A cardiac electro-physiological model based approach for filtering high frequency ECG noise

Mneimneh, M.A.; Corliss, George F.; Povinelli, Richard J.

doi:10.1109/cic.2007.4745433

Cited by 6 publications

(5 citation statements)

References 6 publications

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“…Analog filters can also be used, but non-linear phase shift is introduced by the implementation of the same. [5] The performance of the various filters are compared in removing the high frequency noises and the results are tabulated below.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Various Filtering Techniques Used for Removing High Frequency Noise in Ecg Signal

Krishnamurthy¹,

Swethaanjali²,

Lakshmi³

2015

ijsrtm

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About 8.8 million males and 6.6 million females alive today have been affected by Coronary Heart Disease (CHD). Of these 5.0 million males and 2.6 million females have been diagnosed with Myocardial Infarction (MI), according to the statistics updated by the American Heart Association (AHA) in 2013 [1]. Early diagnosis and appropriate treatment of CHD is essential to minify the mortality rate due to CHD.This accentuates the need for an accurate and reliable equipment for monitoring the health conditions of hearts of human beings to treat the disease in advance before it brings about an irrevocable change in their body. One of the equipment’s used for this purpose is an Electrocardiograph, and the acquired signal is called an ECG signal. The acquisition process of this signal is hindered by a number of artifacts and removal of these artifacts is of paramount importance before the ECG signal could be used for disease diagnosis purpose.

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Section: Introductionmentioning

confidence: 99%

Comparison of Various Filtering Techniques Used for Removing High Frequency Noise in Ecg Signal

Krishnamurthy¹,

Swethaanjali²,

Lakshmi³

2015

ijsrtm

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“…According to Censi et al [1], the baseline wonder will affect the P wave, thereby affecting the interpretation of ECG signals. Mneimneh et al [2]. An adaptive Kalman filter (KF) is proposed to remove baseline miracles in real time.…”

Section: Signal Processing and Noise Removal In Ecg Signalsmentioning

confidence: 99%

Machine learning based techniques for ECG noise removal and feature extraction

2023

TNS

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Machine learning (ML) is being applied to all aspects of life with the development of artificial intelligence (AI). This paper explores the application of machine learning technology in electrocardiogram (ECG) analysis to diagnose and classify a patient's current cardiac disease, predict possible future diseases, and provide a personalised treatment plan. However, several challenges have been highlighted. First, individual ECG signals display variability, causing concern about effective diagnosis based on changing ECG data. Second, different diseases can produce similar ECG results, requiring powerful classification algorithms to accurately classify diseases. Finally, using patient information to predict the probability of future heart attacks is critical to developing appropriate prevention and treatment strategies. Overcoming these challenges could revolutionise the field of cardiology. It could enable precise and proactive medical intervention. The study highlights the potential of machine learning to improve cardiovascular care and personalised medicine and emphasises the importance of addressing key challenges to maximise its impact in clinical practice.

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“…(2) Baseline wander, which can result from a participant's movement or respiration, may lead to incorrect signal labeling (as depicted in Fig. 4(a)), subsequently impacting the accurate identification of features [34]. To eliminate these noises and prepare the signals for further analysis, we employ a two-step approach: Toolbox in MATLAB [35] to design two notch filters with center frequencies, f C , set at 50 Hz and 100 Hz, respectively.…”

Section: A Signal Preprocessingmentioning

confidence: 99%

Exploring Affective Peripheral Patterns Based on Body Surface Potentials with Covariance

Wu,

Pi,

Zhang

et al. 2023

Preprint

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Affective patterns based on physiological signals reflect bodily changes linked to specific emotional states. Previous studies on the cardiac electrical signal, a key peripheral physiological signal, were limited by the measurement density of single-lead ECG signal, focusing solely on temporal pattern analysis but ignoring topographic pattern analysis that can reflect the body’s emotional response. Our research advances affective peripheral pattern studies by innovatively using body surface potentials to comprehensively monitor cardiac electrical activity with increased measurement density. To tackle the challenge of extracting spatial and temporal features from multi-channel body surface potentials, we establish a dynamic correlation among these diverse channel signals through covariance matrices. Our hypothesis is that the dynamic inter-channel relationship provides a valuable source of insights into emotional clues. Experimental results demonstrate that the extracted spatial and temporal features effectively capture topographic and temporal patterns from cardiac electrical signals, and achieve excellent performance in classification tasks simultaneously. Our finding reveals affective patterns based on body surface potentials for the first time, offering novel insights into affective peripheral patterns analysis.

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A cardiac electro-physiological model based approach for filtering high frequency ECG noise

Abstract: With an increasing focus on automatic diagnoses of

Cited by 6 publications

References 6 publications

Comparison of Various Filtering Techniques Used for Removing High Frequency Noise in Ecg Signal

Comparison of Various Filtering Techniques Used for Removing High Frequency Noise in Ecg Signal

Machine learning based techniques for ECG noise removal and feature extraction

Exploring Affective Peripheral Patterns Based on Body Surface Potentials with Covariance

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