Real-Time ECG Signal Feature Extraction for the Proposition of Abnormal Beat Detection - Periodical Signal Extraction

Szczepański, Adam; Saeed, Khalid

doi:10.1109/icbake.2013.51

Cited by 4 publications

(6 citation statements)

References 20 publications

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“…The whole part of the signal between the last correct heartbeat before the anomaly and the first correct heartbeat after the anomaly is extracted. True beginnings of the anomalies are not necessary considering the case of raising alert when the anomalies are detected, although in the simulation software they are found during anomaly description stage using linear least squares approach [15]. …”

Section: Simulation Tests and Resultsmentioning

confidence: 99%

“…The authors have completed the algorithm for acquisition of characteristic points from abnormal ECG fragments and their description (see [15]) although the software does not yet recognize the cause of the abnormality. It needs to be trained on abnormal samples to learn disease patterns.…”

Section: Methods Description and Implementation For Mobile Devicesmentioning

confidence: 99%

“…Its main pieces, which were described in detail in an earlier publication of the authors [15], are the description of heartbeats and the acquisition of faulty beats. For the purpose of this paper the algorithms have undergone optimization and are used in a prototype implementation for mobile devices.…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned earlier, the algorithm is described thoroughly in [15] so only a short description is presented here.…”

Section: Related Workmentioning

confidence: 99%

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A Mobile Device System for Early Warning of ECG Anomalies

Szczepański

Saeed

2014

Sensors

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With the rapid increase in computational power of mobile devices the amount of ambient intelligence-based smart environment systems has increased greatly in recent years. A proposition of such a solution is described in this paper, namely real time monitoring of an electrocardiogram (ECG) signal during everyday activities for identification of life threatening situations. The paper, being both research and review, describes previous work of the authors, current state of the art in the context of the authors' work and the proposed aforementioned system. Although parts of the solution were described in earlier publications of the authors, the whole concept is presented completely for the first time along with the prototype implementation on mobile device—a Windows 8 tablet with Modern UI. The system has three main purposes. The first goal is the detection of sudden rapid cardiac malfunctions and informing the people in the patient's surroundings, family and friends and the nearest emergency station about the deteriorating health of the monitored person. The second goal is a monitoring of ECG signals under non-clinical conditions to detect anomalies that are typically not found during diagnostic tests. The third goal is to register and analyze repeatable, long-term disturbances in the regular signal and finding their patterns.

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Section: Simulation Tests and Resultsmentioning

confidence: 99%

Section: Methods Description and Implementation For Mobile Devicesmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…As mentioned earlier, the algorithm is described thoroughly in [15] so only a short description is presented here.…”

Section: Related Workmentioning

confidence: 99%

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A Mobile Device System for Early Warning of ECG Anomalies

Szczepański

Saeed

2014

Sensors

Self Cite

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“…In literature, a number of studies found that build a reasonable accuracy level in terms of classification of different T wave episodes, but due to these methods, the difference between flattened T wave and inversion T wave is still unclear [30][31][32][33][34]. Accurate and robust classification of different T wave episodes relies entirely on the identification of the values of T wave parameters especially in terms of T-onset and T-offset [35][36][37][38][39][40][41]. Such findings are further helpful for highlighting the intensity of MI in terms of ST-segment elevation and ST-segment depression [42][43][44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Methodological identification of anomalies episodes in ECG streams: a systematic mapping study

Iqbal,

Almakki,

Usman

et al. 2024

BMC Med Res Methodol

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An electrocardiogram is a medical examination tool for measuring different patterns of heart blood flow circle either in the form of usual or non-invasive patterns. These patterns are useful for the identification of morbidity condition of the heart especially in certain conditions of heart abnormality and arrhythmia. Myocardial infarction (MI) is one of them that happened due to sudden blockage of blood by the cause of malfunction of heart. In electrocardiography (ECG) intensity of MI is highlighted on the basis of unusual patterns of T wave changes. Various studies have contributed for MI through T wave’s classification, but more to the point of T wave has always attracted the ECG researchers. Methodology. This Study is primarily designed for proposing the combination of latest methods that are worked for the solutions of pre-defined research questions. Such solutions are designed in the form of the systematic review process (SLR) by following the Kitchen ham guidance. The literature survey is a two phase’s process, at first phase collect the articles that were published in IEEE Xplore, Scopus, science direct and Springer from 2008 to 2023. It consist of steps; the first level is executed by filtrating the articles on the basis of keyword phase of title and abstract filter. Similarly, at two level the manuscripts are scanned through filter of eligibility criteria of articles selection. The last level belongs to the quality assessment of articles, in such level articles are rectified through evaluation of domain experts. Results. Finally, the selected articles are addressed with research questions and briefly discuss these selected state-of-the-art methods that are worked for the T wave classification. These address units behave as solutions to research problems that are highlighted in the form of research questions. Conclusion and future directions. During the survey process for these solutions, we got some critical observations in the form of gaps that reflected the other directions for researchers. In which feature engineering, different dependencies of ECG features and dimensional reduction of ECG for the better ECG analysis are reflection of future directions.

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Abnormal Signal Detection based on Time Series Clustering

Zhang

et al. 2019

Proceedings of the 2019 4th International Conference on Information and Education Innovations - ICIEI 2019

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Real-Time ECG Signal Feature Extraction for the Proposition of Abnormal Beat Detection - Periodical Signal Extraction

Cited by 4 publications

References 20 publications

A Mobile Device System for Early Warning of ECG Anomalies

A Mobile Device System for Early Warning of ECG Anomalies

Methodological identification of anomalies episodes in ECG streams: a systematic mapping study

Abnormal Signal Detection based on Time Series Clustering

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