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Objective: Timely monitoring right ventricular systolic blood pressure (RVSBP) is helpful in the early detection of pulmonary hypertension (PH). However, it is not easy to monitor RVSBP directly. The objective of this paper is to develop a deep learning technique for RVSBP noninvasive estimation using heart sound (HS) signals supported by (electrocardiography) ECG signals without complex features extraction. Methods: Five beagle dog subjects were used. The medicine U-44069 was injected into the subjects to induce a wide range of RVSBP variation. The blood pressure in right ventricle, ECG of lead I and HS signals were recorded simultaneously. Thirty-two records were collected. The relations between RVSBP and cyclic HS signals were modeled by the Bidirectional Long Short-Term Memory (Bi-LSTM) network. Results: The mean absolute error (MAE) ± standard deviation (SD) inside record was 1.85 ± 1.82 mmHg. It was 4.37 ± 2.49 mmHg across record but within subject. The corrective factors were added after training the Bi-LSTM network across subjects. Finally, the MAE ± SD from 12.46 ± 6.56 mmHg dropped to 6.37 ± 4.90 mmHg across subjects. Significance: Our work was the first to apply the Bi-LSTM network to build relations between the HS signal and RVSBP. This work suggested a noninvasive and continuous RVSBP estimation using the HS signal supported by the ECG signal by deep learning architecture without the need of healthcare professionals.

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The weighting method of the traditional fixed combination forecasting model is the only criterion considered to improve accuracy, which has some limitations. In order to improve the comprehensive prediction performance of the combined model, hierarchical structure of the combined model by selecting some parameters which can reflect the performance of the model (including prediction accuracy, robustness, sensitivity, and the amount of fitting data) is established and a kind of multiple factor and multiple criteria weighting method of combination forecasting model is put forward. Based on SVR model, GM (1, 1) model, and ARIMA model, a combination forecasting model based on Improved Analytic Hierarchy Process (AHP) is constructed and applied to a foundation pit. The experimental results show that the combined forecasting model based on improved AHP are better than the single model in precision and robustness; it also has good effect in sensitivity, which has more comprehensive prediction performance than the single models, and has good engineering and practical value.