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A survey: from shallow to deep machine learning approaches for blood pressure estimation using biosensors
journal contribution
posted 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 MohaweshOver 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.
History
Publication title
Expert Systems With ApplicationsVolume
197Article number
116788Number
116788Pagination
1-24ISSN
0957-4174Department/School
School of Information and Communication TechnologyPublisher
Pergamon-Elsevier Science LtdPlace of publication
The Boulevard, Langford Lane, Kidlington, Oxford, England, Ox5 1GbRights statement
Copyright 2022 Elsevier Ltd.Repository Status
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