Classification of Atrial Tachycardia Types Using Dimensional Transforms of ECG Signals and Machine Learning

Accurate non-invasive diagnoses in the context of cardiac diseases are problems that hitherto remain unresolved. We propose an unsupervised classification of atrial flutter (AFL) using dimensional transforms of ECG signals in high dimensional vector spaces. A mathematical model is used to generate synthetic signals based on clinical AFL signals, and hierarchical clustering analysis and novel machine learning (ML) methods are designed for the un-supervised classification. Metrics and accuracy parameters are created to assess the performance of the model, proving the power of this novel approach for the diagnosis of AFL from ECG using innovative AI algorithms.