Brief Review — An Extensive Literature on Heart Sound Classification Using Integrated Signal Processing and Deep Learning Techniques
Comparison Between ML and DL
2 min readJul 6, 2024
An Extensive Literature on Heart Sound Classification Using Integrated Signal Processing and Deep Learning Techniques
Literature on HS Classification, by Karunya Institute of Technology and Sciences
2023 ICSPC (Sik-Ho Tsang @ Medium)Phonocardiogram (PCG)/Heart Sound Classification
2013 … 2023 … [CTENN] [Bispectrum + ViT]
==== My Other Paper Readings Are Also Over Here ====
- This conference paper presents a short literature review of heart sound classification.
Outline
- Signal Processing, Dimensional Reduction Techniques
- Machine Learning, Deep Learning Algorithms
1. Signal Processing, Dimensional Reduction Techniques
- Authors present some of the Signal Processing, Dimensional Reduction Techniques used for heart sound classification.
1.1. Signal Processing
- Mel-Frequency Cepstral Coefficients (MFCCs)
- Discrete Wavelet Transform (DWT)
- Other Signal Features
1.2. Dimensional Reduction
- PCA
- LDA
2. Machine Learning, Deep Learning Algorithms
2.1. Machine Learning (ML)
- Support Vector Machine (SVM)
- Gradient Boosting
- Random Forest (RF)
2.2. Deep Learning (DL)
- Convolutional Neural Network (CNN)
- Recurrent Neural Network (RNN)
- Authors mention that:
- Deep learning has shown promising results in recent years and is often preferred for complex tasks. However, deep learning algorithms often require more computational resources and larger datasets for training.
- When coming to Machine learning, algorithms are typically easier to implement and interpret, and can often achieve good performance with smaller datasets.
