ECG Signal De-noising with Signal Averaging and Filtering Algorithm

Gautam, Alka; Lee, Young-Dong; Chung, Wan-Young

doi:10.1109/iccit.2008.393

Cited by 13 publications

(9 citation statements)

References 3 publications

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“…The pre-processing stage was finalized by averaging of the heartbeat templates extracted from a single recording to improve signal-to-noise ratio [14]. Additionally, parameters referring to the heart rate (mean duration and standard deviation of R-to-R intervals) were derived.…”

Section: Data Pre-processingmentioning

confidence: 99%

The North Sea Bicycle Race ECG Project: Time-Domain Analysis

Dlugosz

Eftestøl

Królak

et al. 2018

JAMRIS

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Analysis of electrocardiogram and heart rate provides useful information about health condition of a patient. The North Sea Bicycle Race is an annual cycling competition in Norway. Examination of ECG recordings collected from participants of this race may allow defining and evaluating the relationship between physical endurance exercises and heart electrophysiology. Parameters reflecting potentially alarming deviations are to be identified in this study. This paper presents results of a time-domain analysis of ECG data collected in 2014, implementing K-Means clustering. A double stage analysis strategy, aimed at producing hierarchical clusters, is proposed. The first phase allows rough separation of data. Second stage is applied to reveal internal structure of the majority clusters. In both steps, discrepancies driving the separation could stem from three sources. Firstly, they could be signs of abnormalities in electrical activity of the heart. Secondly, they may allow discriminating between natural groups of participants-according to sex, age, physical fitness. Finally, some deviations could result from faults in data extraction, therefore serving in evaluation of the parameters. The clusters were defined predominantly by combinations of features: heartbeat signals correlation, P-wave shape, and RR intervals; none of the features alone was discriminative for all the clusters.

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Section: Data Pre-processingmentioning

confidence: 99%

The North Sea Bicycle Race ECG Project: Time-Domain Analysis

Dlugosz

Eftestøl

Królak

et al. 2018

JAMRIS

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“…Since the poor signal to noise ratio (SNR) in signal fading leads to unreliable reconstruction, the SNR value will be used to detect signal fading. To estimate the SNR value in the SMS, we apply the method used in SNR calculation of ECG signals [16].…”

Section: Sensor Diversity Techniquementioning

confidence: 99%

Dealing With Speckle Effects in Self-Mixing Interferometry Measurements

2013

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An analysis of speckle effects and the techniques to overcome them in self-mixing interferometry signals is presented. We characterize the effect of surface roughness and laser spot size on the speckle modulation of the signal, and then propose two simple experimental approaches to overcome the amplitude fading induced by the speckle effect. Unlike the techniques proposed until now, our first approach uses an adaptive optical element in the form of a voltage-programmable liquid lens, which adaptively changes its focal length to modify the speckle pattern. Our second approach combines two laser signals which present different performance parameters. By using any of these simple methods, the introduction of inaccuracies in the measurement process due to speckle is avoided.Index Terms-Displacement measurement, interferometry selfmixing, speckle effect.

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“…It can achieve a depressed average error w.r.t a template deduced from each ECG signal. e. ECG signal has been de-noised using signal averaging and filtering method [9]. This method gets rid of baseline wandering and EMG noises but it is unable to remove motion artifact incorporated with desired ECG signal.…”

Section: Literature Surveymentioning

confidence: 99%

Various Approaches to Minimise Noises in ECG Signal: A Survey

Singh

Budhiraja

2015

2015 Fifth International Conference on Advanced Computing &Amp; Communication Technologies

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ECG signal is time varying in nature which is most common source used for the diagnosis and analysis of heart diseases present in the patient.ECG is recorded by placing electrodes at specified positions of human body. During recording, ECG is contaminated with artifacts and noises which always degrade its quality, and makes accurate and automatic interpretation more difficult. Power line interference, baseline wanders and muscle tremors are mostly noticed artifacts or noises. So for accurate delineation of characteristics points of ECG, a good quality of ECG is necessary. This paper is presenting a survey on various methods developed for de-noising, delineation of characteristics points and classification of diseases along with their respective advantages and disadvantages.

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ECG Signal De-noising with Signal Averaging and Filtering Algorithm

Cited by 13 publications

References 3 publications

The North Sea Bicycle Race ECG Project: Time-Domain Analysis

The North Sea Bicycle Race ECG Project: Time-Domain Analysis

Dealing With Speckle Effects in Self-Mixing Interferometry Measurements

Various Approaches to Minimise Noises in ECG Signal: A Survey

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