Cyberheart-Diagnostics Software Package for Automated Electrocardiogram Analysis Based on Machine Learning Techniques

Abstract

The aim of the study was to develop the Cyberheart-Diagnostics software module, an automated electrocardiogram analysis system being part of the Cyberheart software and hardware complex, and to select machine learning techniques for testing the system based on the comparative analysis of their capabilities. Materials and Methods. The software package was developed using various machine learning techniques working on a large sample of labeled data, i.e. ECG database with known diagnostic conclusions: support-vector machines, decision tree, artificial neural networks, linear and quadratic discriminant analysis, the random subspace method, AdaBoost, random forest, logistic regression (McCulloch-Pitts neuron model). For comparative analysis and evaluation of the obtained results, the Cyberheart-Diagnostics software was tested using open international ECG databases: Arrhythmia Data Set, PhysioNet PTBDB, PhysioNet Competition 2017 as well as our own database comprising 1652 records of a standard 12-lead resting ECG. The ECG records were interpreted by expert physicians who then formed structured medical conclusions considered as reference. Results. In different classes of attributes, the diagnostic accuracy of the Cyberheart-Diagnostics software appeared to be 83.8 to 94.5% as compared to the conclusions of expert doctors-66.3 to 95.1%. Thus, the developed software is comparable with the world analogues in quality of electrocardiogram analysis.

Age Classification in Forensic Medicine Using Machine Learning Techniques

“…In such cases, it is reasonable to use nonlinear data mining with well-marked roundup properties [15]. Modern information technologies (machine learning techniques) are promising [16][17][18].…”