We propose using adaptive filters for removing such artefacts.These filters use reference signals providing information correlated to components in the artefacts.It would improve the heart condition and thus increase the chance of successful defibrillation outcome.
CR appears to decrease the likelihood for readmission at 180 days.
STO rates were higher at 180 days for CR participants, perhaps indicating a need for increased monitoring without rehospitalization.
We propose to use a pattern recognition framework for the decision support system.
In contrast to most earlier work with one-dimensional features, this allows analysis of multivariate information.
Predictors for readmission and STO were varied, based on timeframe.
Being smoke-free, non-hypertensive, married, and not having a myocardial infarction (MI) at admission were significant predictors for enrolling in CR.
One of the possible reasons for this is that a large part of the valuable therapy time is wasted in futile attempts to restart the heart by electrical defibrillations.
Using this time to provide precordial compressions and ventilations to establish and keep up an artificial supply of oxygenated blood would serve the patient better.
These data-driven machine learning algorithms have produced good results in many applications, image analysis being one of them.
Today, many image analysis tasks in the medical field are done manually, taking valuable time and effort from professionals.