Digital pacer detection in diagnostic grade ECG

Shoaib, Mohammed; Garudadri, Harinath

doi:10.1109/health.2011.6026773

Cited by 10 publications

(4 citation statements)

References 16 publications

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“…The above concerns are realistic, especially because the amount of data transmit-ted by a sensor of WBSNs monitoring physiological signals can easily reach nearly 2.7 GB. If a higher data rate is employed, this number can even reach up to 31 GB per day [2].…”

Section: Cloud-assisted Wbsnsmentioning

confidence: 99%

Verifiable, privacy-assured, and accurate signal collection for cloud-assisted wireless sensor networks

Chen

Chao

2015

IEEE Commun. Mag.

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As cloud-assisted WBSNs have become increasingly popular in healthcare applications, security and privacy threats deserve much more attention. In this article, focusing on data privacy and data completeness issues, we propose a VPAA scheme for cloud-assisted WBSNs. Through both simulation and prototype implementation, we confirm that VPAA is energy-efficient and effective in protecting data privacy and completeness.

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Section: Cloud-assisted Wbsnsmentioning

confidence: 99%

Verifiable, privacy-assured, and accurate signal collection for cloud-assisted wireless sensor networks

Chen

Chao

2015

IEEE Commun. Mag.

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“…Therefore, a single pacemaker stimulus, originally lasting 0.2 -2 ms, becomes wider and significantly smaller, as shown in fig.1 in the case of 250 Hz data. A possible way to process low-sampled data may lead to an adaptive threshold [3] or a compressed sensing approach [4].…”

Section: Introductionmentioning

confidence: 99%

Automated Identification of Paced Beats in Holter ECG

Plešinger

Viščor

Smíšek

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Background: Identification of paced beats is a necessity for Holter ECG monitoring reports. However, lower sampling frequencies make the pacing stimuli hard to identify. In this study, we present a method to distinguish between paced and non-paced beats. Method: One-hour ECG recordings (158 patients, single lead, 250 Hz) were recorded during usual daily activities. A total of 44,918 QRS complexes were detected and marked as paced (19,004) or non-paced (25,914). This dataset was split (60%, out-of-patient) to training and testing datasets. Three features based on amplitude envelopes in two frequency bands were used to build a logistic regression model. An additional external dataset (2,193 recordings with 16,941 QRS) was assessed at a different facility and was used for the cross-database test. Results: The model showed the test F1-score of 0.93, cross-database test shown F1-score of 0.924. Conclusion: The presented method recognizes paced and non-paced heartbeats in lower sampling frequency, even if it can hardly be visually observed in the raw signal.

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“…IoT-based real-time remote monitoring has been applied in numerous areas such as healthcare, which has witnessed a large growth of interest in recent years [1][2][3]. Connecting smart devices, machines and humans in IoT ensures the development of efficient healthcare and medical asset monitoring systems.…”

Section: Introductionmentioning

confidence: 99%