Creating 12‐lead electrocardiogram waveforms using a three‐lead bedside monitor to ensure appropriate monitoring

Yoneyama, Kihei; Naka, Mayumi; Harada, Tomoo; Akashi, Yoshihiro J.

doi:10.1002/joa3.12441

Cited by 3 publications

(3 citation statements)

References 3 publications

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“…The ECG characteristics taken using the KT bands on a healthy volunteer is shown in Figure 2A. The precordial lead measurements were taken with respect to a reference electrode placed on top of the sternum, which has been demonstrated as a close equivalent 15 to the virtual Wilson Central Terminal (WCT) averaged from three limb leads. The sternum reference is used here when the conventional limb lead electrodes are not connected, but the reference can be easily reverted back to virtual WCT when limb leads are present.…”

Section: A Stretchable Ecg Interfacementioning

confidence: 99%

Stretchable Textile Bands for Ambulatory Electrocardiogram and Oximetry

Liu

Sadatian

et al. 2022

IEEE Flex. Electron.

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This work presents a vital sign monitoring interface combining electrocardiogram (ECG) and reflective photoplethysmography (PPG) acquisition on stretchable kinesiology tapes. The integrated textile bands are less prone to electrode misplacement and deliver high-quality diagnostic ECG signals with dry electrodes. The reflective PPG measured over the subclavian artery is used to track blood oxygen saturation, and in combination with ECG, enables the determination of pulse transit time and inference of blood pressure. This multimodal interface improves clinical work flows because it is easy to don, reduce motion artifacts, and facilitate more efficient and accurate diagnosis in emergency settings.

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Section: A Stretchable Ecg Interfacementioning

confidence: 99%

Stretchable Textile Bands for Ambulatory Electrocardiogram and Oximetry

Liu

Sadatian

et al. 2022

IEEE Flex. Electron.

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“…1. The RA--, LA+-and LL++ form the Einthoven's triangle [7], which is an imaginary formation for electrode lead placement points, for measuring voltage potential differences between them, of cardiac activity. Theoretically, the unipolar augmented vector leads aVR(-150º), aVL(-30º) and aVF(+90º) are vectors derived from the respective electrodes RA, LA and LL in relation to the heart position.…”

Section: Theoretical Aspects and Prototype Developmentmentioning

confidence: 99%

Mobile Cardiac Telemetry System for Isolated Immunosuppressed Patients

Miron-Alexe

2021

J. Sci. Arts

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This article represents an experiment for a portable open-source ECG monitoring system, purposed for isolated immunosuppressed patients, with heart conditions. At the current stage of development, the proposed experimental MCT (Mobile Cardiac Telemetry) system can be wirelessly used in conjunction with a smart phone and a real-time graph streaming application, through the BLE (Bluetooth Low Energy) module of an IoT embedded platform. At the moment, the system is intended for autonomous low power consumption and short wireless proximity range suited for close monitoring of patients by themselves at home, or by the medical staff in the isolated hospital ward rooms or mobile isolation containters.

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“…12 leads ECG. a) Signals from 12 Leads ECG[20]. b) Position of placements of the 12 leads on human body[21].…”

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confidence: 99%

Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders

Dey¹,

Pal²,

Biswas³

2022

Biomedical Engineering

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Electrocardiography (ECG) has been a reliable method for monitoring the proper functioning of the cardiovascular system for decades. Recently, there has been a lot of research focusing on accurately analyzing the heart condition through ECG. In recent days, numerous attempts are being made to analyze these signals using deep learning algorithms, including the implementation of artificial neural networks like convolutional neural networks, recurrent neural networks, and the like. In this context, this chapter intends to present some important techniques for classifying heartbeats based on deep neural networks with 1D CNN. Five ECG signals (N, S, V, F, and Q) standardization are based on the AAMI EC57 standard. The primary focus of this chapter is to discuss the techniques to classify ECG signals in those classes with promising accuracy and draw a clear picture of the current state-of-the-art in this sphere of study.

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Creating 12‐lead electrocardiogram waveforms using a three‐lead bedside monitor to ensure appropriate monitoring

Abstract: How do you place the three electrodes to create waveforms for leads I, II, III, aVR, aVL, aVF, and V1‐V6?

Cited by 3 publications

References 3 publications

Stretchable Textile Bands for Ambulatory Electrocardiogram and Oximetry

Stretchable Textile Bands for Ambulatory Electrocardiogram and Oximetry

Mobile Cardiac Telemetry System for Isolated Immunosuppressed Patients

Deep Learning Algorithms for Efficient Analysis of ECG Signals to Detect Heart Disorders

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