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We developed a simple method to eliminate electrocardiogram (ECG) artifacts from electroencephalogram (EEG) records by using simultaneously recorded ECG data. The raw EEG data, the real EEG data and the ECG data were regarded as multi-dimensional vectors Ea, Er and C, respectively. Also, the ECG data, with reduced amplitude whose coefficient was denoted as 'k', were assumed to be overlapped on the real EEG. These assumptions introduced the equations [Ea = Er + k.C], [Er.C = 0] and finally [k = Ea. C/C.C]. This calculation method was implemented by a Macintosh computer using data exported from digital EEG recordings (sampled at 200 Hz with 16-bit resolution). In several subjects, sampling intervals of 5 or 10 seconds for calculation succeeded in eliminating ECG artifacts. However, regardless of the sampling interval, this elimination condition was not always efficient in several other subjects, including a brain-dead patient. It was suggested that the ECG data used were insufficient for the calculation, because only one hand-to-hand reference was used for simultaneous recording, as usual. This one ECG reference was able to express only one ECG projection. Then two other hand-to-foot references of ECG were added to the recordings, and the elimination procedure was performed using all of the simultaneously recorded ECG data at the three references. Consequently, elimination was much improved in most subjects, including the brain-dead patient. Our method may be useful for eliminating ECG artifacts without changing reference electrodes.
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Open Heart
September 2025
Department of Cardiology, Angiology and Internal Intensive Care Medicine, RWTH Aachen University, Aachen, Germany.
Background: Acute myocarditis is a potentially life-threatening cardiac condition and immediate assessment of this disease is imminent. While laboratory tests, electrocardiography or transthoracic echocardiography can provide indirect signs for the presence of acute myocarditis, cardiac magnetic resonance (CMR) imaging enables direct visualisation of myocardial inflammation and confirms the diagnosis.Since there is limited accessibility to CMR, the goal of this study was to evaluate the sensitivity and specificity of an elevation of established biomarkers for the diagnosis of myocarditis and to define a specific rule-out threshold for deferring CMR.
View Article and Find Full Text PDFJ Integr Neurosci
August 2025
School of Aeronautic Science and Engineering, Beihang University, 100191 Beijing, China.
Background: Pilots often experience mental fatigue during task performance, accompanied by fluctuations in positive (e.g., joy) and negative (e.
View Article and Find Full Text PDFInt J Gen Med
September 2025
Department of Radiology, Tangdu Hospital, The Fourth Military Medical University, Xi'an, Shaanxi Province, 710038, People's Republic of China.
Purpose: Compared with retrospective ECG-gated arterial phase scan, to investigate the clinical application value of dual-source CT large-spiral arterial late scan in the imaging evaluation of left atrial appendage (LAA).
Patients And Methods: A total of 108 patients requiring LAA CT angiography (CTA) due to atrial fibrillation (AF) were selected from September 2024 to December 2024, including 52 patients in group A (Flash large-spiral arterial late scan) and 56 patients in group B (retrospective ECG-gated arterial phase scan). All patients underwent double-phase scan.
Int J Cardiol Heart Vasc
October 2025
Department of Cardiothoracic Surgery, Friedrich-Schiller-University Jena, University Hospital Jena, Germany.
Background: Cardiac biomarkers are important components for diagnosing perioperative myocardial infarction (MI). Efforts to detect MI by biomarker-release only faced heavy criticism, because cardiac biomarker-release has also been observed in situations that are not always related to cell death (e.g.
View Article and Find Full Text PDFFront Med (Lausanne)
August 2025
Universidad Internacional Iberoamericana, Arecibo, PR, United States.
Electrocardiogram (ECG) classification plays a critical role in early detection and trocardiogram (ECG) classification plays a critical role in early detection and monitoring cardiovascular diseases. This study presents a Transformer-based deep learning framework for automated ECG classification, integrating advanced preprocessing, feature selection, and dimensionality reduction techniques to improve model performance. The pipeline begins with signal preprocessing, where raw ECG data are denoised, normalized, and relabeled for compatibility with attention-based architectures.
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