An advanced LAN model based on optimized feature algorithm: Towards hypertension interpretability. (July 2021)

• Record Type: Journal Article
• Authors: Agham, Nishigandha Dnyaneshwar; Chaskar, Uttam M.
• Is Part Of: Biomedical signal processing and control. Volume 68(2021)
• Publication Date: 2021-07
• Subjects: AutoRegressive integrated moving average -- Blood pressure -- Genetic algorithm -- Long short-term memory; Signal processing -- Periodicals; Biomedical engineering -- Periodicals; Signal Processing, Computer-Assisted -- Periodicals; Image Processing, Computer-Assisted -- Periodicals; Biomedical Engineering -- Periodicals; 610.28
• DOI: 10.1016/j.bspc.2021.102760
• Languages: English
• ISSNs: 1746-8094

Abstract

Graphical abstract: Highlights: Cuffless Blood pressure measurement stands in the mainstream of modern healthcare industries. An advanced LAN model achieved significant performance towards hypertension interpretability. The new feature optimization method serves insight of various cardiovascular events responsible for blood pressure variation. Continuous BP measurement with long-term evaluation of the cardiovascular parameter. Abstract: Background: The existing cuffless BP measuring methods still show the inadequacy of the model that utilizes long term dependencies in BP dynamics and come up with the outcome of recurrent calibration. Objective: This study aims to develop a learning-based predictive model for continuous BP measurement with long-term evaluation of the cardiovascular parameter. Methods: Towards hypertension interpretability, the proposed model assists the current as well as the previous status of cardiovascular events based on long short-term memory networks (LSTM) and flexible autoregressive integrated moving average (ARIMA). It is often complicated and challenging to continuously estimate BP from random cardiac events. This paper proposes a new SGFA (Sequential Genetic Feature Algorithm) to tackle the feature optimization problem. Results: The experiment was performed on a MIMIC database containing 2780 datafiles of PPG-BP. The proposed model provides the best performance with root mean square error (RMSE) and mean absolute error (MAE) of 1.17 and 1.04 for SBP, whereas 1.06 and 1.02 for DBP. The proposed method has also been evaluated on hypertensive and hypotensive patients. In hypertension condition, estimated SBP and DBP present a good correlation with the true measurements. MAE and RMSE of the estimated SBP are 0.96 and 1.21, whereas, for DBP, it shows 0.42 and 0.57 respectively. Extensive experimentation results confirm that the proposed method delivers a remarkable performance of BP prediction. Conclusion: The proposed work shows a remarkable BP estimation performance compared to the previous inventive methods and signifies insight of various cardiovascular events responsible for BP variation and hypertension interpretability.