Weiteres über das Elektrokardiogramm

Einthoven, W.

doi:10.1007/978-94-010-1301-7_11

Cited by 3 publications

(3 citation statements)

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“…Electrocardiography (ECG) continues to be the most commonly used laboratory procedure for the diagnosis of heart disease. Introduced in 1902 by Einthoven [ 2 ], the procedure reflects electrical changes associated with primary or secondary myocardial processes for example coronary artery disease, hypertension, and electrolyte abnormalities [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a web-based ECG-interpretation programme for undergraduate medical students

et al. 2008

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Background: Most clinicians and teachers agree that knowledge about ECG is of importance in the medical curriculum. Students at Karolinska Institutet have asked for more training in ECGinterpretation during their undergraduate studies. Clinical tutors, however, have difficulties in meeting these demands due to shortage of time. Thus, alternative ways to learn and practice ECGinterpretation are needed. Education offered via the Internet is readily available, geographically independent and flexible. Furthermore, the quality of education may increase and become more effective through a superior educational approach, improved visualization and interactivity.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a web-based ECG-interpretation programme for undergraduate medical students

et al. 2008

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“…For example, a 4-lead subset consisting of leads I, aVR, V1, and V4 from the ECG report is completely asynchronous. According to the Einthoven law and Goldberger equation, for the 6 limb leads (leads I, II, III, aVR, aVL, and aVF), the remaining 4 leads can be calculated even if only 2 leads are available [ 21 , 22 ]. Here, we trained/validated our AI model by randomly masking each lead channel and then tested our model with various target lead sets (while masking the remaining lead channels) to determine the optimal number of leads for sufficient diagnostic power.…”

Section: Methodsmentioning

confidence: 99%

Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals—Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study

Han¹,

Song²,

Lim³

et al. 2021

J Med Internet Res

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Background When using a smartwatch to obtain electrocardiogram (ECG) signals from multiple leads, the device has to be placed on different parts of the body sequentially. The ECG signals measured from different leads are asynchronous. Artificial intelligence (AI) models for asynchronous ECG signals have barely been explored. Objective We aimed to develop an AI model for detecting acute myocardial infarction using asynchronous ECGs and compare its performance with that of the automatic ECG interpretations provided by a commercial ECG analysis software. We sought to evaluate the feasibility of implementing multiple lead–based AI-enabled ECG algorithms on smartwatches. Moreover, we aimed to determine the optimal number of leads for sufficient diagnostic power. Methods We extracted ECGs recorded within 24 hours from each visit to the emergency room of Ajou University Medical Center between June 1994 and January 2018 from patients aged 20 years or older. The ECGs were labeled on the basis of whether a diagnostic code corresponding to acute myocardial infarction was entered. We derived asynchronous ECG lead sets from standard 12-lead ECG reports and simulated a situation similar to the sequential recording of ECG leads via smartwatches. We constructed an AI model based on residual networks and self-attention mechanisms by randomly masking each lead channel during the training phase and then testing the model using various targeting lead sets with the remaining lead channels masked. Results The performance of lead sets with 3 or more leads compared favorably with that of the automatic ECG interpretations provided by a commercial ECG analysis software, with 8.1%-13.9% gain in sensitivity when the specificity was matched. Our results indicate that multiple lead-based AI-enabled ECG algorithms can be implemented on smartwatches. Model performance generally increased as the number of leads increased (12-lead sets: area under the receiver operating characteristic curve [AUROC] 0.880; 4-lead sets: AUROC 0.858, SD 0.008; 3-lead sets: AUROC 0.845, SD 0.011; 2-lead sets: AUROC 0.813, SD 0.018; single-lead sets: AUROC 0.768, SD 0.001). Considering the short amount of time needed to measure additional leads, measuring at least 3 leads—ideally more than 4 leads—is necessary for minimizing the risk of failing to detect acute myocardial infarction occurring in a certain spatial location or direction. Conclusions By developing an AI model for detecting acute myocardial infarction with asynchronous ECG lead sets, we demonstrated the feasibility of multiple lead-based AI-enabled ECG algorithms on smartwatches for automated diagnosis of cardiac disorders. We also demonstrated the necessity of measuring at least 3 leads for accurate detection. Our results can be used as reference for the development of other AI models using sequentially measured asynchronous ECG leads via smartwatches for detecting various cardiac disorders.

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“…Einthoven demonstrated significant differences in normal and abnormal ECG waveforms in 1906 [25] and 1908 [26].…”

Section: A Willem Einthoven's String Galvanometermentioning

confidence: 97%

Origins of ECG and Evolution of Automated DSP Techniques: A Review

Arora¹,

Mishra

2021

IEEE Access

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Over the years researchers have studied the evolution of Electrocardiogram (ECG) and the complex classification of cardiovascular diseases. This review focuses on the evolution of the ECG and covers the most recent signal processing schemes with milestones over the last 150 years in a systematic manner. Development phases of ECG, ECG leads, portable ECG monitors, Signal Processing schemes and Complex Transformations are discussed. This paper summarizes the development of ECG features detection for cardiac anomalies and the history of the development of ECG monitors, beginning from String Galvanometer. It also discusses the automated detections on ECG beginning from 1960 to the most recent signal processing techniques. Additionally, this paper provides recommendations.

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Weiteres über das Elektrokardiogramm

Cited by 3 publications

References 4 publications

Evaluation of a web-based ECG-interpretation programme for undergraduate medical students

Evaluation of a web-based ECG-interpretation programme for undergraduate medical students

Automated Detection of Acute Myocardial Infarction Using Asynchronous Electrocardiogram Signals—Preview of Implementing Artificial Intelligence With Multichannel Electrocardiographs Obtained From Smartwatches: Retrospective Study

Origins of ECG and Evolution of Automated DSP Techniques: A Review

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