Toward a diagnostic CART model for Ischemic heart disease and idiopathic dilated cardiomyopathy based on heart rate total variability
| Autor: | Agostino Accardo, Luca Restivo, Miloš Ajčević, Aleksandar Miladinović, Katerina Iscra, Giulia Silveri, Marco Merlo, Gianfranco Sinagra |
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| Přispěvatelé: | Accardo, Agostino, Restivo, Luca, Ajčević, Miloš, Miladinović, Aleksandar, Iscra, Katerina, Silveri, Giulia, Merlo, Marco, Sinagra, Gianfranco |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Cardiomyopathy
Dilated Interpretable machine learning Ischemic heart disease Biomedical Engineering Myocardial Ischemia Dilated cardiomyopathy Stroke Volume Computer-aided diagnosis Computer-aided diagnosi Ventricular Function Left Computer Science Applications Heart Rate Humans Heart rate variability |
| Popis: | Diagnosis of etiology in early-stage ischemic heart disease (IHD) and dilated cardiomyopathy (DCM) patients may be challenging. We aimed at investigating, by means of classification and regression tree (CART) modeling, the predictive power of heart rate variability (HRV) features together with clinical parameters to support the diagnosis in the early stage of IHD and DCM. The study included 263 IHD and 181 DCM patients, as well as 689 healthy subjects. A 24 h Holter monitoring was used and linear and non-linear HRV parameters were extracted considering both normal and ectopic beats (heart rate total variability signal). We used a CART algorithm to produce classification models based on HRV together with relevant clinical (age, sex, and left ventricular ejection fraction, LVEF) features. Among HRV parameters, MeanRR, SDNN, pNN50, LF, LF/HF, LFn, FD, Beta exp were selected by the CART algorithm and included in the produced models. The model based on pNN50, FD, sex, age, and LVEF features presented the highest accuracy (73.3%). The proposed approach based on HRV parameters, age, sex, and LVEF features highlighted the possibility to produce clinically interpretable models capable to differentiate IHD, DCM, and healthy subjects with accuracy which is clinically relevant in first steps of the IHD and DCM diagnostic process. Graphical abstract |
| Databáze: | OpenAIRE |
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