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

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.

KEYWORDS:

• Artificial neural network
• Autoregressive
• Breast cancer
• Fast Fourier transform
• Heart rate variability
• Support vector machine

Acknowledgements

Authors are grateful to Dr. Rajesh Singh (Professor and Head), Dr. Seema, Dr. Richa Madhavi and Dr. Dinesh Sinha (Assistant Professor) of Indira Gandhi Institute of Medical Sciences, Cancer Centre, Patna, India. Also, authors express their gratitude to medical oncologist Dr. Shreeniwas Raut in HMRI Paras Hospital, Patna, for his clinical inputs and permitting for data collection in the hospital. Authors are also thankful to Dr. Rakesh Kumar Sinha (Professor, Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, India) for his technical inputs for the work.

Additional information

Notes on contributors

Reema Shyamsunder Shukla

Reema S Shukla is pursuing her PhD from Bio-Engineering Department, Birla Institute of Technology, Mesra, Ranchi, Jharkhand. She completed her MTech (Electronics Department) from MPSTME, NMIMS University, Mumbai, and BE from Biomedical Department, Thadomal Shahani College of Engineering, Mumbai, Maharashtra. She had worked previously in several academic and corporate organisations.

Yogender Aggarwal

Yogender Aggarwal is working as an assistant professor in Bioengineering Department, Birla Institute of Technology, Mesra, Ranchi, Jharkhand. He completed his Doctorate from BIT Mesra in 2011, MSc in biomedical instrumentation from BIT Mesra in 2005. He had completed his BASc in instrumentation (Hons) from Delhi University in year 2002. Email: [email protected]