Noise Removal from ECG Signal Based on Filtering Techniques

Almalchy, Mohammed Tali; Ciobanu, Vlad; Popescu, Nirvana

doi:10.1109/cscs.2019.00037

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…The final part is CNN-LSTM ECG beat categorization. Arrhythmia diagnostic research has traditionally emphasized on noise filtering from ECG recordings [ 3 ], segmenting waveforms [ 4 ], and extraction of various features. Several processes and techniques for analyzing and classifying the ECG signal have been proposed.…”

Section: Introductionmentioning

confidence: 99%

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

Anbarasi

Ravi

Manjula

et al. 2022

BioMed Research International

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Arrhythmias are anomalies in the heartbeat rhythm that occur occasionally in people’s lives. These arrhythmias can lead to potentially deadly consequences, putting your life in jeopardy. As a result, arrhythmia identification and classification are an important aspect of cardiac diagnostics. An electrocardiogram (ECG), a recording collecting the heart’s pumping activity, is regarded the guideline for catching these abnormal episodes. Nevertheless, because the ECG contains so much data, extracting the crucial data from imagery evaluation becomes extremely difficult. As a result, it is vital to create an effective system for analyzing ECG’s massive amount of data. The ECG image from ECG signal is processed by some image processing techniques. To optimize the identification and categorization process, this research presents a hybrid deep learning-based technique. This paper contributes in two ways. Automating noise reduction and extraction of features, 1D ECG data are first converted into 2D pictures. Then, based on experimental evidence, a hybrid model called CNNLSTM is presented, which combines CNN and LSTM models. We conducted a comprehensive research using the broadly used MIT_BIH arrhythmia dataset to assess the efficacy of the proposed CNN-LSTM technique. The results reveal that the proposed method has a 99.10 percent accuracy rate. Furthermore, the proposed model has an average sensitivity of 98.35 percent and a specificity of 98.38 percent. These outcomes are superior to those produced using other procedures, and they will significantly reduce the amount of involvement necessary by physicians.

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Section: Introductionmentioning

confidence: 99%

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

Anbarasi

Ravi

Manjula

et al. 2022

BioMed Research International

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“…Almalchy et al [7] investigated a number of models of finite impulse response (FIR) low-pass and high-pass filters, as well as their characteristics in terms of reply time, earning, consistent deformation, and repudiation, to define best bandpass nomination sample to produce an ECG signal that exceedingly like a patient's actual heart function. A hybrid nomination technique is also presented and tested.…”

Section: Related Workmentioning

confidence: 99%

“…The ECG signals are intrinsically low and noisy signals made up of numerous changeable components due to a variety of environmental conditions such as variations in body temperature, body movement, and line frequency (50/60 Hz), among others. Because the ECG signal cannot be directly conditioned, amplified, or replicated, digital filtering techniques with configurable windows are utilized [7].…”

Section: Introductionmentioning

confidence: 99%

ECG Paper Digitization and R Peaks Detection Using FFT

Fathail¹,

Bhagile

2022

Applied Computational Intelligence and Soft Computing

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An electrocardiogram (ECG) uses electrodes to monitor the heart rhythm and identify minute electrical changes that occur with each beat. It is employed to investigate particular varieties of aberrant heart activity, such as arrhythmias and conduction problems. One of the most essential tools for detecting heart problems is the electrocardiogram (ECG). The majority of ECG records are still on paper. Manual ECG paper record analysis can be difficult and time-consuming. It is possible to digitally digitize these paper ECG recordings for automated analysis and diagnosis. In this paper, we proposed a system to digitize the ECG paper, automatically detecting R peaks, calculating the average heart rate, and sending SMS to the doctor via cloud in the event of detection of abnormality. The method of the system is uploading an ECG image, then dimensionality reduction, feature extraction in the form of digital signals, and saving it in a CSV file format using the MATLAB programming language. After that, the system retrieves the signals for further processing of the raw signals. We used the fast Fourier transform (FFT) algorithm to calculate R peaks and calculate the heart rate. If the heart rate is abnormal, the system sends SMS messages to doctors via a technology platform (Twilio) using the Python programming language.

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“…Since the ECG is a non-stationary signal, normal filters cannot be effective to remove the noise; so, several techniques are used to do so for such types of signals. ECG signals have a wide variety of applications in the medical domain such as cardiorespiratory monitoring, seizure detection and monitoring, ECG-based biometrics authentication, real-time analysis of electrocardiographic rhythm, heart-rate variability analysis using smart electrocardiography patch, and study of cardiac ischemia [6][7][8][9][10][11]. These applications require a proper determination of the morphological and interval aspects of the recorded ECG signal, which are susceptible to various kinds of predominant noises such as base-line wander (BW), muscle artefacts (MA) or electromyogram (EMG) noise, channel noise (additive white Gaussian noise, AWGN), power-line interference (PLI), and miscellaneous noises such as composite noise (CN), random noise, electrode motion artefacts (EM), and instrumentation noise, making it challenging to determine disease-specific morphological anomalies in the ECG signals [5].…”

Section: Introductionmentioning

confidence: 99%

A review of ECG signal filtering approaches

Ahmed¹,

Al-Obaidi²

2022

Global J. Eng. Technol. Adv.

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An electrocardiogram (ECG) quantifies the electrical activity of the heart to screen for different heart diseases, although it can be impacted by noise. ECG signal filtering is a crucial pre-processing step that reduces noise and emphasizes the characteristic waves in ECG data. In real-world applications, the ECG signal is contaminated by different types of noise. Separating the desired signal from noises produced by artifacts such as muscle noise, power line interference (PLI), baseline wandering (BW), and motion artifacts (MA) is a complicated task. In this paper, a quick review of various ECG signal denoising methods is introduced.

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Noise Removal from ECG Signal Based on Filtering Techniques

Cited by 9 publications

References 3 publications

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

A Modified Deep Learning Framework for Arrhythmia Disease Analysis in Medical Imaging Using Electrocardiogram Signal

ECG Paper Digitization and R Peaks Detection Using FFT

A review of ECG signal filtering approaches

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