ANESTHESIA DETECTION SYSTEM

Anesthesia is essential in surgical operations, ensuring that patients remain unconscious and free from pain. A crucial aspect of this procedure is the meticulous management of neuromuscular blockade, which, if not monitored correctly, can pose significant risks. Conventional methods, such as the Train-of-Four (TOF) technique, often rely on subjective manual evaluations that lack uniformity. To overcome these issues, this project utilizes machine learning frameworks to accurately forecast levels of neuromuscular blockade by considering variables such as anesthetic concentration, muscle response, and individual patient characteristics. By implementing a neural network model, patients are classified into specific states: recovery, shallow, moderate, or deep blockade. This advancement provides anesthesiologists with real-time insights, ultimately improving patient safety, reducing human error, and enabling quicker, data-driven clinical decisions. The incorporation of sophisticated predictive analytics in anesthesia practices represents a significant advancement towards more accurate and safer surgical care.