Table Info

Table 2.

Main features of AF prediction studies based on ECGs in sinus rhythm

Input data	Model	N ECGs	N AF ECGs	Time to onset of AF	AUC (%)
12 lead ECG [18]	CNN	80,125	13,824	< 31 days	87
12 lead ECG [19]	DNN	1,632,487	9,377	< 1 year	85
12 lead ECG [20]	RNN	1,057	1,355	< 1 day	79
12 lead ECG [21]	CNN	45,770	2,171	< 5 years	82
12 lead ECG [22]	CNN	907,858	28,117	> 31 days	86
12 lead ECG [13]	ResNet	669,782	22,695	< 5 years	78
2 lead ECG [23]	CNN	29,884	3,307	< 15 days; > 1 day	79
1 lead ECG [24]	CNN	169,013	13,553	NA	62
1 lead ECG [24]	CNN	166,447	15,052	NA	62
1 lead ECG [24]	CNN	143,503	10,951	NA	80

AF: atrial fibrillation; ECGs: electrocardiograms; N ECGs: number of ECGs; AUC: area under the receiver operating characteristic curve; CNN: convolutional neural network; DNN: deep neural network; RNN: recurrent neural network; ResNet: residual network; NA: not available

Declarations

Author contributions

JMG: Conceptualization, Investigation, Writing—original draft, Writing—review & editing. CG and FM: Software, Validation, Funding acquisition. HB and SC: Validation, Writing—review & editing, Supervision. All authors read and approved the submitted version.

Conflicts of interest

The authors declare that they have no conflicts of interest.

Ethical approval

Not applicable.

Consent to participate

Not applicable.

Consent to publication

Not applicable.

Availability of data and materials

Not applicable.

Funding

This work was supported in part by the French Community of Belgium [FRIA funding: FC 038733]; this project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska Curie grant agreement [No 101034383]. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright

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