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A Multilayer CNN Using the ECG, Age and Sex Predicts Ventricular Arrhythmias in the General Population

文献类型： 会议论文

第一作者： Julia Ramírez

作者： Julia Ramírez 1 ; Antonio Miguel 2 ; Stefan van Duijvenboden 3 ; Michele Orini 4 ; William J Young 5 ; Andrew Tinker 6 ; Pier D Lambiase 5 ; Patricia B Munroe 6 ; Juan Pablo Martínez 7 ;

作者机构： 1.Instituto de Investigación en Ingeniería de Aragón, IIS-Aragón, University of Zaragoza, Spain|Biomedical Research Networking Centre on Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain|Queen Mary University of London, London, United Kingdom|Aragon Institute of Engineering Research, University of Zaragoza, Zaragoza, Spain

2.Instituto de Investigación en Ingeniería de Aragón, IIS-Aragón, University of Zaragoza, Spain

3.Queen Mary University of London, London, United Kingdom|University of Oxford, Oxford, United Kingdom

4.University College London, London, United Kingdom

5.Queen Mary University of London, London, United Kingdom|St Bartholomew's Hospital, London, United Kingdom

6.Queen Mary University of London, London, United Kingdom

7.Instituto de Investigación en Ingeniería de Aragón, IIS-Aragón, University of Zaragoza, Spain|Biomedical Research Networking Centre on Bioengineering, Biomaterials and Nanomedicine, Zaragoza, Spain

关键词： Training;Sensitivity;Arrhythmia;Wearable computers;Sociology;Electrocardiography;Lead

会议名称： Computing in Cardiology Conference

主办单位：

页码： 1-4

摘要： Life-threatening ventricular arrhythmias (LTVA) prediction in individuals without cardiovascular disease remains a major challenge. We tested the performance of a multilayer convolutional neural network (CNN) using ECG signals, age and sex. We split 86,603 individuals from the UK Biobank study into a training (90%) and a test (10%) set. In the training set, we trained a multilayer CNN using 15-second ECGs at rest from lead I, age and sex as inputs. The output was the probability of LTVA within a 12-year follow-up. The CNN model consisted of a four-layer CNN (128, 128, 256 and 256 channels, kernel sizes of 3) and a single attention layer. Age and sex were included as external inputs to the final layer. In the test set (0.9% LTVA events), the CNN's prediction led to a median AUC of 0.601, and a specificity of 0.287 for a sensitivity of 0.750. We set a threshold at the CNN's prediction value maximising the sum of specificity and sensitivity in the training set. Survival analyses showed a hazard ratio (HR) of $1.396(P=0.021)$ for individuals with a CNN's prediction value > threshold, versus those with a CNN's prediction value < threshold. A multilayer CNN model using 10-second ECG data from lead I, together with information on age and sex, can stratify individuals at risk of LTVA. Our findings support the potential utility of wearables for accessible screening in the general population.

分类号： tp3

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