Denoising ECG Signals by Using Extended Kalman Filter to Train Multi-Layer Perceptron Neural Network

Mobile Target Tracking is one of the most important applications in Wireless Sensor Networks (WSNs), particularly for surveillance purposes. The tracking accuracy is highly dependent on distance estimation or localization, and so far more works has been done in this aspect. This paper proposes a new energy-saving target tracking scheme with two phases: (i) Mobility Target Tracking and (ii) Target Movement Prediction. At first, the target tracking is attained by Extended Kalman Filter. Following this, the target movement is predicted with the aid of input factors such as Angle of Arrival (AoA) and Received Signal Strength (RSS), thereby the mobile node's optimal movement is predicted. This scenario is considered as the optimization crisis as the prediction of optimal node movement is one of most significant problems in WSN. In order to make the optimal prediction more precise, a new hybrid algorithm named Lion Mutated-Crow Search Algorithm (LM-CS) is introduced. The proposed algorithm combines the concept of Lion Algorithm (LA) and Cuckoo Search algorithm (CS), respectively. To the end, the performance of proposed work is evaluated over other models with respect to convergence analysis, error analysis and so on.

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Preprocessing of the electrocardiogram signal for a patient parameter monitoring system

Dastidar¹,

Panigrahy

2021

Electronic Devices, Circuits, and Systems for Biomedical Applications

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Denoising ECG Signals by Using Extended Kalman Filter to Train Multi-Layer Perceptron Neural Network

Cited by 8 publications

References 11 publications

Novel Approach for Reinforcement the Extraction of ECG Signal for Twin Fetuses Based on Modified BSS

Novel Approach for Reinforcement the Extraction of ECG Signal for Twin Fetuses Based on Modified BSS

Adaptive Mobility Target Tracking with Metaheuristic Aided Target Movement Prediction Scheme in Wireless Sensor Network

Preprocessing of the electrocardiogram signal for a patient parameter monitoring system

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