Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring

Satija, Udit; Ramkumar, Barathram; Manikandan, M. Sabarimalai

doi:10.1109/jbhi.2017.2686436

Cited by 135 publications

(57 citation statements)

References 30 publications

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“…Differently from our present research, these database recordings are not continuously labeled in terms of their signal quality. In addition, these studies seldom describe a detailed noise scale classification, but only the usual BW, muscle artifact, and PLI noise powers [ 35 , 36 , 37 , 38 ], and controlled noise with different SNR values is often added to the ECG signal [ 39 , 40 , 41 ] or is assigned in global terms of acceptable or unacceptable signals for the ECG quality assessment [ 29 , 42 , 43 ].…”

Section: Discussionmentioning

confidence: 99%

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Everss

Melgarejo-Meseguer

Blanco–Velasco

et al. 2017

Sensors

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Noise and artifacts are inherent contaminating components and are particularly present in Holter electrocardiogram (ECG) monitoring. The presence of noise is even more significant in long-term monitoring (LTM) recordings, as these are collected for several days in patients following their daily activities; hence, strong artifact components can temporarily impair the clinical measurements from the LTM recordings. Traditionally, the noise presence has been dealt with as a problem of non-desirable component removal by means of several quantitative signal metrics such as the signal-to-noise ratio (SNR), but current systems do not provide any information about the true impact of noise on the ECG clinical evaluation. As a first step towards an alternative to classical approaches, this work assesses the ECG quality under the assumption that an ECG has good quality when it is clinically interpretable. Therefore, our hypotheses are that it is possible (a) to create a clinical severity score for the effect of the noise on the ECG, (b) to characterize its consistency in terms of its temporal and statistical distribution, and (c) to use it for signal quality evaluation in LTM scenarios. For this purpose, a database of external event recorder (EER) signals is assembled and labeled from a clinical point of view for its use as the gold standard of noise severity categorization. These devices are assumed to capture those signal segments more prone to be corrupted with noise during long-term periods. Then, the ECG noise is characterized through the comparison of these clinical severity criteria with conventional quantitative metrics taken from traditional noise-removal approaches, and noise maps are proposed as a novel representation tool to achieve this comparison. Our results showed that neither of the benchmarked quantitative noise measurement criteria represent an accurate enough estimation of the clinical severity of the noise. A case study of long-term ECG is reported, showing the statistical and temporal correspondences and properties with respect to EER signals used to create the gold standard for clinical noise. The proposed noise maps, together with the statistical consistency of the characterization of the noise clinical severity, paves the way towards forthcoming systems providing us with noise maps of the noise clinical severity, allowing the user to process different ECG segments with different techniques and in terms of different measured clinical parameters.

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Section: Discussionmentioning

confidence: 99%

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Everss

Melgarejo-Meseguer

Blanco–Velasco

et al. 2017

Sensors

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“…The heartbeat signal system can be studied and applied in several psychological studies [6] and can also be applied for monitoring the condition of humant's heart cond [7] [8]. Joshua Proulx et al [9], research is about the development and evaluation of the Bluetooth electrocardiogram sensor which is designed to transmit medical data to mobile phones and the data can also be displayed and stored on mobile phones.…”

Section: Literature Studymentioning

confidence: 99%

Image Extraction of ECG Signals on Human Emotions Using Wavelet Transformation

Arifin¹,

Amalia²,

Maulana³

2018

Proceedings of the the 1st International Conference on Computer Science and Engineering Technology Universitas Muria Kudus

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Data processing of human heart's signals can be learned and applied in several studies, whether in psychological subject or in monitoring the condition of the human heart. A human's emotional aspect could be observed when the person recorded his heartbeat activities. The state of emotion observed in a normal, happy and sad conditions.The first stage of this research consists of measuring, recording, printing out the ECG data and scanning.The second stage, which named as pregeneration of the ECG image data stage, consists of median filtering and determining the threshold value and morphology. The third stage uses thinning and spatial-to-time methods for processing its ECG image.The last stage is named as characteristic extraction which uses a wavelet transform. From the characteristic extraction, the average of energy signal is obtained.

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“…This massive diversity in ECG monitoring systems' contexts, technologies, computational schemes, and purposes makes it hard for researchers and professionals to design, classify, and analyze ECG monitoring systems. Some efforts attempted to provide a common understanding of ECG monitoring systems' processes [42][43][44][45][46][47], guiding the design of efficient monitoring systems. However, these studies lack comprehensiveness and completeness.…”

Section: Introductionmentioning

confidence: 99%

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Serhani

Kassabi

Ismail

et al. 2020

Sensors

215

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Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems’ components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems’ value chain is conducted, and a thorough review of the relevant literature, classified against the experts’ taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

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Automated ECG Noise Detection and Classification System for Unsupervised Healthcare Monitoring

Abstract: Extensive studies on benchmark databases demonstrate that the proposed framework is more suitable for reducing false alarm rates and selecting appropriate noise-specific denoising techniques in automated ECG analysis applications.

Cited by 135 publications

References 30 publications

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Noise Maps for Quantitative and Clinical Severity Towards Long-Term ECG Monitoring

Image Extraction of ECG Signals on Human Emotions Using Wavelet Transformation

ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

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