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Electrocardiogram (ECG) is considered as the important diagnostic tests in medical field for detecting the cardiac anomalies. But, the ECG signals are polluted with numerous noise from power line intrusion, muscle noise, baseline wander, motion artifacts, low frequency noise signals, high frequency noise signals and T-wave, which automatically affects the QRS profile. The existing method provides the result in lesser accuracy with higher rate of error detection. To overcome these issues, QRS detector using modified maximum mean minimum (MoMaMeMi) filter optimized with mayfly optimization algorithm (QRS-MoMaMeMi-MOA) is proposed in this paper for less computational cost along with resource requirements. The proposed filter design consists of two phases for detecting QRS detector, such as filtering process associated to the enhancement and detection phase. Initially, the ECG data are taken from MIT/BIH arrhythmia dataset (MIT-AD). For eradicating the baseline wander in ECG data, MaMeMi filter is used. For expanding the performance of the modified MaMeMi filter, filter parameters, such as $\sigma$ and $\mathrm{\Delta }$ are optimized by MOA to accomplish the best values and measure the performance of the whole QRS detector. For high frequency noise suppression in ECG data, the range function, noise subtractors, modified triangular detector are used. Then, heart beat detection can be done with the help of adaptive thresholding technique. The proposed filter design is carried out in MATLAB and implemented on field programmable gate arrays (FPGAs). The proposed QRS-MoMaMeMi-MOA filter design had 0.93%, 0.12% and 0.19% higher accuracy and 89.32%, 50% and 62% low detection error rate, compared to the existing filters, like Kalman filtering based adaptive threshold algorithm for QRS complex detection (QRS-KF-ATA), QRS detection of ECG signal utilizing hybrid derivative with MaMeMi filter by efficiently removing the baseline wander (QRS-HD-MaMeMi), and knowledge-based QRS detection operated by cascade of moving average filters (QRS-CAF). Then, the device utilization of the proposed FPGA implementation of the QRS-MoMaMeMi-MOA filter provides 95.556% and 71.428% lower power usage compared with the existing algorithms, like Kalman filtering based adaptive threshold algorithm for QRS complex detection in FPGA (FPGA-QRS-KF-ATA), and efficient architecture for QRS detection in FPGA utilizing integer Haar wavelet transform (FPGA-QRS-IHWT).

Electrocardiogram (ECG) signals might be affected by various artifacts and noises that have biological and external sources. Baseline wander (BW) is a low-frequency artifact that may be caused by breathing, body movements and loose sensor contact. In this paper, a novel method based on empirical mode decomposition (EMD) for removal of baseline noise from ECG is presented. When compared to other EMD-based methods, the novelty of this research is to reach the optimized number of decomposed levels for ECG BW de-noising using mean power frequency (MPF), while the reduction of processing time is considered. To evaluate the performance of the proposed method, a fifth-order Butterworth high pass filtering (BHPF) with cut-off frequency at 0.5Hz and wavelet approach are applied. Three performance indices, signal-to-noise ratio (SNR), mean square error (MSE) and correlation coefficient (CC), between pure and filtered signals have been utilized for qualification of presented techniques. Results suggest that the EMD-based method outperforms the other filtering method.

ECG signals play a vital role in the diagnosis of cardiovascular conditions. However, they often suffer from the effects of various noise sources, including baseline wandering, respiratory artifact noise, power line interference and electrode motion artifacts. To overcome these challenges, it is imperative to implement low-frequency signal noise reduction strategies. Such strategies aim to significantly improve the quality of ECG signals, thus promoting more accurate and reliable diagnosis of cardiovascular disorders. This paper conducts a comparative analysis to assess the effectiveness of commonly used filtering and wavelet techniques in reducing Baseline Wander (BW) noise within ECG signals generated by the influence of breathing or electrode movements. It is common to observe the selection and evaluation of only one particular technique in the existing literature. In contrast, this study aims to provide a comprehensive comparative analysis, providing insight into the performance and relative merits of different techniques. Our research uses both filtering and Discrete Wavelet Transform (DWT) techniques in baseline noise removal. In this context, a reference point is established utilizing noise-free signals and a meticulous investigation of the wavelet-based approach that most effectively eliminates the resulting noise is provided. Subsequently, we assess the reference input and output signal via Signal-to-Noise Ratio (SNR) and Kolmogorov–Smirnov statistical test measurements. The most important contribution of this work to the scientific community resides in the comprehensive examination of IIR/FIR-based and wavelet method-based filtering methods capable of yielding the highest SNR levels across various ECG signals with various types of BW noise. Additionally, the effectiveness of the Chebychev-II filter in BW noise removal is highlighted. Our study was conducted using the MATLAB platform and code command lines were shared to facilitate the reproduction of our study by other researchers. It is considered that this study will be an important reference in the selection of effective techniques for removing BW noise within ECG signals.