Key result
Nonlinear heart rate variability analysis using support vector machines and artificial neural networks predicted performance status in pulmonary metastases patients with 100% and 82.25% accuracy.
Why the study?
Does nonlinear HRV analysis combined with machine learning predict performance status in patients with pulmonary metastases?
Case-Control (n=54)
Does nonlinear HRV analysis combined with machine learning predict performance status in patients with pulmonary metastases?
Nonlinear HRV analysis combined with machine learning algorithms like SVM can accurately predict performance status in patients with pulmonary metastases.
Cancer causes chronic stress and is associated with impaired autonomic nervous system (ANS). Heart rate variability (HRV) has been suggested to be an important tool in the identification and prediction of performance status (PS) in cancer. Lead II surface electrocardiogram (ECG) was recorded from 24 pulmonary metastases (PM) subjects and 30 healthy controls for nonlinear HRV analysis. Artificial neural network (ANN) and support vector machine (SVM) were applied for the prediction analysis. Analysis of variance (ANOVA) along with post-hoc Tukey’s HSD test was conducted using statistical R, 64-bit, v.3.3.2, at [Formula: see text]. The obtained results suggested lower HRV that increases with cancer severity from the Eastern Cooperative Oncology Group (ECOG)1 PS to ECOG4 PS. ANOVA results stated that approximate entropy (ApEn) ([Formula: see text]-[Formula: see text], [Formula: see text]), detrended fluctuation analysis (DFA) [Formula: see text] ([Formula: see text]-[Formula: see text], [Formula: see text]) and correlation dimension (CD) ([Formula: see text]-[Formula: see text], [Formula: see text]) were significant. The 13 nonlinear features were fed to ANN and SVM to obtain 82.25% and 100% accuracies, respectively. Nonlinear HRV analysis has given promising results in the prediction of diagnosis of PS in PM patients. These inputs would be very useful for clinicians to diagnose PS in their cancer patients and improve their quality of living.
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Shukla et al. (2018) conducted a case-control in Pulmonary metastases (n=54). Pulmonary metastases vs. Healthy controls was evaluated on Prediction accuracy of performance status. Nonlinear heart rate variability analysis using support vector machines and artificial neural networks predicted performance status in pulmonary metastases patients with 100% and 82.25% accuracy.
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