Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network
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 usi...
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MDPI AG
2020-08-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/16/5466 |
| _version_ | 1797559774465753088 |
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| author | Miao Wang Hong Tang Tengfei Feng Binbin Guo |
| author_facet | Miao Wang Hong Tang Tengfei Feng Binbin Guo |
| author_sort | Miao Wang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T17:50:06Z |
| format | Article |
| id | doaj.art-a011712294f64393ab258b2eef274ec6 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T17:50:06Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-a011712294f64393ab258b2eef274ec62023-11-20T09:23:58ZengMDPI AGApplied Sciences2076-34172020-08-011016546610.3390/app10165466Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM NetworkMiao Wang0Hong Tang1Tengfei Feng2Binbin Guo3School of Biomedical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Biomedical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Biomedical Engineering, Dalian University of Technology, Dalian 116024, ChinaSchool of Biomedical Engineering, Dalian University of Technology, Dalian 116024, ChinaObjective: 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.https://www.mdpi.com/2076-3417/10/16/5466right ventricular systolic blood pressureheart sound signalbidirectional LSTM (Bi-LSTM) networkcorrective factorsdetection of pulmonary hypertension |
| spellingShingle | Miao Wang Hong Tang Tengfei Feng Binbin Guo Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network Applied Sciences right ventricular systolic blood pressure heart sound signal bidirectional LSTM (Bi-LSTM) network corrective factors detection of pulmonary hypertension |
| title | Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network |
| title_full | Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network |
| title_fullStr | Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network |
| title_full_unstemmed | Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network |
| title_short | Continuous and Noninvasive Estimation of Right Ventricle Systolic Blood Pressure Using Heart Sound Signal by Deep Bidirectional LSTM Network |
| title_sort | continuous and noninvasive estimation of right ventricle systolic blood pressure using heart sound signal by deep bidirectional lstm network |
| topic | right ventricular systolic blood pressure heart sound signal bidirectional LSTM (Bi-LSTM) network corrective factors detection of pulmonary hypertension |
| url | https://www.mdpi.com/2076-3417/10/16/5466 |
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