Background An algorithm with complexity measures, nonlinear dynamics, and neural networks were developed to detect and classify the anesthetic deep (AD). It has been shown that extreme anesthetic depth has been correlated with an increased risk of mortality. Likewise, intraoperative awareness has been reported in the anesthetic drugs under dosage. Methods Our artificial intelligence classifier algorithm was developed using a Complexity Brainwave Index (CBI) and a heart rate variability parameter (HRVP). Data from 60 patients (25 men and 35 women), adults between 18 and 65 years of age undergoing surgical procedures (ASA I–II) were collected. The classifier was designed using multi‐layer feedforward neural networks with a hyperbolic activation function using the patient data set and their anesthesia records. Results The CBI by itself showed a better prediction probability with Pk of 0.935 when compared with Datex‐Ohmeda index in which State Entropy and Response Entropy had a Pk of 0.884 and 0.899 respectively; also, our CBI showed a performance of 99% of the awake state and 93.3% for deep anesthesia state. Additionally, when combining the CBI and the HRVP, the C‐Statistic was 99% for the awake state, 87.41% for light anesthesia, 82,46% for general anesthesia, and 93.33% for deep anesthesia. Conclusions Our results demonstrate that special biological signal filtering and a machine learning algorithm may be used to classify AD during a surgical procedure on ASA I–II patients, assisting anesthesiologists and clinicians in decision making. Support or Funding Information University of La Sabana
Nowadays, finite element analysis techniques are employed are used to reduce costs in the manufacturing process of sports prostheses. This study particularly focuses on the finite element analysis of a design for a transtibial prosthesis of a paralympic cyclist, in which integrated the biomechanics of an athlete with amputation in both legs below the knee with two prostheses categorized before the Union Cycling International (UCI) with a disability of degree C-3, considering the characteristics of the terrain and the dynamic model. The analysis by means of finite elements aims to evaluate the static and dynamic behavior of the proposed design when subjected to a competition in the track-cycling category. As a result of this analysis, mechanical aspects such as: static forces, buckling, frequency, fatigue, free fall, impact and aerodynamics can be evaluated, allowing to verify that the design of the proposed transtibial prosthesis meets an suitable aerodynamic profile and its mechanical characteristics to be used in a high performance Paralympic cycling competition.
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