title: Multilabel classification using LSTM on variable length signal using Keras
body: <p>I have recently started working on ECG signal classification in to various classes. It is basically multi label classification task (Total 4 classes). I am new to Deep Learning, LSTM and Keras that why i am confused in few things.</p>
  
  <ol>
  <li><p>I am thinking about giving normalized original signal as input to the network, is this a good approach?</p></li>
  <li><p>I also need to understand training input shape for LSTM as ECG signals are of variable length (9000 to 18000 samples) and usually classifier need fixed variable input. How can i handle such type of input in case of LSTM.</p></li>
  <li><p>Finally what should be structure of deep LSTM network for such lengthy input and how many layers should i use.</p></li>
  </ol>
  
  <p>Thanks for your time.
  Regards</p>