Heart Sounds Segmentation

Graduate thesis in Electronics Engineering at Faculty of Engineering, University of Buenos Aires.

Requirements

• MATLAB R2019b or newer

Datasets

The dataset used in this project is the one provided by David Springer. Additionally, it has automatically extracted labels using Springer's labeling algorithm.

Labelled PCG example

The dataset can be downloaded from here in MAT format.

It contains the following fields.

• audio_data: raw audio data
• labels: label for each sample.

1: Sound 1
2: Systolic interval
3: Sound 2
4: Diastolic interval

• annotations: R-wave and end of T-wave annotations.
• binary_diagnosis: 0 if it's normal or 1 if it's abnormal.
• patient_number: patient number associated with audio data by position in the array.
• features: Hilbert envelope, Homomorphic envelope, DWT envelope, PSD envelope.

Note: The features in the datasets are not used in this project except for the Hilbert Envelope.

Extracted features

The features used in the training are instantaneous frequencies computed by the Fourier Synchrosqueezed Transform (FSST). Below you can see a 2D and 3D spectrogram using the FSST.

Results

K-Fold Cross-validation has been used to obtain results. Where K = 10.

After that, only one model is selected by taking into consideration the Area Under the Curve (AUC) and metrics such as Precision, Sensitivity, F1-score and Accuracy.

Fold number 1 turned out to be the best one. Below you can see the Receiver Operation Characteristic (ROC) curve and its confusion matrix.

Training progress

The training was performed on a validation set to control the losses. Preventing the model from overfitting.

|======================================================================================================================|
|  Epoch  |  Iteration  |  Time Elapsed  |  Mini-batch  |  Validation  |  Mini-batch  |  Validation  |  Base Learning  |
|         |             |   (hh:mm:ss)   |   Accuracy   |   Accuracy   |     Loss     |     Loss     |      Rate       |
|======================================================================================================================|
|       1 |           1 |       00:03:33 |       23.81% |       54.82% |       1.3868 |       1.6246 |          0.0100 |
|       1 |          50 |       00:36:26 |       68.17% |       79.93% |       0.7300 |       0.6020 |          0.0100 |
|       1 |         100 |       01:08:00 |       79.32% |       87.75% |       0.5484 |       0.3595 |          0.0100 |
|       1 |         150 |       01:38:52 |       85.29% |       89.86% |       0.4015 |       0.2817 |          0.0100 |
|       1 |         200 |       02:20:33 |       85.14% |       84.66% |       0.4000 |       0.4206 |          0.0100 |
|       2 |         250 |       02:46:28 |       88.87% |       91.66% |       0.3009 |       0.2323 |          0.0100 |
|       2 |         300 |       03:26:02 |       90.89% |       91.93% |       0.2512 |       0.2306 |          0.0100 |
|       2 |         350 |       03:52:03 |       89.28% |       91.61% |       0.2943 |       0.2364 |          0.0100 |
|       2 |         400 |       04:17:42 |       89.53% |       92.49% |       0.2711 |       0.2140 |          0.0100 |
|       3 |         450 |       04:48:48 |       86.87% |       92.67% |       0.3368 |       0.2074 |          0.0100 |
|       3 |         500 |       05:30:56 |       90.88% |       93.18% |       0.2398 |       0.1887 |          0.0100 |
|       3 |         550 |       05:59:04 |       89.65% |       92.69% |       0.2736 |       0.2114 |          0.0100 |
|       3 |         600 |       06:41:37 |       92.79% |       92.84% |       0.1917 |       0.2019 |          0.0100 |
|       3 |         650 |       07:33:08 |       91.32% |       92.85% |       0.2366 |       0.2066 |          0.0100 |
|       4 |         700 |       08:19:27 |       92.58% |       93.73% |       0.1957 |       0.1856 |          0.0010 |
|       4 |         750 |       08:44:36 |       92.41% |       93.88% |       0.2048 |       0.1797 |          0.0010 |
|       4 |         800 |       09:21:05 |       93.42% |       93.86% |       0.1695 |       0.1804 |          0.0010 |
|       4 |         850 |       10:12:13 |       94.16% |       93.57% |       0.1525 |       0.1876 |          0.0010 |
|       5 |         900 |       10:54:25 |       93.85% |       93.88% |       0.1601 |       0.1791 |          0.0010 |
|       5 |         950 |       11:24:33 |       94.71% |       93.77% |       0.1439 |       0.1830 |          0.0010 |
|       5 |        1000 |       12:13:00 |       95.13% |       93.79% |       0.1266 |       0.1877 |          0.0010 |
|       5 |        1010 |       12:21:20 |       94.45% |              |       0.1483 |              |          0.0010 |
|======================================================================================================================|

Confusion Matrix

The actual confision matrix is the 4x4 matrix. In addition to it on the right side you can find the sensitivity or recall for each class. Below, the precision is computed for each class as well.

Off of the confusion matrix the accuracy can also be computed, yielding a 93.29%.

ROC Curve

In this plot you can find the ROC curve for each class and the AUC scores are computed.

Curve	AUC
S1	0.984
Sys	0.992
S2	0.994
Dias	0.992
-------	--------
Average	0.991

Model

The model has been saved for future use and can be found in here.

Publication

The paper has been published in the Elektron Journal at the University of Buenos Aires. Follow this link to read more about it.

Contributors

• Supervisor: Dr. Eng. Pedro David Arini
• Student: Álvaro Joaquín Gaona