Time-domain heart rate variability-based computer-aided prognosis of lung cancer

Incidence of lung cancer (LC) is increasing day by day with exposure to smoke, radiation, and chemicals; LC is one of the leading causes of death. The major difficulty in treatment was delayed diagnosis. This study aims to propose a time-domain heart rate variability (HRV) feature-based automated system in LC prediction and its staging.

HRV analysis was done using recorded electrocardiographic signal from 104 LC participants and 30 control volunteers. Artificial neural network (ANN) and support vector machine (SVM) were implemented on HRV time-domain features for early prognosis of the disorder. Statistical significance of HRV parameters was tested, and graphical user interface (GUI) was also implemented.

It was revealed that progression of cancer causes low HRV. An accuracy of 89.64% and 100% was obtained with ANN and SVM, respectively, in automated cancer prediction. Statistical analysis suggested the significance of data at P < 0.05 between different performance statuses among patients.

The severity of LC alters the sympathovagal balance through autonomic dysfunction. HRV analysis with an expert system was found useful for the early diagnosis of the disease, and thus, a noninvasive technique is of prognostic importance in classifying LC stages. The GUI designed for clinicians can help them to diagnose the Eastern Cooperative Oncology Group performance status scale of future patients.