Titlebook: Machine Intelligence and Signal Analysis; M. Tanveer,Ram Bilas Pachori Conference proceedings 2019 Springer Nature Singapore Pte Ltd. 2019

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Human Gait State Prediction Using Cellular Automata and Classification Using ELM, two states and has total eight states. We have considered the current and previous states to predict next state. So we have formulated 16 rules using cellular automata, eight rules for each leg. The priority order maintained using the fact that if right leg in swing phase then left leg will be in s

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CA-DE: Hybrid Algorithm Based on Cultural Algorithm and DE,presented to mend the eminence of resolutions, ahead of the individual performance of both the algorithms. We have applied the newly proposed algorithm on a set of six standard benchmark optimization problems to evaluate the performance. The comparative results presented demonstrate that CA-DE has a

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2194-5357 signals using numerous classification techniques. The book is envisioned for researchers and graduate students in Computer Science and Engineering, Electrical Engineering, Applied Mathematics, and Biomedical Signal Processing..978-981-13-0922-9978-981-13-0923-6Series ISSN 2194-5357 Series E-ISSN 2194-5365

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Detecting R-Peaks in Electrocardiogram Signal Using Hilbert Envelope,thod, first, the ECG signals are bandpass filtered to reduce various kinds of noises. Then, the Hilbert envelope of the bandpass filtered ECG signals is used to estimate the approximate locations of R-peaks. These locations are further processed to determine the correct R-peaks in the ECG signal. Th

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Baseline Wander and Power-Line Interference Removal from ECG Signals Using Fourier Decomposition Meproblems of baseline wander and power-line interference. In this paper, we propose a new approach to eliminate such noises from ECG signals using the Fourier decomposition method. Simulation results are presented to show the efficacy of our method over previously used EMD-based methods. The proposed

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Noise Removal from Epileptic EEG signals using Adaptive Filters,. EEG waves are highly vulnerable to diverse forms of noise which pose notable challenges in the analysis of EEG data. In this paper, adaptive filtering techniques, namely, Recursive Least Squares (RLS), Least Mean Squares (LMS), and Shift Moving Average (SMA) filters, were applied to the collected