Comparison of different electrocardiogram signal power line denoising methods based on SNR improvement

Amiri, Mina; Afzali, Maryam; Vahdat, Bijan Vosoughi

doi:10.1109/icbme.2012.6519677

Cited by 7 publications

(3 citation statements)

References 7 publications

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“…The result shows a reduction in noise of −18 dB. To eradicate power line noise from ECG signals, the discrete Fourier transform founded algorithm, IIR, FIR, Kalman wavelet, and in addition, advanced-order statistical filtering techniques were applied by Amiri et al [ 15 ]. It has been determined that FIR filtering and IIR filtering improve SNR the most.…”

Section: Existing Methodologymentioning

confidence: 99%

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia

Wadhwani

2022

Journal of Healthcare Engineering

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Cardiovascular health and training success can be assessed using electrocardiogram (ECG) data. For over a quarter of a century, an individual’s resting heart rate is varying more. As a result, it has become the subject of inquiry and reveals the intricate relationship between the human body and its environment. The autonomic nervous system has impact on blood flow system based on the rate of heartbeats. However, heart rate variation (HRV) characteristics analysis throughout the time period has lack of physical activity information. In the presence of patient movement, ECG signal is suffering from hard artefacts. Time-varying HRV parameters can be derived from low-frequency (LF) and high-frequency (HF) domains of the correct frequency. However, sometimes it is critical to ensuring accurate detection of the R-peak position. The proposed ROIIR (reduced-order IIR) offers 8.8% improvement in peak-to-peak swing than earlier IIR filter. We present an advanced filtering algorithm that is used for R-peak detection.

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Section: Existing Methodologymentioning

confidence: 99%

Stability and Phase Response Analysis of Optimum Reduced-Order IIR Filter Designs for ECG R-Peak Detection

Amhia

Wadhwani

2022

Journal of Healthcare Engineering

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“…In the ECG denoising task, traditional research mainly includes a IIR/FIR filter (Amiri et al 2012), and an adaptive filter (AlMahamdy and Riley 2014). In addition, there are many methods based on time-domain signal decomposition techniques, such as the discrete wavelet transform (Jenkal et al 2016), empirical mode decomposition (Pal and Mitra 2012) and variational mode decomposition (Smruthy and Suchetha 2017), or a combination of the above methods (Haritha et al 2016, Dwivedi et al 2021.…”

Section: Introductionmentioning

confidence: 99%

A lightweight U-net for ECG denoising using knowledge distillation

et al. 2022

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Objective: Electrocardiogram (ECG) signals are easily polluted by various noises, which are likely to have adverse effects on subsequent interpretations. Research on model lightweighting can promote the practical application of deep learning-based ECG denoising methods in real-time processing. Approach: Firstly, grouped convolution and conventional convolution are combined to replace the continuous conventional convolution in the model, the depthwise convolution with stride is used to compress the feature map in the encoder modules. Secondly, additional identity connections and local maximum and minimum enhancement module are designed, which can retain the detailed information and characteristic waveform in the ECG waveform while effectively denoising. Finally, we develop knowledge distillation in the experiments, which further improves the ECG denoising performance without increasing the model complexity. The ground-truth ECG is from The China Physiological Signal Challenge (CPSC) 2018, and the noise signal is from the MIT-BIH Noise Stress Test Database (NSTDB). We evaluate denoising performance by the signal-to-noise ratio (SNR), the root mean square error (RMSE) and the pearson correlation coefficient (P). Using floating point of operations (FLOPs) and Parameters to calculate computational complexity. Main Results: Different data generation processes are used to conduct experiments: Group1, Group2 and Group3. The results show that the proposed model (ULde-net) can improve SNRs by 10.30 dB, 12.16 dB and 12.61 dB; reduce RMSEs by 9.88×10-2, 20.63×10-2 and 15.25×10-2; increase Ps by 14.77×10-2, 27.74×10-2 and 21.32×10-2. Moreover, the denoising performance after knowledge distillation is further improved. The ULde-net has parameters of 6.9 K and FLOPs of 6.6 M, which are much smaller than the compared models. Significance: We designed a lightweight model, but also retain the adequate ECG denoising performance. We believe that this method can be successfully applied to practical applications under time or memory limits.

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“…In another study it has been observed that FIR and IIR filters show maximum SNR improvement when used to remove powerline interference. Hence these simple filters arequite commonly used for Electrocardiogram signal denoising [9]. This study thus compares the performance of various Finite Impulse Response (FIR) and Infinite Impulse Response (IIR) filters for Electrocardiogram signal noise removal based on their signal-to noise ratio (SNR), and thus aims to find the most suitable filter design and the optimum order at which it will effectively remove noise from Electrocardiogram.…”

Section: Introductionmentioning

confidence: 99%