Zobrazeno 1 - 5
of 5
pro vyhledávání: '"Payongkit Lakhan"'
Autor:
Nat Dilokthanakul, Payongkit Lakhan, Pitshaporn Leelaarporn, Nannapas Banluesombatkul, Theerawit Wilaiprasitporn, Pichayoot Ouppaphan, Nattapong Jaimchariyatam, Huy Phan, Ekapol Chuangsuwanich, Busarakum Chaitusaney, Wei Chen
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 25:1949-1963
Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians. Deep learning approaches have been introduced in order to challenge the automatic sleep stage classification conundrum. However, the difficult
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a6b4b95630339d4a5338da64b25ba2e7
https://doi.org/10.1109/jbhi.2020.3037693
https://doi.org/10.1109/jbhi.2020.3037693
Autor:
Pitshaporn Leelaarporn, Tanut Choksatchawathi, Wei Chen, Phantharach Natnithikarat, Kamonwan Thanontip, Tinnakit Udsa, Patcharapol Wachiraphan, Subhas Chandra Mukhopadhyay, Thitikorn Kaewlee, Rattanaphon Chaisaen, Rawipreeya Laosirirat, Payongkit Lakhan, Theerawit Wilaiprasitporn
Publikováno v:
IEEE Sensors Journal. 21:10369-10391
This comprehensive review mainly analyzes and summarizes the recently published works on IEEExplore in sensor-driven smart living contexts. We have gathered over 150 research papers, especially in the past five years. We categorize them into four maj
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6267fbe70e330d0ed891693e55379c99
https://doi.org/10.1109/jsen.2021.3059304
https://doi.org/10.1109/jsen.2021.3059304
Autor:
Subhas Chandra Mukhopadhyay, Payongkit Lakhan, Nattee Niparnan, Theerasarn Pianpanit, Maytus Piriyajitakonkij, Pitsharponrn Leelaarporn, Theerawit Wilaiprasitporn, Nakorn Kumchaiseemak, Supasorn Suwajanakorn, Patchanon Warin
Publikováno v:
IEEE journal of biomedical and health informatics. 25(4)
Recognizing movements during sleep is crucial for the monitoring of patients with sleep disorders, and the utilization of ultra-wideband (UWB) radar for the classification of human sleep postures has not been explored widely. This study investigates
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::99e2ae0f83f931b39aba436809f9a39c
https://pubmed.ncbi.nlm.nih.gov/32960771
https://pubmed.ncbi.nlm.nih.gov/32960771
Publikováno v:
TENCON
Dramatic raising of Deep Learning (DL) approach and its capability in biomedical applications lead us to explore the advantages of using DL for sleep Apnea-Hypopnea severity classification. To reduce the complexity of clinical diagnosis using Polysom
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::74938e5a20dff61e51294fc3ceef4977
https://doi.org/10.1109/tencon.2018.8650491
https://doi.org/10.1109/tencon.2018.8650491
Autor:
Vongsagon Changniam, Pitshaporn Leelaarporn, Nannapas Banluesombatkul, Ekkarat Boonchieng, Ratwade Dhithijaiyratn, Theerawit Wilaiprasitporn, Supanida Hompoonsup, Payongkit Lakhan
For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets has been generated with the use of diverse emoti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::56c647e98268d4e08b13582a9aaca237
