Digital Assistant for Ventilators Using SVM Algorithm and Speech Recognition

Abstract

Many healthcare assists have been developed to help clinicians in treating patients. A single monitor for managing an instrument seems very expensive. The transferring of data from the single setup also requires high communication costs. The personal assistant developed has many drawbacks due to the changes in prosodic cues according to people’s language slang and the trouble in analyzing the paralinguistic information. The network data and energy consumption required for the transfer of information from the healthcare devices become quite large. The project involves an easy transmission module and assisting method to avoid these issues. This project is involved in assisting a practitioner, physician, or respiratory therapist in the proper handling of a ventilator, in accordance with the patient’s health state and parameters. When providing ventilation, it is important to notice the ventilator readings such as iPEEP, Ppeak, Pplat (developed values in the patient’s respiratory system) which are the response of the patient etc. On observation of these readings, the parameters such as e-PEEP, VT, RR, and FiO2 (values to be set by the clinician) have to be adjusted for better ventilation and for the purpose of weaning off the patient in a short period. The preliminary work involved is the data acquisition and logging. The SVM algorithm has been developed with many data points as the parameters obtained from data. The protruding idea is to analyze the patient’s age, gender, weight, disorder, type of surgery, and its duration. Thus, the value of the parameter that has to be adjusted can be determined intricately with the protruding idea of digital assist.