ECG Sensor Verification System with Mean-Interval Algorithm for Handling Sport Issue

Tseng, Kuo-Kun; Zeng, Fufu; Ip, W. H.; Wu, Chun-Ho

doi:10.1155/2016/1814264

Cited by 4 publications

(4 citation statements)

References 28 publications

(30 reference statements)

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“…Among others, there are those based on P onset and T offset locations [43] , although these marks are not precisely defined in practice. While in others [1,44] , the cutting is based on the length of the RR intervals, i.e. the time elapsed between two successive QRS detections.…”

Section: Appendix B Data Preprocessingmentioning

confidence: 99%

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Compelling new electrocardiographic markers for automatic diagnosis

Rueda

Fernández

Larriba

et al. 2022

Computer Methods and Programs in Biomedicine

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Section: Appendix B Data Preprocessingmentioning

confidence: 99%

“…Therefore, an ECG segmentation revision is desirable. Most of these procedures are based on the RR interval properties such as length or density [44,48] .…”

Section: Appendix B Data Preprocessingmentioning

confidence: 99%

Compelling new electrocardiographic markers for automatic diagnosis

Rueda

Fernández

Larriba

et al. 2022

Computer Methods and Programs in Biomedicine

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“…Therefore, an ECG segmentation revision is desirable. Most of these procedures are based on the RR interval properties such as length or density [48,44].…”

Section: Appendix a Fmm Ecg Examples For Bbb Featuresmentioning

confidence: 99%

Compelling new electrocardiographic markers for automatic diagnosis

Rueda,

Fernández,

Larriba

et al. 2021

Preprint

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Background and Objective: The automatic diagnosis of heart diseases from the electrocardiogram (ECG) signal is crucial in clinical decision-making. However, the use of computer-based decision rules in clinical practice is still deficient, mainly due to their complexity and a lack of medical interpretation. The objetive of this research is to address these issues by providing valuable diagnostic rules that can be easily implemented in clinical practice. In this research, efficient diagnostic rules friendly in clinical practice are provided. Methods: In this paper, interesting parameters obtained from the ECG signals analysis are presented and two simple rules for automatic diagnosis of Bundle Branch Blocks are defined using new markers derived from the so-called FMM ecg delineator. The main advantages of these markers are the good statistical properties and their clear interpretation in clinically meaningful terms. Results: High sensitivity and specificity values have been obtained using the proposed rules with data from more than 35000 patients from well known benchmarking databases. In particular, to identify Complete Left Bundle Branch Blocks and differentiate this condition from subjects without heart diseases, sensitivity and specificity values ranging from 93% to 99% and from 96% to 99%, respectively. The new markers and the automatic diagnosis are easily available at https://fmmmodel.shinyapps.io/fmmEcg/, an app specifically developed for any given ECG signal. Conclusions: The proposal is different from others in the literature and it is compelling for three main reasons. On the one hand, the markers have a concise electrophysiological interpretation. On the other hand, the diagnosis rules have a very high accuracy. Finally, the markers can be provided by any device that registers the ECG signal and the automatic diagnosis is made straightforwardly, in contrast to the black-box and deep learning algorithms.

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“…Waveform algorithms [32] use characteristic features obtained directly from the ECG waveform to verify human identity. After an ECG waveform is measured, it passes through high- and low-pass filters for pre-processing.…”

Section: Comparisonmentioning

confidence: 99%

Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches

Tseng

Chen

et al. 2018

Sensors

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Electrocardiograph (ECG) technology is vital for biometric security, and blood oxygen is essential for human survival. In this study, ECG signals and blood oxygen levels are combined to increase the accuracy and efficiency of human identification and verification. The proposed scheme maps the combined biometric information to a matrix and quantifies it as a sparse matrix for reorganizational purposes. Experimental results confirm a much better identification rate than in other ECG-related identification studies. The literature shows no research in human identification using the quantization sparse matrix method with ECG and blood oxygen data combined. We propose a multi-dimensional approach that can improve the accuracy and reduce the complexity of the recognition algorithm.

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ECG Sensor Verification System with Mean-Interval Algorithm for Handling Sport Issue

Cited by 4 publications

References 28 publications

Compelling new electrocardiographic markers for automatic diagnosis

Compelling new electrocardiographic markers for automatic diagnosis

Compelling new electrocardiographic markers for automatic diagnosis

Electrocardiograph Identification Using Hybrid Quantization Sparse Matrix and Multi-Dimensional Approaches

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