Mahmoudzadeh, Aysan, Azimi, Iman, Rahmani, Amir M., and Liljeberg, Pasi. Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection. Retrieved from https://par.nsf.gov/biblio/10296390. Procedia Computer Science 184.C Web. doi:10.1016/j.procs.2021.03.025.

Mahmoudzadeh, Aysan, Azimi, Iman, Rahmani, Amir M., & Liljeberg, Pasi. Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection. Procedia Computer Science, 184 (C). Retrieved from https://par.nsf.gov/biblio/10296390. https://doi.org/10.1016/j.procs.2021.03.025

Mahmoudzadeh, Aysan, Azimi, Iman, Rahmani, Amir M., and Liljeberg, Pasi. "Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection". Procedia Computer Science 184 (C). Country unknown/Code not available. https://doi.org/10.1016/j.procs.2021.03.025. https://par.nsf.gov/biblio/10296390.

@article{osti_10296390,
place = {Country unknown/Code not available}, title = {Lightweight Photoplethysmography Quality Assessment for Real-time IoT-based Health Monitoring using Unsupervised Anomaly Detection}, url = {https://par.nsf.gov/biblio/10296390}, DOI = {10.1016/j.procs.2021.03.025}, abstractNote = {}, journal = {Procedia Computer Science}, volume = {184}, number = {C}, author = {Mahmoudzadeh, Aysan and Azimi, Iman and Rahmani, Amir M. and Liljeberg, Pasi}, editor = {null} }