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A survey: from shallow to deep machine learning approaches for blood pressure estimation using biosensors
journal contributionposted on 2023-05-21, 06:16 authored by Sumbal MaqsoodSumbal Maqsood, Shuxiang XuShuxiang Xu, Son TranSon Tran, Saurabh GargSaurabh Garg, Matthew SpringerMatthew Springer, Karunanithi, M, Rami Mohawesh
Over the past two decades, machine learning systems have been proliferating in the healthcare industry domains, such as digital health, fitness tracking, patient monitoring, and disease diagnostics. In addition to this, with technological advancement, physiological sensors paired with artificial intelligence have acquired people’s attention because of their multifarious advantages. Such sensors are predominantly inexpensive, portable, easy to use and can help measure health parameters continuously and non-invasively using artificial intelligence. Technologies, such as PPG (Photoplethysmography) and ECG (Electrocardiography), are two promising techniques with immense potential that can track cardiovascular health with significant impact. In this survey paper, we aim to analyse, summarise, and compare the state-of-the-art methods for machine learning-based blood pressure estimation in a continuous, cuffless, and non-invasive manner by PPG biosignals. This survey divides the research work into two machine learning categories: shallow learning and deep learning. PPG feature extraction techniques and datasets are also presented in this paper. Additionally, a concise comparative analysis of PPG and ECG has been provided from the literature. Moreover, to compare different state-of-the-art traditional feature extraction techniques using PPG biosignals, we applied several machine learning algorithms to predict hypertension and heart rate estimation. Finally, we conclude by summarising critical implications and propose some promising future perspectives that will lead to advancements in this domain.
Publication titleExpert Systems With Applications
Department/SchoolSchool of Information and Communication Technology
PublisherPergamon-Elsevier Science Ltd
Place of publicationThe Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1Gb
Rights statementCopyright 2022 Elsevier Ltd.