A system configured to classify cardiac rhythms, the system comprising one or more processors, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors, the program instructions comprising: first program instructions to obtain data in form of an RR time interval series of the times between heartbeats of a patient; second program instructions to segment the time series into a plurality of segments; third program instructions to calculate a plurality of parameters corresponding to each of the segments, characterized in that: the parameters including a coefficient of sample entropy, a detrended fluctuation analysis, and a local dynamics score; fourth program instructions to analyze the obtained data and the calculated parameters using a plurality of trained multivariable algorithms for rhythm classification so as to obtain multiple probability estimates of candidate rhythms in form of normal sinus rhythm, atrial fibrillation and sinus rhythm with ectopy, wherein the multivariable algorithms include at least one of neural networks, Bayesian networks, decision tree algorithm, and rough set theory algorithm; and fifth program instructions to synthesize the results of the plurality of multivariable algorithms to obtain a final rhythm classification by using the highest probability estimate among the multiple estimates as the final classification. first program instructions to obtain data in form of an RR time interval series of the times between heartbeats of a patient; second program instructions to segment the time series into a plurality of segments; third program instructions to calculate a plurality of parameters corresponding to each of the segments, characterized in that: the parameters including a coefficient of sample entropy, a detrended fluctuation analysis, and a local dynamics score; fourth program instructions to analyze the obtained data and the calculated parameters using a plurality of trained multivariable algorithms for rhythm classification so as to obtain multiple probability estimates of candidate rhythms in form of normal sinus rhythm, atrial fibrillation and sinus rhythm with ectopy, wherein the multivariable algorithms include at least one of neural networks, Bayesian networks, decision tree algorithm, and rough set theory algorithm; and fifth program instructions to synthesize the results of the plurality of multivariable algorithms to obtain a final rhythm classification by using the highest probability estimate among the multiple estimates as the final classification. the parameters including a coefficient of sample entropy, a detrended fluctuation analysis, and a local dynamics score; fourth program instructions to analyze the obtained data and the calculated parameters using a plurality of trained multivariable algorithms for rhythm classification so as to obtain multiple probability estimates of candidate rhythms in form of normal sinus rhythm, atrial fibrillation and sinus rhythm with ectopy, wherein the multivariable algorithms include at least one of neural networks, Bayesian networks, decision tree algorithm, and rough set theory algorithm; and fifth program instructions to synthesize the results of the plurality of multivariable algorithms to obtain a final rhythm classification by using the highest probability estimate among the multiple estimates as the final classification.