TY - JOUR
T1 - Sleepiness should be reinvestigated through the lens of clinical neurophysiology
T2 - A mixed expertal and big-data Natural Language Processing approach
AU - Martin, Vincent P.
AU - Gauld, Christophe
AU - Taillard, Jacques
AU - Peter-Derex, Laure
AU - Lopez, Régis
AU - Micoulaud-Franchi, Jean Arthur
N1 - Publisher Copyright:
© 2023 Elsevier Masson SAS
PY - 2024/4
Y1 - 2024/4
N2 - Historically, the field of sleep medicine has revolved around electrophysiological tools. However, the use of these tools as a neurophysiological method of investigation seems to be underrepresented today, from both international recommendations and sleep centers, in contrast to behavioral and psychometric tools. The aim of this article is to combine a data-driven approach and neurophysiological and sleep medicine expertise to confirm or refute the hypothesis that neurophysiology has declined in favor of behavioral or self-reported dimensions in sleep medicine for the investigation of sleepiness, despite the use of electrophysiological tools. Using Natural Language Processing methods, we analyzed the abstracts of the 18,370 articles indexed by PubMed containing the terms ‘sleepiness’ or ‘sleepy’ in the title, abstract, or keywords. For this purpose, we examined these abstracts using two methods: a lexical network, enabling the identification of concepts (neurophysiological or clinical) related to sleepiness in these articles and their interconnections; furthermore, we analyzed the temporal evolution of these concepts to extract historical trends. These results confirm the hypothesis that neurophysiology has declined in favor of behavioral or self-reported dimensions in sleep medicine for the investigation of sleepiness. In order to bring sleepiness measurements closer to brain functioning and to reintroduce neurophysiology into sleep medicine, we discuss two strategies: the first is reanalyzing electrophysiological signals collected during the standard sleep electrophysiological test; the second takes advantage of the current trend towards dimensional models of sleepiness to situate clinical neurophysiology at the heart of the redefinition of sleepiness.
AB - Historically, the field of sleep medicine has revolved around electrophysiological tools. However, the use of these tools as a neurophysiological method of investigation seems to be underrepresented today, from both international recommendations and sleep centers, in contrast to behavioral and psychometric tools. The aim of this article is to combine a data-driven approach and neurophysiological and sleep medicine expertise to confirm or refute the hypothesis that neurophysiology has declined in favor of behavioral or self-reported dimensions in sleep medicine for the investigation of sleepiness, despite the use of electrophysiological tools. Using Natural Language Processing methods, we analyzed the abstracts of the 18,370 articles indexed by PubMed containing the terms ‘sleepiness’ or ‘sleepy’ in the title, abstract, or keywords. For this purpose, we examined these abstracts using two methods: a lexical network, enabling the identification of concepts (neurophysiological or clinical) related to sleepiness in these articles and their interconnections; furthermore, we analyzed the temporal evolution of these concepts to extract historical trends. These results confirm the hypothesis that neurophysiology has declined in favor of behavioral or self-reported dimensions in sleep medicine for the investigation of sleepiness. In order to bring sleepiness measurements closer to brain functioning and to reintroduce neurophysiology into sleep medicine, we discuss two strategies: the first is reanalyzing electrophysiological signals collected during the standard sleep electrophysiological test; the second takes advantage of the current trend towards dimensional models of sleepiness to situate clinical neurophysiology at the heart of the redefinition of sleepiness.
KW - Natural language processing
KW - Neurophysiology
KW - Sleep medicine
KW - Sleepiness
KW - Text mining
UR - http://www.scopus.com/inward/record.url?scp=85185759225&partnerID=8YFLogxK
UR - https://pubmed.ncbi.nlm.nih.gov/38401240
U2 - 10.1016/j.neucli.2023.102937
DO - 10.1016/j.neucli.2023.102937
M3 - Review article
C2 - 38401240
AN - SCOPUS:85185759225
SN - 0987-7053
VL - 54
JO - Neurophysiologie Clinique
JF - Neurophysiologie Clinique
IS - 2
M1 - 102937
ER -