Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network

Abstract

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.

Ocean Observation Data Prediction for Argo Data Quality Control Using Deep Bidirectional LSTM Network

“…In [23], a novel LSTMattention based approach was proposed to predict the travel time and further improve the effectiveness and intelligent of transportation systems. In [24], Bi-LSTM was utilized to noninvasively estimate the right ventricular systolic blood pressure through heart sound signals, and the Bi-LSTM has more effective performance than the conventional LSTM networks.…”

Research process on deep learning methods for heart sounds classification

“…The parameters of these heart sound signals can vary based on the condition of the heart. Notably, there is a substantial divergence between normal and pathological cardiac sounds, as their corresponding PCG signals differ in character-Progress in Medical Devices istics such as magnitude, duration, intensity, spectrum, and uniformity [5].…”

Detection of pulmonary hypertension with six training strategies based on deep learning technology

“…Machine learning is a branch of AI technology, which involves feature extraction, feature selection, statistical analysis, and classification 20–22 . Combine with machine learning algorithm, different feature extraction techniques and classifiers algorithms are used to detect PH disease 23–30 . Recently, some advanced algorithms are applied to classify the different types of heart diseases using heart sounds 31–34 .…”

“…[20][21][22] Combine with machine learning algorithm, different feature extraction techniques and classifiers algorithms are used to detect PH disease. [23][24][25][26][27][28][29][30] Recently, some advanced algorithms are applied to classify the different types of heart diseases using heart sounds. [31][32][33][34] In the area of biomedical engineering, the automatic detection of abnormal heart sounds is considered as an important prerequisite for the diagnosis of cardiovascular diseases.…”