Hybrid IIR/FIR Wavelet Filter Banks for ECG Signal Denoising

Abstract

ElectroCardioGram (ECG) signals are usually corrupted with various types of noise/artifacts such as baseline wander and muscle contraction artifacts which degrade the signal quality and might lead to misdiagnosis of the patient. The wavelet denoising technique is widely studied in the artifact removal literature which employs conventional Finite Impulse Response (FIR) wavelet filter banks for decomposing, thresholding and reconstructing the noisy signal to obtain high fidelity and clean ECG signal. However, the use of high order FIR wavelet filters increases the hardware complexity and cost of the system. This paper presents novel hybrid Infinite Impulse Response (IIR)/FIR Discrete Wavelet Transform (DWT) filter banks that can be employed in ambulatory health monitoring applications for denoising purposes. The proposed systems are evaluated and compared to the conventional FIR based DWT systems in terms of the computational complexity as well as the denoising performance. For this purpose, raw ECG data from MIT-BIH arrhythmia database are contaminated with synthetic noise and denoised with the aforementioned filter banks. The results from 100 Monte Carlo simulations demonstrated that the proposed filter banks provide better denoising performance with fewer arithmetic operations than those reported in the open literature.

An Adaptive ECG Noise Removal Process Based on Empirical Mode Decomposition (EMD)

“…To the signals, addition of three virtual noises, namely, electromyogram (EMG) noise, white Gaussian noise, and main line interference at numerous SNR decibel (dB) levels ranging from 0 to 25 at 5 dB steps, is done. Then, a comparison of the put forward work's performance with those of the existing ECG denoising techniques, namely, EMD-based technique [ 23 ], NML filter [ 28 ], FIR [ 17 ], and PCA-based filter [ 30 ], is done.…”

“…Several studies have contributed many reviews to propose the formulation of techniques for computerised ECG denoising. Such developed methods are primarily based on deep learning techniques [14,15], deep recurrent neural networks (RNN) [16], filter banks [17], time-frequency techniques [18][19][20], discrete wavelet transform (DWT) filtering [21,22], empirical mode decomposition (EMD) [23][24][25][26], impulse response (FIR) filter [17,27], nonlocal mean (NLM) filter [28], and principle component analysis (PCA) [29,30].…”

“…Nevertheless, FIR-based filtering methods [ 17 ] can enable one to eliminate the noise frequencies which are beyond the variety of frequency of ECG signals. Furthermore, these techniques do not retain the ECG components of low frequency (P and T waves).…”

Hardware Implementation of Audio Watermarking Based on DWT Transform

“…The vital discrete wavelet transform (DWT) is frequently refined by a convolution-based filter implementation using the FIR-filters for doing its transform [16]. FIR filters are applicable for improving the execution of the DWT hardware design [17]. Since a lifting structures have points of interest over a convolution-based regarding computation memory usage and complexity, more consideration is paid to the liftingbased approach.…”

Linearized Kalman Filter aided Wavelet Transform with Adaptive Thresholding Methodfor ECG signal

“…From the designed filter banks, the input ECG signals are decomposed into six sub bands which are later processed through the feature extraction and classification phase. Recently, Eminaga, Coskun, and Kale [35] focused on motion artifact removal from the ECG signals and developed FIR wavelet filter bank based method. The conventional FIR filtering methods suffer from the computational complexity issues hence, in this work hybrid approach of FIR/IIR is presented along with DWT filtering.…”