ASSESSMENT AND PREDICTION OF CARDIOVASCULAR STATUS DURING CARDIAC ARREST AND THE POST-RESUSCITATION PERIOD USING SIGNAL PROCESSING AND MACHINE LEARNING
Real-time, short-term analysis of ECG, by using multiple signal processing and machine learning techniques, is used to determine counter shock success in defibrillation. Combinations of measures when used with machine learning algorithms readily predict successful resuscitation, guide therapy and predict complications. In terms of guiding resuscitation, they may serve as indicators and when to provide counter shocks and at what energy levels they should be provided as well as to serve as indicators of when certain drugs should be provided (in addition to their doses). For cardiac arrest, the system is meant to run in real time during all current resuscitation procedures including post-resuscitation care to detect deterioration for guiding care such as therapeutic hypothermia.
