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.

Item Type:	Refereed Article
Keywords:	deep learning, machine learning, blood pressure estimation, photoplethysmography, PPG
Research Division:	Information and Computing Sciences
Research Group:	Applied computing
Research Field:	Applications in health
Objective Division:	Information and Communication Services
Objective Group:	Information systems, technologies and services
Objective Field:	Artificial intelligence
UTAS Author:	Maqsood, S (Ms Sumbal Maqsood)
UTAS Author:	Xu, S (Dr Shuxiang Xu)
UTAS Author:	Tran, S (Dr Son Tran)
UTAS Author:	Garg, S (Dr Saurabh Garg)
UTAS Author:	Springer, M (Dr Matthew Springer)
UTAS Author:	Mohawesh, R (Mr Rami Mohawesh)
ID Code:	149121
Year Published:	2022
Web of Science® Times Cited:	7
Deposited By:	Information and Communication Technology
Deposited On:	2022-03-10
Last Modified:	2023-01-06
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