A survey: From shallow to deep machine learning approaches for blood pressure estimation using biosensors
作者：
Highlights：
• Artificial intelligence-based blood pressure estimation research using photoplethysmography (PPG) with their outcomes and significant findings.
• A concise comparison of studies related to ECG and PPG.
• Traditional features extraction techniques from PPG signals.
• Experimental analysis for comparison of traditional features extraction techniques using PPG signals.
• Future prospective and critical Implications.
摘要
•Artificial intelligence-based blood pressure estimation research using photoplethysmography (PPG) with their outcomes and significant findings.•A concise comparison of studies related to ECG and PPG.•Traditional features extraction techniques from PPG signals.•Experimental analysis for comparison of traditional features extraction techniques using PPG signals.•Future prospective and critical Implications.
论文关键词：
论文评审过程：Received 14 October 2020, Revised 21 June 2021, Accepted 26 February 2022, Available online 2 March 2022, Version of Record 9 March 2022.
论文官网地址：https://doi.org/10.1016/j.eswa.2022.116788
