Heart sound signal is a kind of physiological signal with nonlinear and nonstationary features. In order to improve the accuracy and efficiency of the phonocardiogram (PCG) classification, a new method was proposed by means of support vector machine (SVM) in which the complete ensemble empirical modal decomposition with adaptive noise (CEEMDAN) permutation entropy was as the eigenvector of heart sound signal. Firstly, the PCG was decomposed by CEEMDAN into a number of intrinsic mode functions (IMFs) from high to low frequency. Secondly, the IMFs were sifted according to the correlation coefficient, energy factor and signal-to-noise ratio. Then the instantaneous frequency was extracted by Hilbert transform, and its permutation entropy was constituted into eigenvector. Finally, the accuracy of the method was verified by using a hundred PCG samples selected from the 2016 PhysioNet/CinC Challenge. The results showed that the accuracy rate of the proposed method could reach up to 87%. In comparison with the traditional EMD and EEMD permutation entropy methods, the accuracy rate was increased by 18%–24%, which demonstrates the efficiency of the proposed method.
Simulation of the human biological lung is a crucial method for medical professionals to learn and practice the use of new pulmonary interventional diagnostic and therapeutic devices. The study on ventilation effects of the simulation under positive pressure ventilation mode provide valuable guidance for clinical ventilation treatment. This study focused on establishing an electrical simulation ventilation model, which aims to address the complexities in parameter configuration and slow display of air pressure and airflow waveforms in simulating the human biological lung under positive pressure ventilation mode. A simulated ventilation experiment was conducted under pressure-regulated volume control (PRVC) positive pressure ventilation mode, and the resulting ventilation waveform was compared with that of normal adults. The experimental findings indicated that the average error of the main reference index moisture value was 9.8% under PRVC positive pressure ventilation mode, effectively simulating the ventilatory effect observed in normal adults. So the established electrical simulation ventilation model is feasible, and provides a foundation for further research on the simulation of human biological lung positive pressure ventilation experimental platform.