Brief Review — Multi-classification neural network model for detection of abnormal heartbeat audio signals
MFCC+LSTM for Heart Sound Classification
Multi-classification neural network model for detection of abnormal heartbeat audio signals
MFCC+LSTM, by University of Management and Technology, National College of Business Administration and Economics Lahore
2022 JBEA (Sik-Ho Tsang @ Medium)Heart Sound Classification
2013 … 2022 [CirCor Dataset] [CNN-LSTM] [DsaNet] [Modified Xception] [Improved MFCC+Modified ResNet] [Learnable Features + VGGNet/EfficientNet] [DWT + SVM] 2023 [2LSTM+3FC, 3CONV+2FC] [NRC-Net]
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- Mel frequency cepstrum coefficient (MFCC) is applied to extract the dominant features, and a bandpass filter is used to remove the noise.
- RNN using LSTM is proposed for classification.
Outline
- Dataset, Preprocessing & MFCC
- Proposed LSTM Model
- Results
1. Dataset, Preprocessing & MFCC
1.1. Dataset
1.2. Preprocessing
- Mel frequency cepstrum coefficient (MFCC) is extracted.
- The power spectrogram of the first 5 s of the heartbeat signals is shown above.
- The downsampling technique reduces the sampling frequency of each heartbeat audio file to the sizes of 20,000 Hz and 300 Hz frame rate for PASCAL and PhysioNet challenge databases, respectively.
- In addition, heartbeat signals have normalized by removing noise using a bandpass filter, and then the zero-padding process has applied.
- Using an 8 × 8 low pass filter, the sampling transforms a 50 kHz sampling frame rate to an 800 Hz sound signal frequency for the PASCAL Challenge dataset and 300 Hz for the PhysioNet dataset.
- The low pass filter allows low-frequency signals to pass easily and is rated at a cutoff frequency of 1.6% of the sampling frame rate.
- (Please read the paper for more details.)
2. Proposed LSTM Model
The designed model consists of multiple layers like LSTM, Dropout, Dense, and Softmax layers, as above. The cross-entropy loss is used as the loss function.
3. Results
3.1. ML Technique Comparisons
Table 4: On PASCAL, the proposed deep learning algorithm attained a better classification accuracy of 99.71%, specificity of 99.3%, the sensitivity of 98.6%, and 98.9% of f1-score as compared to the MLP as well as traditional ML classifiers.
Table 5: On the PhysioNet dataset, the overall accuracy of 98.7%, specificity of 99%, sensitivity of 98.5%, and f1-score of 98.8% were gained by the proposed model
3.2. SOTA Comparisons
The proposed RNN (LSTM) model achieved the best classification accuracy in terms of different parameters like accuracy, sensitivity, specificity, and f1-score.
