Brief Review — Supervised threshold-based heart sound classification algorithm
Extension of MFCC+CNN CinC’16 With Optimized λ
2 min readJan 20, 2024
Supervised threshold-based heart sound classification algorithm
MFCC+CNN+Optimizied λ, by Guangdong University of Technology, Guangdong Institute of Intelligent Manufacturing
2018 J. Physiol. Meas., Over 20 Citations (Sik-Ho Tsang @ Medium)Heart Sound Classification
2013 … 2023 [2LSTM+3FC, 3CONV+2FC] [NRC-Net] [Log-MelSpectrum+Modified VGGNet] [CNN+BiGRU]
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- MFCC is extracted, CNN in MFCC+CNN CinC’16 [19] is used.
- λ is introduced and optimized by maximizing the prediction accuracy of the training instances.
Outline
- Proposed Method
- Results
1. Proposed Method
- For features and model, authors use MFCC and CNN in MFCC+CNN CinC’16. (Please read MFCC+CNN CinC’16 for more details.)
The heart sound instance is considered to be classified correctly, depending on the proportion of the total number of segments predicted accurately in M, and a threshold λ (0<λ<1).
- Each abnormal and normal sample is divided into N^i_M (i = 1, 2, …, N1) segments and P^i_M (i = 1, 2, …, N2) segments.
- If the i-th abnormal sample is predicted correctly, it means the value of
- exceeds the λ.
- Therefore, the value of:
- exceeds (1-λ), indicating the i-th normal sample classified predicted correctly.
λ is continued tuning iteratively through gradient ascent/descent.
- The iterations needed for seeking the optimal λ are 64, 317, 26, 121, 215, 112, 348, 65, 108 and 50 respectively.
2. Results
- The detailed experimental results of cross-validation are given in Tab.2.
- The relevant confusion matrix of the results from the proposed algorithm is listed in Tab.3.
The proposed algorithm acquires a better MAcc.
