Multilabel classification using LSTM on variable length signal using Keras

Question

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.

1. I am thinking about giving normalized original signal as input to the network, is this a good approach?

2. 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.

3. Finally what should be structure of deep LSTM network for such lengthy input and how many layers should i use.

Thanks for your time. Regards

Answer 1

I am thinking about giving normalized original signal as input to the network, is this a good approach?

Yes this is a good approach. It is actually quite standard for Deep Learning algorithms to give them your input normalized or rescaled.

This usually helps your model converge faster, as now you are inside smaller range (i.e.: [-1, 1]) instead of greater un-normalized ranges from your original input (say [0, 1000]). It also helps you get better, more precise results, as it helps solve problems like the vanishing gradient as well as adapting better to modern activation and optimizer functions.

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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.

This part is really important. You are correct, LSTM expects to receive inputs with a fixed shape, one that you know beforehand (in fact, any Deep Learning layer expects fixed shape inputs). This is also explained in the keras docs on Recurrent Layers where they say:

Input shape

3D tensor with shape (batch_size, timesteps, input_dim).

As we can see, it expects your data to have a number of timesteps as well as a dimension on each one of those timesteps (batch size is usually 1). To exemplify, suppose your input data consists of elements like: [[1,4],[2,3],[3,2],[4,1]]. Then, using a batch_size of 1, the shape of your data would be (1,4,2). As you have 4 timesteps, each with 2 features.

So bottom line, you have to make sure that you pre-process you data so it has a fixed shape you can then pass to your LSTM layers. This one you will have to find out by yourself, as you know your data and problem better than we do.

Maybe you can fix the samples you obtain from your signal, discarding some and keeping others so every signal is of the same length (if you say your signals are between 9k and 18k choosing 9000 could be the logical choice, discarding samples from the others you get). You could even do some other conversion to your data in a way that you can map from inputs of 9000-18000 to a fixed size.

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Finally what should be structure of deep LSTM network for such lengthy input and how many layers should i use.

This one is really quite broad and doesn't have a unique answer. It would depend on the nature of your problem, and determining those parameters a priori is not so straightforward.

What I recommend you do is to start with a simple model first, and then add layers and blocks (neurons) incrementally until you are satisfied with the results.

Try just one hidden layer first, train and test your model and check your performance. You can then add more blocks and see if your performance improved. You can also add more layers and check for the same until you are satisfied.

This is a good way to create Deep Learning models, as you will arrive to the results you want while keeping your Network as lean as possible, which in turn helps your execution time and complexity. Good luck with your coding, hope you find this useful.