TY - GEN
T1 - Rectangular Nature of Megahertz Bursts Naturally Eliminates Operation Theater’s Sinusoidal Noises Affecting Anesthetized Subjects
T2 - 4th International Conference on Trends in Electronics and Health Informatics, TEHI 2024
AU - Banishetty, Shanthi
AU - Katiyar, Sarika
AU - Tipu, Saifullah
AU - Singh, Pushpendra
AU - Dutta, Tanusree
AU - Ranjan, Rupesh
AU - Hameroff, Stuart
AU - Bandyopadhyay, Anirban
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.
PY - 2026
Y1 - 2026
N2 - This study presents a comprehensive framework to isolate and validate brain signals in hospital environments, where interference from medical devices, radio transmissions, and ambient noise complicates measurement. We focus on 6–26 MHz rectangular bursts repeated at 50–60 kHz, measured from anesthetized patients’ foreheads that appear during unconsciousness and disappeared upon waking, as confirmed by Dodecanogram or DDG, and BIS metrics from 40 gastroenteric patients under propofol anesthesia. Our noise-signal interaction simulator integrates advanced signal processing, statistical modeling, and multidimensional analyses to differentiate genuine neural activity from artifacts. Central to this effort is a specialized database capturing intricate noise–signal interactions, which informs an automated filter for isolating pure brain signals. By mapping signal variability across transitions into and out of unconsciousness, we find that pulsed signal can generate EEG like signals by interacting with brown noise. Experimental validations confirm that the rectangular nature of megahertz bursts saves them against diverse sinusoidal interference sources. Our simulator-database combo is potent for reliable diagnoses and neuroscientific research, where even minor noise artifacts can obscure crucial physiological processes. This unified framework thus sets a new standard for noise-robust brainwave analysis, paving the way for advanced clinical tools and deeper scientific understanding of the brain’s consciousness profile.
AB - This study presents a comprehensive framework to isolate and validate brain signals in hospital environments, where interference from medical devices, radio transmissions, and ambient noise complicates measurement. We focus on 6–26 MHz rectangular bursts repeated at 50–60 kHz, measured from anesthetized patients’ foreheads that appear during unconsciousness and disappeared upon waking, as confirmed by Dodecanogram or DDG, and BIS metrics from 40 gastroenteric patients under propofol anesthesia. Our noise-signal interaction simulator integrates advanced signal processing, statistical modeling, and multidimensional analyses to differentiate genuine neural activity from artifacts. Central to this effort is a specialized database capturing intricate noise–signal interactions, which informs an automated filter for isolating pure brain signals. By mapping signal variability across transitions into and out of unconsciousness, we find that pulsed signal can generate EEG like signals by interacting with brown noise. Experimental validations confirm that the rectangular nature of megahertz bursts saves them against diverse sinusoidal interference sources. Our simulator-database combo is potent for reliable diagnoses and neuroscientific research, where even minor noise artifacts can obscure crucial physiological processes. This unified framework thus sets a new standard for noise-robust brainwave analysis, paving the way for advanced clinical tools and deeper scientific understanding of the brain’s consciousness profile.
KW - Anesthesia
KW - Brain signal
KW - Clinical study
KW - Consciousness
KW - EEG
KW - High frequency
UR - https://www.scopus.com/pages/publications/105039478382
UR - https://www.scopus.com/pages/publications/105039478382#tab=citedBy
U2 - 10.1007/978-981-95-6013-4_23
DO - 10.1007/978-981-95-6013-4_23
M3 - Conference contribution
AN - SCOPUS:105039478382
SN - 9789819560127
T3 - Lecture Notes in Networks and Systems
SP - 319
EP - 345
BT - Proceedings of Trends in Electronics and Health Informatics - TEHI 2024
A2 - Mahmud, Mufti
A2 - Kaiser, M. Shamim
A2 - Bandyopadhyay, Anirban
A2 - Ray, Kanad
A2 - Bundele, Mahesh
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 28 December 2024 through 29 December 2024
ER -