Project: Automatic Heart Sound Classification

This repository contains implementations of different deep learning networks which can efficiently classify normal/abnormal heartsound with only PCG (Phonocardiography) input. The framework is based on pytorch-template.

Dataset

We use the heart sound signals from the PhysioNet/CinC challenge 2016, which contains in total of 3,240 heart sound recordings, lasting from 5 seconds to around 120 seconds.

Below is the scores of the challenge¹

As no official data splits were provided, we use train_test_split from sklearn.model_selection to split the data into train(80%), val(10%) and test(10%). By following command, you can train a single fold model, and a train.csv and test.csv will be automatically generated under data/.

python train.py -c config/config_crnn.json.json

A k-fold validation framework is also implemented.

python train_fold_validation.py -c config/config_crnn.json

Audio Preprocess & Feature Extraction

• The fifth order Butterworth band-pass filter was applied to remove low-frequency artifacts, baseline wandering as well as high-frequency inference. Log-Mel Spectrogram with 128 Mel-bins were selected in the experiment.
• No extra segmentation was applied in this work.
• Log-Mel Spectrogram with 128 Mel-bins are selected as the input features. File to extract features can be find in utils/audio_feature_extractor.py.

Models

Four different types of neural networks were adopted in the project. The structures of each type are illustrated below:

simple_cnn

VGG_like CNN

BiLSTM

CRNN

Validation metrics

Accuracy = (TP + TN) \ (TP + FN + FP + TN)
Sensitivity = TP \ (TP + FN) 
Specificity = TN \ (FP + TN) 
MAcc = (Sensitivity + Specificity) \ 2 
F1_Score = 2 * TP \ (2 * TP + FP + FN)

Results

Model	Model size	Accuracy	Sensitivity	Specificity	MAcc	F1 Score
simple_cnn	428.5MB	85.2	83.6	91.9	87.8	90.1
VGG11	72MB	92.3	95.4	79.0	87.2	95.2
BiLSTM	37MB	83.0	82.1	87.1	84.6	88.7
CRNN	59MB	88.0	86.6	93.5	90.1	92.1

10 fold validation with model CRNN

Fold	Accuracy	Sensitivity	Specificity	MAcc	F1 Score
1	0.898	0.891	0.925	0.908	0.933
2	0.941	0.961	0.866	0.913	0.963
3	0.917	0.926	0.881	0.903	0.946
4	0.929	0.914	0.985	0.950	0.953
5	0.935	0.946	0.896	0.921	0.959
6	0.809	0.775	0.940	0.857	0.866
7	0.886	0.876	0.924	0.900	0.924
8	0.898	0.888	0.940	0.913	0.933
9	0.898	0.899	0.894	0.897	0.934
10	0.880	0.888	0.848	0.868	0.922
Average	0.899	0.896	0.910	0.903	0.933

requirements

gensim==4.1.2
h5py==2.10.0
librosa==0.8.1
numba==0.54.0
numpy==1.20.3
opencv-python==4.5.5.62
pandas==1.3.3
pylint==2.11.1
pyparsing==2.4.7
scikit-learn==0.24.2
scipy==1.7.1
six==1.16.0
SoundFile==0.10.3.post1
spacy==3.2.1
spacy-legacy==3.0.8
spacy-loggers==1.0.1
tensorboard==2.7.0
tensorboard-data-server==0.6.1
tensorboard-plugin-wit==1.8.0
tokenizers==0.11.4
torch==1.10.0
tqdm==4.62.2
urllib3==1.26.6
zsvision==0.7.12

Footnotes

1. G. D. Clifford et al., "Classification of normal/abnormal heart sound recordings: The PhysioNet/Computing in Cardiology Challenge 2016," 2016 Computing in Cardiology Conference (CinC), 2016, pp. 609-612.] ↩