A method of analysis of biopotential signal data , the method comprising: Receiving, at an analysis system , a biopotential signal data set associated with the subject, said biopotential signal data set being associated with at least one biopotential signal collected from one or more electrical leads; pre-processing said bio-potential signal data set to extract variables by generating, via a processor, a first fractional derivative signal data set and a second fractional derivative signal data set by numerically performing, for each of the first fractional derivative signal data set and the second fraction derivative data set one or more fractional derivative operations on the biopotential signal data set in a frequency domain and converting a result of the one or more fractional derivative operations to a time domain signal data set, wherein each of the first generated fractional derivative data set and the second generated fractional derivative signal data set comprises a same length and a same sampling frequency as the biopotential signal data set ; and generating, via the processor, a three-dimensional space data set wherein each corresponding value of the biopotential signal data set , the first fractional derivative signal data set , and the second fractional derivative signal data set forms a three-dimensional point in said space data set , wherein geometric features and/or dynamical properties of the three-dimensional space are used as variables representative of the subject in a machine learning operation to assess parameters associated with a presence of one or more diagnosable pathologies of the subject. Receiving, at an analysis system , a biopotential signal data set associated with the subject, said biopotential signal data set being associated with at least one biopotential signal collected from one or more electrical leads; pre-processing said bio-potential signal data set to extract variables by generating, via a processor, a first fractional derivative signal data set and a second fractional derivative signal data set by numerically performing, for each of the first fractional derivative signal data set and the second fraction derivative data set one or more fractional derivative operations on the biopotential signal data set in a frequency domain and converting a result of the one or more fractional derivative operations to a time domain signal data set, wherein each of the first generated fractional derivative data set and the second generated fractional derivative signal data set comprises a same length and a same sampling frequency as the biopotential signal data set ; and generating, via the processor, a three-dimensional space data set wherein each corresponding value of the biopotential signal data set , the first fractional derivative signal data set , and the second fractional derivative signal data set forms a three-dimensional point in said space data set , wherein geometric features and/or dynamical properties of the three-dimensional space are used as variables representative of the subject in a machine learning operation to assess parameters associated with a presence of one or more diagnosable pathologies of the subject. <EOS>