## 1 Introduction
In order to compare the performance of our algorithm with others, we selected two common features of the four envelopes used in previous studies [3; 13; 15] and the short-time Fourier transform (STFT) for testing. These four envelopes have been used widely to test the performance of different algorithms, and STFT is a traditional method for spectrum extraction. In addition, we tested a one-dimensional convolutional neural network (1D-CNN) for extracting features by autonomously combining neural networks and compared 1D-CNN with the envelope and spectrum methods. As shown in Table 1, the most common methods for feature classification included threshold approaches, logistic regression, K-nearest neighbor, recurrent neural networks (RNNs), and CNNs. In previous studies, the algorithms have been used to cluster the time intervals between peaks rather than the features of peaks to distinguish the systole and diastole. The current mainstream algorithms directly classified the features of each time point into heart sound states. Due to the complexity of the actual heart sound signal, in order to improve the classification ability of each time point, the feature extraction step should integrate the signal information for one period or multiple periods of heart sounds, which had a large number of features, so the feature classification algorithm will be complicated. At present, CNN and RNN algorithms are the mainstream algorithms for HSS research. The strong classification capacity of deep learning algorithms is explained by the strong ability of deep neural networks to fit complex functions [19], which is more effective for feature classification with a large number of features. For example, [PERSON] et al. [15] used a bidirectional RNN for classification, which can integrate the long-distance information before and after the classified time \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Feature Extraction** & **Feature Classification** & **State Annotation** \\ \hline
[9] & Power spectral density functions & Artificial neural network & Corresponding time intervals \\  & Short-time frequency amplifier & (ANN) & vary more for diastole \\
[10] & and envelope smoothed with a & Statistical duration-based & Given directly after S1 and S2 \\  & Gaussian smoothing filter & assessment methodology & are classified \\
[11] & Optimized S-transform & Three-nearest neighbor classifier based on singular value decomposition & Given directly after S1 and S2 \\  & Ensemble empirical mode & Threshold based on kurtosis & Larger time intervals are the \\
[12] & decomposition (EEMD) combined with kurtosis features & features of peaks & diastolic period \\  & Homomorphic envelope, Hilbert & Logistic regression & Extended Viterbi algorithm \\
[13] & envelope, wavelet envelope, and power spectral density envelope & K-means algorithm and ANN & Given directly after S1 and S2 \\
[14] & (MFCCs) & Short-time Fourier transform & \\  & (STFT), homomorphic envelope, & Bidirectional recurrent & \\
[15] & Hilbert envelope, wavelet envelope, power spectral density & neural networks & - \\  & Homomorphic envelope, Hilbert & Convolutional neural & \\
[3] & envelope, wavelet envelope, and power spectral density envelope & networks based on U-Net & Sequential max temporal \\  & & [16] & modeling and several different \\
[17] & Hilbert transform and STFT & Threshold method for peaks & \\  & Homomorphic envelope, Hilbert & \\
[18] & envelope, wavelet envelope, power spectral density envelope, and MFCCs & Bidirectional long short-term memory with attention & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Cont. point, where the method can obtain excellent direct classification results, but this method was applied to process the whole heart sound signal and the algorithm could only be calculated after acquiring the complete signal. The long short-term memory (LSTM) network is a classical algorithm for RNN and it can allow classification in real time.