Systolic and diastolic multiclass classification of PPG signals using neural network with random weight

• Main Authors: Tan, Jiun Hann, Noor liza, Simon, Asrul, Adam
• Format: Conference or Workshop Item
• Language: English; English
• Published: IEEE 2019
• Subjects: QA76 Computer software
• Online Access: http://umpir.ump.edu.my/id/eprint/27704/1/43.%20Systolic%20and%20diastolic%20multiclass%20classification%20of%20PPG.pdf; http://umpir.ump.edu.my/id/eprint/27704/2/43.1%20Systolic%20and%20diastolic%20multiclass%20classification%20of%20PPG.pdf; http://umpir.ump.edu.my/id/eprint/27704/; https://doi.org/10.1109/SSCI44817.2019.9002876

Photoplethysmography (PPG) signals can be defined as a type of signal which obtained through a noninvasive optical method that contains information of the cardiovascular system such as arterial blood pressure, tissue perfusion, heart rate and respiratory rate. Due to its noninvasive characteristics...