagnosis
     diagnosis = diagnosis.replace('Dx: ', '')
     list_diagnosis = [int(x.strip()) for x in diagnosis.split(',')]
     return list_diagnosis
+# --------------------------------------------------------------------------------
+# Generate the data
+# --------------------------------------------------------------------------------
+if __name__ == '__main__':
+    """
+    The following categories are used to classify the records:
 
-def get_diagnosis_name(diagnosis):
+    SB,    Sinusbradykardie
-    name = [diagnosis_lookup[diagnosis_lookup['Snomed_CT'] == x]['Full Name'].to_string(index=False) for x in diagnosis]
+    AFIB,  Vorhofflimmern und Vorhofflattern (AFL)
-    return name
+    GSVT,  supraventrikulärer Tachykardie, Vorhoftachykardie, AV-Knoten-Reentry-Tachykardie, AV-Reentry-Tachykardie, Vorhofschrittmacher
-
+    SR     Sinusrhythmus und Sinusunregelmäßigkeiten
-def filter_signal_df_on_diag(df_dict, diagnosis_dict, filter_codes_df):
+    """
-    filter_cod_li = list(filter_codes_df['Snomed_CT']) + [0]
-    filter_dict_diag = {k: v for k, v in diagnosis_dict.items() if all(i in filter_cod_li for i in v)}
-    filtered_df_dict = {i: df.loc[df.index.isin(filter_dict_diag.keys())] for i, df in df_dict.items()}
-    return filtered_df_dict
-
-# Verzeichnisse und Dateipfade
-project_dir = 'C:/Users/arman/PycharmProjects/pythonProject/DSA/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0'
-data_dir = project_dir + '/WFDBRecords'
-path_diag_lookup = "C:/Users/arman/PycharmProjects/pythonProject/DSA/a-large-scale-12-lead-electrocardiogram-database-for-arrhythmia-study-1.0.0/ConditionNames_SNOMED-CT.csv"
-
-# Daten erkunden
-diagnosis_lookup = pd.read_csv(path_diag_lookup)
-
     categories = {
     'SB':    [426177001],
     'AFIB':  [164889003, 164890007],
@@ -38,41 +52,54 @@ categories = {
     }
 
     diag_dict = {k: [] for k in categories.keys()}
+
+    # Create a counter for the number of records
     counter = 0
     max_counter = 100_000
-
+    failed_records = []
+    # Loop through the records
     for dir_th in os.listdir(data_dir):
         path_to_1000_records = data_dir + '/' + dir_th
         for dir_hd in os.listdir(path_to_1000_records):
             path_to_100_records = path_to_1000_records + '/' + dir_hd
             for record_name in os.listdir(path_to_100_records):
+                # check if .hea is in the record_name 
                 if '.hea' not in record_name:
                     continue
+                # Remove the .hea extension from record_name
                 record_name = record_name.replace('.hea', '')
                 try:
+                    # Read the record
                     record = wfdb.rdrecord(path_to_100_records + '/' + record_name)
+                    # Get the diagnosis
                     diagnosis = np.array(get_diagnosis_ids(record))
+                    # check if diagnosis is a subset of one of the categories
                     for category_name, category_codes in categories.items():
+                        # if any of the diagnosis codes is in the category_codes
                         if any(i in category_codes for i in diagnosis):
                             diag_dict[category_name].append(record)
                             break
+                    # Increment the counter of how many records we have read
                     counter += 1
                     counter_bool = counter >= max_counter
+                    # Break the loop if we have read max_counter records
                     if counter % 100 == 0:
-                    print(f"Gelesen {counter} Datensätze")
+                        print(f"Read {counter} records")
                     if counter_bool:
                         break
                 except Exception as e:
-                print(f"Fehler beim Lesen des Datensatzes {record_name}: {e}")
+                    failed_records.append(record_name)
+                    print(f"Failed to read record {record_name} due to ValueError. Sum of failed records: {len(failed_records)}")
             if counter_bool:
                 break
         if counter_bool:
             break
 
+    # write to pickle
     for cat_name, records in diag_dict.items():
-    print(f"Schreibe {cat_name} in eine komprimierte Datei mit {len(records)} Datensätzen")
+        print(f"Writing {cat_name} to pickle with {len(records)} records")
+        # if path not exists create it
         if not os.path.exists('./data'):
             os.makedirs('./data')
-    compressed_filename = f'./data/{cat_name}.pkl.bz2'
+        with open(f'./data/{cat_name}.pkl', 'wb') as f:
-    with bz2.open(compressed_filename, 'wb') as f:
             pickle.dump(records, f)