Cluster Computing https://doi.org/10.1007/s10586-017-1602-0 FPGA implementation of modified error normalized LMS adaptive filter for ECG noise removal C. Venkatesan 1 · P. Karthigaikumar 2 · R. Varatharajan 3 Received: 20 October 2017 / Revised: 15 December 2017 / Accepted: 20 December 2017 © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract High frequency noise and channel noise are dominant in wireless ECG monitoring systems which can be modeled as white Gaussian noise. Least mean square (LMS) algorithm based adaptive filters are the preferred choice for white Gaussian noise removal, because they require fewer computations and less amount of power consumption. Though LMS algorithm is simple to implement in real time systems, it is necessary to modify the LMS algorithm to reduce the mean square error for improved filtering performance. In this paper, a delayed error normalized LMS (DENLMS) adaptive filter is studied with pipelined architecture to remove the white Gaussian noise from ECG signal. The pipelined VLSI architecture is utilized to boost the operational speed of adaptive filter by reducing the critical path using delay elements. The performance of pipelined DENLMS algorithm is compared with ENLMS and DNLMS algorithms. The pipelined DENLMS filter increases the speed of operation and reduces power consumption at the cost of increase in area due to the presence of latches. Virtex 5 FPGA XC5LVX330 Field programmable gate array has been utilized as target chip to determine the speed, logic utilization and power consumption. Keywords Adaptive algorithms · Gaussian noise · Field programmable gate arrays · Pipeline processing · Least mean square methods 1 Introduction Electrocardiogram (ECG) signal preprocessing is essen- tial in biomedical applications to remove unwanted noisy components and to enhance the desired feature extraction. High-quality ECG data is required for improved clinical diagnosis and better treatment of cardiac diseases. How- ever, in real situations, the recorded ECG signals are affected by baseline wander, motion artifacts and high frequency noise. High-frequency noises are occurred in ECG signals recordings due to power line interferences, electromyogram (EMG) noise and electrodes. Baseline wander arises due to the fact that there is a possible movement of either patients or recording cables. Moreover, ECG signals are affected by B C. Venkatesan venkatintphd@gmail.com 1 Faculty of Information and Communication Engineering, Anna University, Chennai, Tamilnadu, India 2 Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamilnadu, India 3 Department of Electronics and Communication Engineering, Sri Ramanujar Engineering College, Chennai, India white noise due to poor channel conditions in biotelemetry applications [1]. In view of these interruptions and noises dur- ing acquisition, ECG preprocessing techniques are focused mainly towards noise removal. Several ECG enhancement methodologies have been discussed in the existing woks that are mainly concentrated on suppression of noises such as baseline wander, high frequency noise and white noise using various signal processing tools and algorithms. Some of the techniques are advanced averaging, wavelet transform, non- linear filter banks and adaptive filters [2,3]. In telemedicine applications, transmission of ECG sig- nals over a wireless channel is often affected by noises due to improper channel. The noises are normally modeled as white Gaussian noise [4]. In some applications, white Gaussian noise is considered as a general frequency noise compo- nent which is intentionally included to the uncontaminated ECG signals. Chang and Liu [5] proposed a white Gaussian noise removal technique using Wiener filtering approach [5]. Wiener filter is an optimal filter and is mainly used in place of traditional low pass filters where the input signal power spec- trum and noise spectrum cannot be separated [6]. Extended Kalman filter (EKF) is an alternative filtering approach to 123