Mendeley
TY  _  JOUR
ID  -  1402092545081
TI  -  Synthetic Photoplethysmogram (PPG) Generation Using Genetic Programming Based Generative Model
JO  -  Journal of Information and Communication Technology
JA  -  ES
LA  -  fa
SN  -  2717-0411
AU  -  Ghasemi Fateme
AU  -  abdali Fardin
AD  -  هیات علمی
AD  -  گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه رازی، کرمانشاه، ایران
AD  -  گروه مهندسی کامپیوتر و فناوری اطلاعات، دانشکده فنی و مهندسی، دانشگاه رازی، کرمانشاه، ایران
Y1  -  2025
PY  -  2025
VL  -  63
IS  -  
SP  -  43
EP  -  54
KW  -  Photoplethysmogram
KW  -   Generative Model
KW  -   Genetic Programming
KW  -   Scalability
KW  -   Mathematical Model.
DO  -  
N2  -  <p style="direction: ltr;">Today, advancements in Information and Communication Technology (ICT), particularly in healthcare and cardiac activity monitoring, have led to increased adoption of Photoplethysmogram (PPG) technology in smart devices and mobile phones. The development of generative models for producing artificial PPG signals faces challenges such as a lack of diversity and constraints in training data. This article employs a Genetic Programming (GP) based approach to introduce a generative model capable of producing PPG data with increased diversity and enhanced accuracy using an initial sample of PPG signals. In contrast to conventional regression, Genetic Programming automates the determination of the mathematical model's structure and compositions. The proposed approach, with a Mean Squared Error (MSE) of 0.0001, Root Mean Square Error (RMSE) of 0.01, and a correlation of 0.999, demonstrates superior performance due to appropriate optimization and acceptable accuracy in generating synthetic PPG data. It outperforms other methods in terms of efficiency and execution capability, especially in resource-constrained environments.</p>
UR  - rimag.ir/en/Article/45081
L1  - rimag.ir/en/Article/Download/45081
ER  -  
