Prediction of Medically Drug-Induced Arrhythmias (Torsades de Pointes, Ventricular Tachycardia, and Ventricular Fibrillation) in Rabbit Model up to One Hour Before Their Onset Using Computational Method Based on Entropy Measure and Machine Learning

Background: In general, this methodical paper describes a well-documented application of one complexity measure and various machine learning methods to solve a specific problem in biosignal processing: predictions of ventricular tachycardia & fibrillation and Torsades de Pointes arrhythmia. The methodology part provides a concise introduction to all used methods and is accompanied by a sufficient citation apparatus. Once the presented methodology gets explained, it is easy to apply it to many other research areas. Currently, allopathic medicine is facing one of the biggest challenges and transitions that it has been going through during its history. Deeper understanding of human physiology will enable medicine to reach better understanding of human body functioning. Simultaneously it will allow to design novel, so-far-inaccessible, complex, dynamically changing therapies based on this knowledge. We address the following general question: "Are there existing mathematical tools enabling us to predict changes in physiological functions of human bodies at least minutes or even hours before they start to operate?" This general question is studied on a specific, simple model of the rabbit heart subjected to by medically-induced drug insults that are leading to the drug-induced Torsades de Pointes (TdP) arrhythmia. This class of models can improve our ability to assess the current condition of the heart and even to predict its future condition and disease development within the next minutes and even hours. This can eventually lead to substantial improvement of the out-of-bed cardiology care. Methods: Electrocardiograph (ECG) recordings were acquired—in a different research project—from anesthetized rabbits (ketamine and xylazine) that were subjected to infusion of gradually increasing doses of arrhythmia-inducing methoxamine and dofetilide drugs. Subsequently, ECG curves were evaluated using the permutation entropy for different lag values, where the lag is the evaluation parameter. Lag is defining the distance between neighboring measuring points. Computed entropy curves were processed by machine learning (ML) techniques: Random Forest (RF), Support Vector Machine (SVM), Logistic Regression (LR), k-nearest neighbors (k-NN), Ensemble Learning (EL), and others. ML methods performed classification of arrhythmia above the evaluated segments of permutation entropy curves. Results: A possibility to predict drug-induced TdP arrhythmia up to one hour before its onset was confirmed in a small study of 37 rabbits with specificity and sensitivity achieving 93% (for important statistical features [measurable properties]). It was demonstrated that animals can be divided into two distinct groups: susceptible and resistant to arrhythmia. It was shown that animals can be classified using just five-minute segments prior to and after the application of methoxamine (this drug can be used in human medicine, unlike dofetilide). The drawback of the study is the too low a number of measured animals. Conclusion: This pilot study demonstrated a relatively high probability that the prediction of the onset of TdP arrhythmia is possible tens of minutes or even hours before its actual onset with sensitivity and specificity around 93%. Those findings must be confirmed in wider animal studies and on human ECGs. Another human study got similar results using deep learning methods. Presented software predicting of arrhythmia has a big potential in human medicine, because it can be applied in hospital monitors, implantable defibrillators, and wearable electronics guarding the health condition of patients. A small set of tested animals does not allow their subdivision into sufficiently big subgroups (TdP and Normal). Groups are too small and asymmetric. It is recommended to test achieved results on different, larger ECG databases of animal models and on large human ECG databases.