import os from tqdm import tqdm from wfdb import io, plot import pandas as pd # https://mc.ai/diagnosing-myocardial-infarction-using-long-short-term-memory-networks-lstms/ # The folder where you want to store your data data_folder = '../data/' # First get the list of available records and then download # those records and store them in data_folder. record_names = io.get_record_list('sddb') #io.dl_database('sddb', data_folder, record_names) # Read the first record record_name = record_names[0] print(record_name) record = io.rdrecord(record_name="../data/" + record_name) records = [] for record_name in tqdm(record_names): record = io.rdrecord(record_name=os.path.join('../data/', record_name)) print(record.sig_name) print(record.comments) # label = comments_to_dict(record.comments)['Reason for admission'][1:] label = record.comments patient = record_name.split('/')[0] signal_length = record.sig_len records.append({ 'name': record_name, 'label': label, 'patient': patient,
import keras from keras.layers import Conv1D, MaxPooling1D from keras.layers import Activation, Dropout, Flatten, Dense from keras import backend as K import os def comments_to_dict(comments): key_value_pairs = [comment.split(':') for comment in comments] return {pair[0]: pair[1] for pair in key_value_pairs} data_folder = 'data' db = 'ptbdb' record_names = io.get_record_list(db) record_names def record_to_row(record, patient_id): row = {} row['patient'] = patient_id row['name'] = record.record_name row['label'] = comments_to_dict(record.comments)['Reason for admission'][1:] row['signals'] = record.p_signal row['signal_length'] = record.sig_len channels = record.sig_name signals = record.p_signal.transpose() row['channels'] = channels
import os from tqdm import tqdm from wfdb import io, plot import pandas as pd # https://mc.ai/diagnosing-myocardial-infarction-using-long-short-term-memory-networks-lstms/ # The folder where you want to store your data data_folder = '../data/' # First get the list of available records and then download # those records and store them in data_folder. record_names = io.get_record_list('ptbdb') #io.dl_database('ptbdb', data_folder, record_names) # Read the first record record_name = record_names[0] print(record_name) record = io.rdrecord(record_name="../data/ptdb/" + record_name) records = [] for record_name in tqdm(record_names): record = io.rdrecord( record_name=os.path.join('../data/ptdb/', record_name)) print(record.sig_name) # label = comments_to_dict(record.comments)['Reason for admission'][1:] label = record.comments[4].split(": ")[-1] patient = record_name.split('/')[0] signal_length = record.sig_len records.append({ 'name': record_name, 'label': label, 'patient': patient,