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| Assessment of Pulmonary Artery Pressure and Pulmonary Capillary Wedge Pressure Based on Cardio-Electrical and-Mechanical Signals |
| Zheng Quanzhong1#, Wang Xingyao2#, Li Jianqing1#, Liu Chengyu1#, Yang Chenxi1#* |
1(State Key Laboratory of Digital Medical Engineering, School of Instrument Science and Engineering, Southeast University, Nanjing 210096, China)
2(Institute of High Performance Computing, A*STAR, 1 Fusionopolis Way 138632, Singapore) |
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Abstract Monitoring patients′ hemodynamics and providing timely interventions can significantly enhance the treatment outcomes for heart failure. Recent research has demonstrated the potential of seismocardiogram (SCG) and electrocardiogram (ECG) in heart failure management. To enable more patients to monitor their hemodynamic changes outside the hospital for prompt treatment, this study attempted to utilize cardio-electrical and -mechanical signals to assess pulmonary artery pressure (PAP) and pulmonary capillary wedge pressure (PCWP). The study proposed a prediction method for PAP and PCWP based on multi-scale feature fusion within a multi-task learning framework. This method employed dilated convolutions of varying scales to extract features from individual heartbeat SCG and ECG signals at different scales for fusion and encoding. Subsequently, global information of the encoded data was embedded to adjust the weights of various components, and finally, the encoded signals were decoded to predict PAP and PCWP. Training and validation on data from 66 subjects showed that the MAE for predicting PAP and PCWP were 4.31 and 3.20 mmHg, respectively with corresponding R2 scores of 0.736 and 0.771. The experimental results indicated that the proposed method achieved non-invasive prediction of PAP and PCWP, presenting broad prospects for hemodynamic monitoring out of the clinic.
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Received: 04 November 2024
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