ABSTRACT
En este trabajo se presenta el diseño e implementación de un electrodo capacitivo de no contacto para la detección de biopotenciales en el cuerpo humano. Se presentan los circuitos eléctricos, el criterio de selección del amplificador operacional en base al análisis de la resistencia óptima de ruido, se describe el montaje físico, se presentan las señales obtenidas con este y la evaluación de su desempeño en base a la relación señal a ruido S/N. Se muestra el desempeño de diversos amplificadores operacionales y se demuestra la versatilidad del electrodo para detectar diversos biopotenciales realizando ajustes en los valores de dos componentes eléctricos del electrodo.
In this work the design and implementation of a capacitive non-contact electrode for detecting biopotentials in the human body is presented. Electrical circuits, the selection criteria of the operational amplifier based on the analysis of the optimal noise resistance are presented, the physical assembly is described and the signals obtained and evaluation of its performance are presented based on the signal-to-noise ratio S/N. We show the performance of several operational amplifiers and it is shown the versatility of the electrode to detect several biopotentials making adjustments to the values of two electrical components of the electrode.
ABSTRACT
The objective measurement of subjective, multi-dimensionally experienced pain is a problem for which there has not been an adequate solution. Although verbal methods (e.g., pain scales and questionnaires) are commonly used to measure clinical pain, they tend to lack objectivity, reliability, or validity when applied to mentally impaired individuals. Biopotential and behavioral parameters may represent a solution. Such coding systems already exist, but they are either very costly or time-consuming or have not been sufficiently evaluated. In this context, we collected a database of biopotentials to advance an automated pain recognition system, determine its theoretical testing quality, and optimize its performance. For this purpose, participants were subjected to painful heat stimuli under controlled conditions. One hundred thirty-five features were extracted from the mathematical groupings of amplitude, frequency, stationarity, entropy, linearity, and variability. The following features were chosen as the most selective: (1) electromyography corrugator peak to peak, (2) corrugator shannon entropy, and (3) heart rate variability slope RR. Individual-specific calibration allows the adjustment of feature patterns, resulting in significantly more accurate pain detection rates. The objective measurement of pain in patients will provide valuable information for the clinical team, which may aid the objective assessment of treatment (e.g., effectiveness of drugs for pain reduction, information on surgical indication, and quality of care provided to patients).