Denoising and Features Extraction of ECG Signals in State Space Using Unbiased FIR Smoothing

Lastre-Dominguez, Carlos; Shmaliy, Yuriy S.; Ibarra-Manzano, Oscar; Vazquez-Olguin, Miguel

doi:10.1109/access.2019.2948067

Cited by 26 publications

(7 citation statements)

References 64 publications

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“…Our test dataset consists of signals with different Input SNR from -12 dB to 18 dB, and we categorized these signals into nine levels of -4, -3, -2, -1, 0, 1, 2, 4, and 8 dB. In addition to CEDN and FCNbased DAE, the Kalman bank filter [35], a finite impulse response (FIR) filter [32], and an infinite impulse response (IIR) filter are implemented to compare non-learning and learning ECG denoising approaches with each other. Undoubtedly, DeepRTSNet achieves a higher output SNR than the other approaches, which is increased as the input SNR is raised, which can be found in Fig.…”

Section: ) Experimental Results Based On Mit-bih Test Datasetmentioning

confidence: 99%

“…It is generally held that standard methods for denoising ECG such as Savitsky-Golay, wavelet, notch, median, and bandpass filters, can be accurate with less complexity. In [32], a q-lag unbiased finite impulse response (UFIR) filter is used in discrete-time state-space for denoising ECG signal and enhancing feature extraction, which has a better performance in comparison with conventional methods. In [33], sparse optimization and a low-pass filter are utilized for ECG noise cancellation and BW estimation regarding the diverse signal features.…”

Section: ) Ai and Conventional Methods For Ecg Denoisingmentioning

confidence: 99%

“…The data acquisition board works independently, so installing many boards do not affect the system's behavior. Another x x x x x x x x x x x x x x x x x x [19] x x x x x x x x x x x x x x x [20] x x x x x x x x x x x x [28] x x x x x x x x x x x x x x x x x x [29] x x x x x x x x x x x x x x x [31] x x x x x x x x x x x x x x x x x x [32] x x x x x x x x x x x x x x x x x x x x x [33] x x x x x x x x x x x x x x x x x x x x x [34] x x x x x x x x x x x x x x x x x x x x x [35] x x x x x x x x x x x x x x x x x x x x x [36] x x x x x x x x x [37] x x x x x x x x x x x x x x x x x x [39] x x x x x x x x x x x x x x x x x x Our work advantage is the cellular connectivity of our architecture, as it can simply connect to the internet by just powering on the device. This type of architecture has four levels: must be suitable for applying the information recorded by the ECG probe.…”

Section: Methodology a Circuits And Hardwarementioning

confidence: 99%

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DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

et al. 2022

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In this paper, we develop a low-cost cellular internet of medical things (IoMT)-based electrocardiogram (ECG) recorder for monitoring heart conditions and used in practical cases. In order to remove noise from signals recorded by these non-clinical devices, we propose a cloud-based denoising approach that focuses on utilizing deep neural network techniques in the time-frequency domain through the two stages. Accordingly, we exploit the fractional Stockwell transform (FrST) to transfer the ECG signal into the time-frequency domain and apply the deep robust two-stage network (DeepRTSNet) for noise cancellation. Due to the practical use case, the various heart physiologies and noise levels in different amplitudes and frequencies are needed to be robust against wide-range noises in actual conditions. We utilize the MIT-BIH Apnea-ECG database (APNEA-ECG) with several different heart physiologies. Next, the different noises consisting of muscle artifacts (MA), baseline wander (BW), and electrode motion (EM) from the MIT-BIH Noise Stress Test Database (NSTDB) and random noise, are added to the signals. The main focus of the noise generation part is the fast Fourier transformation (FFT) of the simulated noisy signal and the practical noisy signal has a maximum cross-correlation to gain a better morphological resemblance between realistic signals and the prepared datasets. Based on the results, DeepRTSNet outperforms prior learning-based methods and conventional non-learning approaches in terms of signal-to-noise ratio (SNR), root mean square error (RMSE), and percent root mean square difference (PRD). Moreover, outcomes reveal that DeepRTSNet has an extraordinary performance with a certain amount of further complexity than others.

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Section: ) Experimental Results Based On Mit-bih Test Datasetmentioning

confidence: 99%

Section: ) Ai and Conventional Methods For Ecg Denoisingmentioning

confidence: 99%

Section: Methodology a Circuits And Hardwarementioning

confidence: 99%

See 1 more Smart Citation

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

et al. 2022

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“…The system's performance was evaluated based on accuracy, "RMSE, and normalized RMSE". In (Lastre-Dominguez et al, 2019) [18], we address the discrete-time state-space problem using "a novel q-lag unbiased finite impulse response (UFIR) smoother" that optimizes the "ECG" signal shape by means of time-varying optimal averaging horizon. The adaptive "UFIR" smoother outperforms conventional approaches such as the Savitsky-Golay, waveletbased, low-pass, band-pass, notch, and median filters when applied to ECG data.…”

Section: Literature Reviewmentioning

confidence: 99%

Preparing of ECG Dataset for Biometric ID Identification with Creative Techniques

AL-Jibory¹,

Younis²,

Al-Tamimi³

2022

TEM Journal

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The Electrocardiogram records the heart's electrical signals. It is a practice; a painless diagnostic procedure used to rapidly diagnose and monitor heart problems. The ECG is an easy, noninvasive method for diagnosing various common heart conditions. Due to its unique advantages that other humans do not share, in addition to the fact that the heart's electrical activity may be easily detected from the body's surface, security is another area of concern. On this basis, it has become apparent that there are essential steps of pre-processing to deal with data of an electrical nature, signals, and prepare them for use in Biometric systems. Since it depends on the structure and function of the heart, it can be utilized as a biometric attribute for identification and recognition. This academic paper presents a new data formatting technique to be used in biometric systems based on the two major dataset kinds, PTB and PTB-XL.

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“…Whether it is traditional leads with cables or a wireless body sensor, any equipment can introduce noise into the ECG signal, as shown in Figure 4. Authors in [25] have attempted to remove internal noise by smoothing the ECG signal using the FIR filters and have achieved 99.3% accuracy. On the other hand, external Power-Line Interference (PLI) is the most disturbing noise that the ECG signal is susceptible to.…”

Section: B Stage2: Denoisingmentioning

confidence: 99%

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

et al. 2020

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Electrocardiogram (ECG) gives essential information about different cardiac conditions of the human heart. Its analysis has been the main objective among the research community to detect and prevent life threatening cardiac circumstances. Traditional signal processing methods, machine learning and its subbranches, such as deep learning, are popular techniques for analyzing and classifying the ECG signal and mainly to develop applications for early detection and treatment of cardiac conditions and arrhythmias. A detailed literature survey regarding ECG signal analysis is presented in this paper. We first introduce a stages-based model for ECG signal analysis where a survey of ECG analysis related work is then presented in the form of this stage-based process model. The model describes both traditional time/frequency-domain and advanced machine learning techniques reported in the published literature at every stage of analysis, starting from ECG data acquisition to its classification for both simulations and real-time monitoring systems. We present a comprehensive literature review of real-time ECG signal acquisition, prerecorded clinical ECG data, ECG signal processing and denoising, detection of ECG fiducial points based on feature engineering and ECG signal classification along with comparative discussions among the reviewed studies. This study also presents a detailed literature review of ECG signal analysis and feature engineering for ECG-based body sensor networks in portable and wearable ECG devices for real-time cardiac status monitoring. Additionally, challenges and limitations are discussed and tools for research in this field as well as suggestions for future work are outlined. INDEX TERMS ECG analysis, cardiac arrhythmias, QRS and ST detection, ECG classification, deep learning.

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Denoising and Features Extraction of ECG Signals in State Space Using Unbiased FIR Smoothing

Cited by 26 publications

References 64 publications

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

DeepRTSNet: Deep Robust Two-Stage Networks for ECG Denoising in Practical Use Case

Preparing of ECG Dataset for Biometric ID Identification with Creative Techniques

Stages-Based ECG Signal Analysis From Traditional Signal Processing to Machine Learning Approaches: A Survey

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