# Brief Review — A Lightweight 1-D Convolution Neural Network Model for Multi-class Classification of Heart Sounds

## DWT + 1D-CNN for 5-Class Classification

A Lightweight 1-D Convolution Neural Network Model for Multi-class Classification of Heart Sounds, by National Institute of Technology Rourkela, Central University of Rajasthan

DWT + 1D-CNN2022 ICETCI(Sik-Ho Tsang @ Medium)

Heart Sound Classification…

20132022[CirCor Dataset] [CNN-LSTM] [DsaNet] [Modified Xception] [Improved MFCC+Modified ResNet] [Learnable Features + VGGNet/EfficientNet] [DWT + SVM] [MFCC+LSTM]2023[2LSTM+3FC, 3CONV+2FC] [NRC-Net]

==== My Other Paper Readings Are Also Over Here ====

**1D-CNN model**is trained on the multi-resolution domain features obtained using the**discrete wavelet transform (DWT)**for**5-category heart sound classification.**

# Outline

**Preprocessing & DWT****1D-CNN****Results**

# 1. Preprocessing & DWT

## 1.1. Preprocessing

**Re-sampling**: Since the frequency range of the FHS and various pathological sound lie below 500 Hz [5], the signal is**down-sampled from 8 kHz to 1 kHz.****Normalization**: The signal is**amplitude normalized**as below:

**Resizing**: The length of the signal varies from 1.15 to 3.99 seconds. After the observation, it is found that each sample consists of approximately three cardiac cycles.- The signal was
**resized**to an**equal length (2800 samples)**after recognising the onset and offset of the signal. - The above figure shows the normalized signal.

## 1.2. DWT

**DWT**decomposes signal into**low frequency**and**high frequency**components. For**high frequency component**, it is**downsampled and decomposed again**into low frequency and high frequency components.

The heart sound signal is

decomposed up to 5 levelsusing ‘coif5’ as mother wavelet.

- The obtained
**5 detailed level coefficients**and**the approximation level****signal**is shown above.

They are arranged in 1D array which results in

length of 2942. And it isfed into 1D-CNN.

**2. 1D-CNN**

The proposed CNN model is consist of

5 layers, 1 input layer, 2 convolution and pooling layers, 1 fully connected (FC) layer and 1 output layer (softmax).

**50 epochs**and**9 iterations in each epoch**are used, resulting in 450 total iterations with a learning rate of 0.01.**Batch size of 64**is used.

# 3. Results

## 3.1. Yaseen GitHub Dataset

**Yaseen GitHub Dataset**is used, which has**1000 samples**,**200 samples each for 5 categories**, including the aortic stenosis (AS), mitral regurgitation (MR), mitral stenosis (MS), mitral valve prolapse (MVP), and normal (N).**The sampling frequency**of each sample is set to**1 kHz**and**a constant length of 2800 samples.**- The complete dataset was randomly split into
**train (70%)**and**test (30%)**datasets.

## 3.2. Confusion Matrix

The proposed model

efficiently classifies all the categories.

## 3.3. **Per-Class Performance Evaluation**

All 5 classesare classified with anF-score of above 98.18%. For the classesMR and N, the F-score ismore than 99%.

**All 4 metrics are higher than 98% except the precision of As class**which is 97.73%. It can also be observed that for all 5 categories, a**high sensitivity (>98%)**with**high specificity(>99%)**have been achieved.

## 3.4. SOTA Comparison

The highest accuracy (98.9%)is achieved using theproposed method.