Cardiac Rhythm Changes During Transfer from the Emergency Medical Service to the Emergency Department: A Retrospective Tertiary Single-Center Analysis on Prevalence and Outcomes

Mandigers, Loes; Rietdijk, Wim J.R.; den Uil, Corstiaan A.; de Graaf, Elise Y.; Strnisa, Silvio; Verdonschot, Rob J.C.G.

doi:10.1016/j.jemermed.2023.05.015

Cited by 2 publications

(1 citation statement)

References 19 publications

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“… 17 A recent retrospective single-center analysis described cardiac rhythm changes during transport to the emergency department in OHCA patients. 18 A significantly lower emergency department survival was observed in patients with cardiac rhythm change versus patients without cardiac rhythm change (26.5% vs 78.5%, p < 0.01). 16 Three different categories of cardiac rhythm change appeared most frequently in the rhythm change group: ROSC to non-shockable (57.1%), shockable to non-shockable (26.5%), and non-shockable to ROSC (8.2%).…”

Section: Discussionmentioning

confidence: 87%

Favorable resuscitation characteristics in patients undergoing extracorporeal cardiopulmonary resuscitation: A secondary analysis of the INCEPTION-trial

Ubben,

Heuts,

Delnoij

et al. 2024

Resuscitation Plus

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Section: Discussionmentioning

confidence: 87%

Favorable resuscitation characteristics in patients undergoing extracorporeal cardiopulmonary resuscitation: A secondary analysis of the INCEPTION-trial

Ubben,

Heuts,

Delnoij

et al. 2024

Resuscitation Plus

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An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms

Xia,

Wang,

Huang

et al. 2024

Electronics

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In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability.

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Cardiac Rhythm Changes During Transfer from the Emergency Medical Service to the Emergency Department: A Retrospective Tertiary Single-Center Analysis on Prevalence and Outcomes

Cited by 2 publications

References 19 publications

Favorable resuscitation characteristics in patients undergoing extracorporeal cardiopulmonary resuscitation: A secondary analysis of the INCEPTION-trial

Favorable resuscitation characteristics in patients undergoing extracorporeal cardiopulmonary resuscitation: A secondary analysis of the INCEPTION-trial

An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms

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