Basics of the 12-lead ECG

Bruce, Kellie; Moore, Amy Karen; Mello, Inola; Hilliard, Tara; Day, Mercedes

doi:10.1097/01.nurse.0000978872.53064.3e

Cited by 1 publication

(1 citation statement)

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“…An ECG signals the curve of the heart’s electrical activity, and waveforms recorded by different leads can effectively reflect changes in various parts of the heart [ 33 ]. Bruce et al [ 34 ] proposed that leads V1, V2, and V3 monitor the activity of the heart from the anterior surface; leads V1 and V2 examine the interventricular septum and right ventricular electrical activity; lead V3 monitors the activity of the left ventricular anterior wall; lead V6 measures the activity of the lower anterolateral wall of the left ventricle; and lead aVF observes the electrical activity on the coronal plane. Leads aVF, V1, V2, V3, and V6 provide different perspectives on the electrical activity of various parts of the heart.…”

Section: Discussionmentioning

confidence: 99%

Visualized Lead Selection for Arrhythmia Classification Based on a Lead Activation Heatmap Using Multi-Lead ECGs

Wang,

Shen,

Jiang

et al. 2024

Bioengineering

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Visualizing the decision-making process is a key aspect of research regarding explainable arrhythmia recognition. This study proposed a visualized lead selection method to classify arrhythmia for multi-lead ECG signals. The proposed method has several advantages, as it uses a visualized approach to select effective leads, avoiding redundant leads and invalid information. It also captures the temporal dependencies of ECG signals and the complementary information between leads. The method deployed a lead activation heatmap (LA heatmap) based on a lead-wise network to select the proper 5 leads from 12-lead ECG heartbeats extracted from the public 2018 Chinese Physiological Signal Challenge database (CPSC 2018 DB), which were then fed into a ResBiTime network combining bidirectional long short-term memory (Bi-LSTM) networks and residual connections for a classification task of nine heartbeat categories (i.e., N, AF, I-AVB, RBBB, PAC, PVC, STD, LBBB, and STE). The results indicate an average precision of 93.25%, an average recall of 93.03%, an average F1-score of 0.9313, and that the proposed method can effectively extract additional information from ECG heartbeat data.

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Section: Discussionmentioning

confidence: 99%