Synthesized 18-Lead Electrocardiogram in Diagnosing Posterior Stemi-Equivalent Acute Coronary Syndrome in Patients with NSTEMI

Horie, Tomoki; Hamaya, Rikuta; Sugiyama, Tomoyo; Hirano, Hirohiko; Hoshino, Masahiro; Kanaji, Yoshihisa; Lee, Tetsumin; Yonetsu, Taishi; Sasano, Tetsuo; Kakuta, Tsunekazu

doi:10.1155/2022/9582174

Cited by 2 publications

(3 citation statements)

References 23 publications

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“…While ECG is highly valuable for diagnosing AF, it has some limitations [37]. Firstly, AF episodes can be sporadic and transient, making it challenging to capture an episode during a short recording period.…”

Section: The Role Of Ecg In Af Detectionmentioning

confidence: 99%

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

et al. 2023

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Atrial fibrillation is a prevalent cardiac arrhythmia that poses significant health risks to patients. The use of non-invasive methods for AF detection, such as Electrocardiogram and Photoplethysmogram, has gained attention due to their accessibility and ease of use. However, there are challenges associated with ECG-based AF detection, and the significance of PPG signals in this context has been increasingly recognized. The limitations of ECG and the untapped potential of PPG are taken into account as this work attempts to classify AF and non-AF using PPG time series data and deep learning. In this work, we emploted a hybrid deep neural network comprising of 1D CNN and BiLSTM for the task of AF classification. We addressed the under-researched area of applying deep learning methods to transmissive PPG signals by proposing a novel approach. Our approach involved integrating ECG and PPG signals as multi-featured time series data and training deep learning models for AF classification. Our hybrid 1D CNN and BiLSTM model achieved an accuracy of 95% on test data in identifying atrial fibrillation, showcasing its strong performance and reliable predictive capabilities. Furthermore, we evaluated the performance of our model using additional metrics. The precision of our classification model was measured at 0.88, indicating its ability to accurately identify true positive cases of AF. The recall, or sensitivity, was measured at 0.85, illustrating the model’s capacity to detect a high proportion of actual AF cases. Additionally, the F1 score, which combines both precision and recall, was calculated at 0.84, highlighting the overall effectiveness of our model in classifying AF and non-AF cases.

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Section: The Role Of Ecg In Af Detectionmentioning

confidence: 99%

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

et al. 2023

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“…European Society of Cardiology and American Heart Association/American College of Cardiology guidelines recommend acquiring posterior leads V7 through V9 for suspected cases of acute left circmflex occlusion, such as in patients with typical symptoms and an initially nondiagnostic ECG or STsegment depression (STD) in leads V1-3 (Amsterdam et al 2014, Ibanez et al 2016, Levine et al 2016, Collet et al 2021.et al (2022) recently published about a synthesized 18-lead ECG which utilizes computerized software to create the additional V7-V9 leads. To test their software, they restropectively examined NSTEMI patients with left circumflex occlusion and reported a sensitivity and specificity of 46.7% and 95.6%, respectively, largely limited by small sample size (n = 16) (Horie et al 2022). It is important to note that Horie et al (2022) report that approximately 1-in-4 patients, whom would be candiates to have V7-V9 recorded, did not have V7-V9 recorded emphasizing the potential utility of synthesized leads.…”

Section: Ecg-silent Myocardial Regionsmentioning

confidence: 99%

“…To test their software, they restropectively examined NSTEMI patients with left circumflex occlusion and reported a sensitivity and specificity of 46.7% and 95.6%, respectively, largely limited by small sample size (n = 16) (Horie et al 2022). It is important to note that Horie et al (2022) report that approximately 1-in-4 patients, whom would be candiates to have V7-V9 recorded, did not have V7-V9 recorded emphasizing the potential utility of synthesized leads.…”

Section: Ecg-silent Myocardial Regionsmentioning

confidence: 99%

Secondary electrocardiographic stratification of NSTEMI to identify an acutely occluded culprit artery

Dzikowicz

Carey

2023

Physiol. Meas.

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In the United States, approximately 720,000 adults will experience a myocardial infarction (MI) every year. The 12-lead electrocardiogram (ECG) is quintessential for the classification of a MI. About 30% of all MIs exhibit ST-segment elevation on the 12-lead ECG and is therefore classified as an ST-Elevation Myocardial Infarction (STEMI), which is treated emergently with percutaneous coronary intervention to restore blood flow. However, in the remaining 70% of MIs, the 12-lead ECG lacks ST-segment elevation and instead exhibits a motley of changes, including ST-segment depression, T-wave inversion, or, in up to 20% of patients, have no changes whatsoever; as such, these MIs are classified as a Non-ST Elevation Myocardial Infarction (NSTEMI). Of this large cluster of MIs, 33% of NSTEMI actually have an occlusion of the culprit artery consistent with a Type I MI per the 4th Universal Definition of MI. This is a serious clinical problem because NSTEMI with an occluded culprit artery have similar myocardial damage like STEMI and are more likely to suffer from adverse outcomes compared to NSTEMI without an occluded culprit artery. In this review article, we review the extant literature on NSTEMI with an occluded culprit artery. Afterward, we generate and discuss hypotheses for the absence of ST-segment elevation on the 12-lead ECG: (1) Transient Occlusion (2) Collateral Blood Flow and Chronically Occluded Artery and (3) ECG-Silent Myocardial Regions. Lastly, we describe and define novel ECG features that are associated with an occluded culprit artery in NSTEMI which include T-wave morphology abnormalities and novel markers of ventricular repolarization heterogeneity.

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Synthesized 18-Lead Electrocardiogram in Diagnosing Posterior Stemi-Equivalent Acute Coronary Syndrome in Patients with NSTEMI

Cited by 2 publications

References 23 publications

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

A Deep Learning Approach for Atrial Fibrillation Classification Using Multi-Feature Time Series Data from ECG and PPG

Secondary electrocardiographic stratification of NSTEMI to identify an acutely occluded culprit artery

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