Key result
Dendritic neural regression outperforms state-of-the-art machine learning for BP estimation, achieving grade A accuracy.
Why the study?
Traditional cuff-based blood pressure measurement causes patient discomfort and cannot monitor continuously, prompting the need for noninvasive cuffless continuous estimation methods.
Does the dendritic neural regression method improve the accuracy of noninvasive cuffless blood pressure estimation compared to conventional machine learning techniques?
Does the dendritic neural regression method improve the accuracy of noninvasive cuffless blood pressure estimation compared to conventional machine learning techniques?
The novel dendritic neural regression method provides highly accurate, noninvasive, cuffless continuous blood pressure estimation, achieving Grade A standards.
May support cuffless BP research; leaves open prospective validation before clinical adoption.
Blood pressure (BP) is one of the most important indicators of health. BP that is too high or too low causes varying degrees of diseases, such as renal impairment, cerebrovascular incidents, and cardiovascular diseases. Since traditional cuff-based BP measurement techniques have the drawbacks of patient discomfort and the impossibility of continuous BP monitoring, noninvasive cuffless continuous BP measurement has become a popular topic. The common noninvasive approach uses machine-learning (ML) algorithms to estimate BP by using the features extracted from simultaneous photoplethysmogram (PPG) and electrocardiogram (ECG) signals, such as the pulse transit time and pulse wave velocity. This study investigates the BP estimation performance of the novel dendritic neural regression (DNR) method proposed by us. Unlike conventional neural networks, DNR utilizes the multiplication operator as the excitation function in each dendritic branch, inspired by biological neuron phenomena, and can effectively capture nonlinear relationships between distinct input features. In addition, AMSGrad is used as the optimization algorithm to further enhance the dendritic neural model's performance. The experimental results show that by being fed a combination of the raw features extracted from the ECG and PPG signals and the components of the BP mathematical models, DNR can increase the accuracy of systolic BP, diastolic BP, and mean arterial pressure estimation significantly, which are superior to the state-of-the-art ML techniques. According to the British Hypertension Society protocol, DNR achieves a grade of A for the long-term BP estimation. Considering its architectural simplicity and powerful performance, the proposed method can be regarded as a reliable tool for estimating long-term continuous BP in a noninvasive cuffless way.
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Ji et al. (2022) studied Blood pressure estimation. Dendritic neural regression (DNR) vs. State-of-the-art machine-learning techniques was evaluated on Accuracy of systolic BP, diastolic BP, and mean arterial pressure estimation. Dendritic neural regression improved the accuracy of blood pressure estimation compared to state-of-the-art machine learning techniques, achieving a British Hypertension Society grade of A.
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