A synthetic cardiac episode generator for explainable, pathology based action potential using heuristic polynomial signatures

Synthetic generation of biophysical signals caters to diverse applications of test vectors generation, exploration, data augmentation and machine learning training. Arrhythmiator, our contribution is a Python framework to generate synthetically, action potential for explain-ability and explore-ability of pathology (Arrhythmia) influenced cardiac episodes. In this work, we address the problem of large cardiac data-sets need, for deep learning models which are hindered by privacy laws by synthetic generation. We have addressed the problem of explainable computations, multi-scale, by virtue of novel stack sliced polynomial signatures, piece-wise linear and application context driven fitting/filtering in algorithmic framework. To account the problem of lack of standardised pathology specifications we developed PDL (Pathology descriptive language) portal which can be customised. We also addressed the challenge of high turnaround development cycle, inter-operate-ability computation issue by developing a single unified Python framework. To address computational costs, we leverage domain-specific heuristics through sliced state space control algorithms and a constant temporal resolution. We demonstrate the utility of our framework by generating two cases of Tachycardia, one influenced by sodium pump inhibition of spontaneous diastolic depolarisation (SDD) slope and other by ischaemic episode causing maximum diastolic depolarisation (MDP) shift. The concept of the pacemaker hierarchy is encompassed by developing pathology explainable computation as a structure (PECS). We showcase the flexibility and explore-ability of our framework through ease of filter connect profile plug-ins for action potential states upstroke and plateau. We validated the generated waveforms by developing trace and debug algorithms and a reference deviation score which marks the pathology influence. The validator module is a vectored attribute of action potential based on state space control algorithms