# New Predictive Diagnostic Method for Cardiac Dynamics Based on Probability Distributions

## TL;DR

This paper introduces a new diagnostic method for heart conditions using probability theory and Holter test data to distinguish between normal, chronic, acute, and pacemaker dynamics.

## Contribution

A novel diagnostic support tool based on probability distributions and frequency ranges for cardiac dynamics classification.

## Key findings

- The method achieved perfect sensitivity, specificity, and kappa coefficient in differentiating cardiac dynamics.
- Probability values below 0.3 indicated normal dynamics, while values above 0.3 indicated chronic, acute, or pacemaker dynamics.
- Cumulative probabilities and frequency repetitions varied significantly across diagnostic categories.

## Abstract

Background/Objectives: Probability theory and dynamic systems have enabled the development of diagnostic support tools that simplify Holter evaluation. Method: A study was conducted on 80 Holter tests over 21 h with patients over 21 years old. Four prototypes were selected based on normal, chronic, acute, and pacemaker diagnoses. An induction was created using the heart rate ranges of the prototypes, from 55 to 105, as the general probability space. Probability theory was applied to the frequency repetition ranges of 1000 to 2000 and 2001 to 3000. A blinded study was conducted with the remaining Holter tests, applying the same methodology used for the prototypes. A physical/mathematical induction was performed for the prototypes, and the other Holter tests were analyzed in a blinded study. Results: The results were compared to the predictions of the prototypes, and sensitivity, specificity, and the kappa coefficient were calculated. In the 1000–2000 range, the repetition counts for normal dynamics were 14 to 11, for chronic cases 31 to 21, for acute cases 11 to 9, and for pacemaker dynamics 5 to 4. In the 2001–3000 range, the repetitions for normal dynamics were 3 to 0, for chronic cases 14 to 10, for acute cases 6 to 3, and for pacemaker dynamics 2. The cumulative probabilities loaded for the 1000–2000 range were as follows: normal dynamics, 0.46 to 0.35; chronic dynamics, 0.48 to 0.35; acute cases, 0.6 to 0.5; and pacemaker dynamics, 0.6 to 0.5. In the 2001–3000 range, the cumulative probabilities loaded for normal dynamics were 1 to 0; for chronic cases, 0.7 to 0.54; for acute cases, 0.75 to 0.46; and for pacemaker dynamics, 1. The frequencies observed in the repetition ranges for 1000–2000 were normal, 95 to 55; chronic, 105 to 65; acute, 100 to 75; and pacemaker, 75 to 60. For the 2001–3000 range, the frequencies were normal, 95 to 65; chronic, 85 to 65; acute, 100 to 80; and pacemaker, 65 to 60. The probabilities were less than 0.3 for normal dynamics and greater than 0.3 for chronic, acute, and pacemaker dynamics across different frequency ranges, differentiating the dynamics. Conclusions: The epidemiological study results for sensitivity, specificity, and kappa coefficient were all 1. To conclude, a diagnostic support tool was developed for cardiac dynamics with clinical applications based on the appearance of frequency ranges and probability theory, enabling differentiation of normal, chronic, acute, and pacemaker dynamics.