# Brief Review — Classification of Heart Sounds Using Convolutional Neural Network

## 497 Features + 1D-CNN

Classification of Heart Sounds Using Convolutional Neural Network, by Dalian University of Technology, RWTH Aachen University, and University of Jyväskylä

497 Features + 1D-CNN2020 MDPI Appl. Sci., Over 70 Citations(Sik-Ho Tsang @ Medium)

Phonocardiogram (PCG)/Heart Sound Classification2013…2023[2LSTM+3FC, 3CONV+2FC] [NRC-Net] [Log-MelSpectrum+Modified VGGNet] [CNN+BiGRU][CWT+MFCC+DWT+CNN+MLP] [LSTM U-Net (LU-Net)] [DL Overview] [MFCC + k-NN / RF / ANN / SVM + Grid Search] [Long-Short Term Features (LSTF)] [WST+1D-CNN and CST+2D-CNN Ensemble] [CTENN] [Bispectrum + ViT]

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

- First,
**497 features**were extracted from 8 domains. - Then, these features are fed into the
**1-D convolutional neural network (CNN)**. Considering the class imbalance, the class weights were set in the loss function.

# Outline

**497 Features****1D-CNN****Results**

**1. 497 Features**

**497 features**are extracted in different domains.- (Please skip to Section 2 for quick read.)

## 1.1. Time Domain

- The methods in [7,29] are used to extract the
**features in index 1–16.** **Another four features (index 17–20)**are added. So, 20 features in total were extracted from the time domain.

## 1.2. State Amplitude Domain

- First, the
**absolute values of the amplitude**were normalized. Then, the**ratios**of the absolute amplitude between di erent states were calculated. Furthermore, the**mean and standard deviation**of the ratio were taken. - A total of
**12 amplitude features**were extracted from four states

## 1.3. Energy Domain

- There are two kinds of features in the energy domain — (1) the
**energy ratio of the band-passed signal to the original signal**; and (2) the**energy ratio of one state to another.** **42 frequency bands from 10 Hz to 430 Hz**([10 20] Hz, [20 30] Hz,- [30 40] Hz, …, and [420 430] Hz). So, there were
**42 features**for each of these bands. - First, the
**Butterworth filter**is applied to the raw signals. Then, the energy ratio of each band was calculated

- The energy ratio between any two states is also required as features. If we have
*N*cycles in a PCG recording and each cycle contains*n*discrete time indices, then the “Ratio_energy_state” can be defined as:

- The
**mean**and the**SD**of Ratio_energy_stateS1_cycle in the ith cycle were calculated as**2 features.**The**energy ratios of state S1 to the S2**, systole and diastole states are also obtained respectively. So, for state S1,**8 features**were extracted. - Similarly,
**6, 4 and 2 features for the S2, systole and diastole states**, respectively, are extracted.

## 1.4. Higher-Order Statistics Domain

- The
**skewness**and**kurtosis**of each state (si) in a cycle:

- The
**mean**and**SD**of the skewness and kurtosis of each state are extracted as**2 separate features**. Therefore, there were**16 features**in this domain.

## 1.5. Cepstrum Domain

**By DFT, log, then IDFT, the first 13 cepstral coecients were extracted**from the cepstrum of the new discrete sequence.

- The same operation was performed on the new discrete sequences
- generated by all of the S2, systole and diastole states of a PCG recording. Finally,
**a total of 65 (13 + 13 × 4) features**was obtained.

## 1.6. Frequency Domain

- The mean frequency spectrum of each state over all cycles in a PCG recording. The spectrum values from 20 Hz to 400 Hz with a 5 Hz interval were extracted as features. Therefore, we could obtain
**77 features from each state.** - A total of
**308 (77 × 4) features**were obtained.

## 1.7. Cyclostationarity Domain

**Ting et al. in Reference [37]**have discussed the**degree of****cyclostationarity**, which indicates the**level of signal repetition in a PCG recording.**as the*γ*(*α*)**cycle frequency spectral density (CFSD)**of a PCG recording at cycle frequency andas the CFSD of the PCG recording at the cycle frequency, which is defined by the main peak location of*γ*(*η*)*γ*(*α*). The degree of cyclostationarity is defined as:

**The mean and SD**, based on the degree of cyclostationarity**of all subsequences**, are calculated.**The ratio of the maximum CFSD to the median CFSD**is also calculated.

- Similarly,
**the mean and SD of the peak_sharpness**of all of the subsequences in a PCG recording can be calculated as another 2 separate features.

## 1.8. Entropy Domain

**Sample entropy**and**fuzzy measure entropy**are measured.**2 entropy features**are also extracted in the**cepstrum domain**.

**2. 1D-CNN**

- As shown in Figure 2, the proposed model is composed of
**3 Conv-blocks, a global average pooling (GAP) layer and a classification layer with the sigmoid function.**

# 3. Results

**PhysioNet**- The
**best**outcomes obtained from**4th-fold**are 94%.

- With class weight applied to loss function, performance is improved.