Figure 3 | Scientific Reports

Figure 3

From: Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians

Figure 3

Flow diagram illustrating the training of the acoustic models.

The second heart sounds were extracted from the recordings as shown in Fig. 1. The process described in Fig. 2 obtained the Mel-Frequency Cepstral Coefficients (MFCC) feature vectors. The MFCC feature vectors for the subjects with PH (mean PA pressure ≥25 mmHg) were combined into one matrix, and the feature vectors for the subjects with normal PA pressures (mean PA pressure <25 mmHg) were combined into another matrix. A Gaussian Mixture Model (GMM) is fitted to the feature vector matrices, resulting in one model for all subjects with PH and one for all subjects without PH.

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