Automated Neural Network Based Classification of HRV and ECG Signals of Smokers: A Preliminary Study Suraj Kumar Nayak, Ipsita Panda, Biswajeet Champaty, Niraj Bagh, Kunal Pal and D.N. Tibarewala Abstract Smoking of cigarettes has been reported to alter the cardiac electro- physiology by modulating the autonomic nervous system. A preliminary investi- gation of the heart rate variability (HRV) parameters suggested sympathetic predominance in smokers. An in-depth analysis of the time domain and wavelet processed ECG signals indicated that the automated neural networks (ANNs) were able to classify the signals with an accuracy of ≥85 %. This suggested that smoking not only modulates the functioning of the autonomic nervous system but is also capable of modulating the cardiac conduction pathway. Keywords Smokers Á Heart rate variability Á Autonomic nervous system Á Automated neural network 1 Introduction Electrocardiogram (ECG) is the electrical potential which is associated with the functioning of the heart. The potential is initiated from the sino-atrial (SA) node. The functioning of SA node is controlled by the Autonomic Nervous System (ANS). ANS consists of two major subsystems, namely, parasympathetic and sympathetic nervous systems. The subsystems of ANS control the pacing of the SA node. ANS try to maintain the heart rate of a person by either increasing or decreasing the activities of the parasympathetic and sympathetic nervous systems. Due to this reason, there is a variation in the timing of the subsequent heart beats. S.K. Nayak Á I. Panda Á B. Champaty Á N. Bagh Á K. Pal (&) Department of Biotechnology and Medical Engineering, NIT-Rourkela, Odisha 769008, India e-mail: pal.kunal@yahoo.com D.N. Tibarewala School of Bioscience and Engineering, Jadavpur University, Kolkata 700032, India © Springer India 2015 S. Gupta et al. (eds.), Advancements of Medical Electronics, Lecture Notes in Bioengineering, DOI 10.1007/978-81-322-2256-9_25 271