Machine Learning-Based ECG Signal Classification for Enhanced Early Detection of Doxorubicin-Induced Cardiotoxicity in Rats

Abstract

Cardiotoxicity, which leads to irreversible myocardial damage, is a major adverse effect associated with chemotherapy. Electrocardiogram (ECG) is an inexpensive, rapid, and simple tool that may provide valuable diagnostic information pertinent to cardiotoxicity. An automatic interpretation and classification of the ECG signals by machine learning algorithms is considered superior to human interpretation of the ECG which may not be able to early detect subtle alterations in the ECG and vary according to the experience of the specialist. The present work aimed at using different machine learning algorithms to classify ECG signals recorded from doxorubicin-injected rats. Rats were divided into four groups and each group was intraperitoneally injected with different cumulative doses of doxorubicin (0, 6, 12, and 18 mg/kg). ECG signal classification depended on multiple features that were extracted from the recorded signals under different conditions. K nearest-neighbors’ algorithm achieved higher classification accuracy (99.83%) than random forest (99.56%), decision tree (99.54%), artificial neural network (99.50%), and support vector machine (99.38%). Furthermore, the dose-dependent cardiotoxicity was validated via a histopathological examination of the left ventricle that indicated significant pathological alterations in the cardiac tissue. The present findings emphasized the potential of the machine learning-based enhanced detection of cardiotoxicity and validated the dose-dependent toxicity of doxorubicin in the cardiac left ventricle. This approach might be applicable clinically to avoid cardiotoxicity in chemotherapy-treated patients.