Fourier Transform and Autoregressive HRV Features in Prediction and Classification of Breast Cancer

Abstract

ABSTRACT The decision making in diagnosis of breast cancer (BC) at the earliest is the necessity to decrease the mortality rate. The 5-minute electrocardiogram of 114 BC subjects and 13 age-matched healthy controls were recorded and spectral features of heart rate variability (HRV) were calculated. Fast Fourier transform (FFT) and autoregressive (AR) spectral methods were compared to analyse the frequency domain of HRV. Lavenberg–Marquardt algorithm-based artificial neural network (ANN) and support vector machine (SVM) classified all the spectral measures with maximum accuracy of 54.2% and 100%, respectively. ANOVA with Tukey’s HSD Posthoc test has also been employed to evaluate the significant variations in different parameters due to BC with respect to control subjects with the help of statistical analysis. The FFT and AR results were found almost similar. Clinicians can achieve an insight of the severity of the disease as per the findings of spectral measures as per Eastern Cooperative Oncology Group Performance Status and improvise the quality of their patients.