Automatic detection of nociceptive pain levels using frequency bands from electroencephalographic (EEG) signals
Notice bibliographique
Résumé
Abstract Pain is considered an unpleasant but vital experience for every living being, it is extremely complex and subjective because it is composed of different variables related to their experiences. Their history, biological sex, socio/cultural context, mood, and hormonal changes can affect their perception. Nociceptive pain is more linked to tissue damage or stimulus, and this will always have a reaction that goes from its activation in the nociceptors in the peripheral nerves of the living being to the central nervous system. The most common way to assess pain today is to apply numerical scales or ”How much pain do you feel?” questionnaires, which are usually falsifiable and unreliable. Therefore, this work seeks to make use of biosignals such as Electroencephalography (EEG) to identify and evaluate pain at different levels. This nociceptive pain is generated by applying a laser on the back of the hand, which consists of three different intensities. It has been possible to differentiate between two levels of pain (high pain and low pain) with 83% accuracy using information from the power of frequency bands of the brain signal. Indicating that there are differences in the powers of the frequency bands as pain increases. Author summary Rogelio Sotero Reyes-Galaviz: Mechatronic engineer from the Polytechnic University of Tlaxcala (UPTlax), with a Master of Science degree in Biomedical Science and Technology at the Instituto Nacional de Astrofísica, Óptica y Electrónica (INAOE). He is currently pursuing a PhD in Biomedical Sciences and Technologies at INAOE. His research is focused on pain quantification using electrical brain signals (EEG) and machine learning methods. His lines of interest are Signal Processing, Electroencephalography, Stress, Music Therapy and Pain. ( rogeliosrg@inaoep.mx ) Luis Villaseñor-Pineda: Luis Villaseñor received his PhD degree in Computational Sciences from l’Université Joseph Fourier (now Université Grenoble-Alpes), France, in 1999. He is currently a senior researcher in the Computational Sciences department at the Instituto Nacional de Astrofísica, Óptica y Electŕonica, México, and a member of the Mexican Academy of Sciences, the Mexican Association of Natural Language Processing, and the Mexican System of Researchers (Level II). His research interests focus on human-computer communication using human language as well as different biosignals (speech, brain-signal, etc.). ( villasen@inaoep.mx ) Camilo E. Valderrama: Assistant Professor in the Applied Computer Science department at the University of Winnipeg, specializing in the application of machine learning, statistical models, and signal processing to extract meaningful patterns and support decision-making processes. His research spans diverse areas, including affordable fetal monitoring, reducing redundant laboratory tests in intensive care units, protecting children from unhealthy-food advertising, and validating neuromarketing principles. Prior to this, he completed a two-year postdoctoral fellowship at the University of Calgary. He holds a Ph.D. in Computer Science with a concentration in Biomedical Informatics from Emory University (Atlanta, GA, USA), a Master of Science in Informatics, and a Bachelor of Science in Software Systems Engineering from Universidad Icesi (Cali, Colombia). ( c.valderrama@uwinnipeg.ca )
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».