Heartbeat Classification

Using a dataset containing heartbeat sounds with 5 different sounds: artifact,murmur, extrastole, extrahls and normal.
With this data we’re going to perform a Heartbeat classification with CNN and LSTM neural networks to compare their performance.

For this project I’m using Python 3.7.7

Install dependencies

To install all the necesary dependencies, run in the terminal:

pip install - r requirements

Data exploration

You can check Data_exploration Jupyter Notebook to explore the data I will be using in this project. In this chart we can see the
distribution of the classes in the dataset.

Also, you can find different plots where you can check for example the onset strength (check librosa python library).
Onset strength computes a spectral flux onset strength envelope. Onset strength at time t is determined by: mean_f max(0, S[f, t] - ref_S[f, t - lag]) where ref_S is S after local max filtering along the frequency axis [1]. By default, if a time series y is provided, S will be the log-power Mel spectrogram.

Models

You can find the scrips in scrips folder where LSTM, CNN and Random Forest model were created.

To train LSTM and Random forest models you need to have this structure. If you don’t have a data folder, please create it
and fill the subfolders with .wav audio files.

data
    set_a
        .wav
    set_b
        .wav
    test
        .wav

On the other hand, if you want to run CNN model, you first need to convert the audio to an image file with an script called “beatsoundtoimage.py”
which you can find in utils folder. After converting the audio to images you need to have the next structure to train the CNN model:

images
    train
        normal
        murmur
        extrahls
        artifact
        extrastole
    test
        unlabelled

To do so, you can run “relocatefilesinfolder.py” placed in utils folder.

Performance comparison

I have trained an CNN and a LSTM neural network. You can go to LSMT_model jupyter notebook to see all the process. Also,
you can go to models/losses/ directory where you can find LSTM and CNN model’s losses in .npz format just in case you want to plot it.

Future work

Increase number of epochs and fine tune the model to reach a better accuracy for all the models.