Correction : Billeci , L . ; et al . Automatic Detection of Atrial Fibrillation and Other Arrhythmias in ECG Recordings Acquired by a Smartphone Device . Electronics 2018 , 7 , 199

Lucia Billeci 1,* , Magda Costi 2, David Lombardi 2, Franco Chiarugi 3 and Maurizio Varanini 1 1 Institute of Clinical Physiology, National Research Council of Italy, via Moruzzi 1, 56124 Pisa, Italy; maurizio.varanini@ifc.cnr.it 2 Cardioline S.p.A., via Linz 19-21-21, 38121 Spini di Gardolo (TN), Italy; m.costi@cardioline.it (M.C.); d.lombardi@cardioline.it (D.L.) 3 Dedalus S.p.A., Via Gaetano Malasoma, 24, 56121 Pisa, Italy; frch07@hotmail.com * Correspondence: lucia.billeci@ifc.cnr.it; Tel.: +39-050-315-2354

The authors wish to make the following corrections to our published paper [1].The scores, shown in the last column of Table 3, refer to the follow-up phase of the CinC 2017 challenge, and may have been obtained with methods and features different from those shown in columns 3 and 2, which refer to Challenge phase and are described in the cited articles published on CinC 2017 Proceedings.In particular: The score F 1 = 0.848 of Teijero et al. was obtained not using 79 features, as shown in the table, but 42 features and the score F 1 = 0.8278 of Plesinger et al. was obtained using not 277, but 60 features.These data are extracted from their recently published articles on Physiological Measurements.
Furthermore, it must be specified that the applied classification method of Teijero et al. is not a Recurrent Neural Network, but an ensemble of a Gradient Boosting classifier and a Recurrent Neural Network.
As for the other two items in the table (Kropf et al., Datta et al.), we have no information on any changes in the follow-up respect to the challenge phase.
In summary, on page 9, Table 3 should be changed from: Notably, our performance on the hidden test set of the official challenge phase was: F 1n = 0.911; F 1a = 0.784; F 1o = 0.739 and F 1 = 0.812 (obtaining the 12th place in the official ranking list).The increase in performance achieved from the official phase to the follow-up phase suggests the importance of having reliable annotations for the algorithm training and evaluation.
to the following correct version: Notably, our performance on the hidden test set of the official challenge phase was: F 1n = 0.911; F 1a = 0.784; F 1o = 0.739 and F 1 = 0.812 (obtaining the 12th place in the official ranking list).The increase in performance achieved from the official phase to the follow-up phase suggests the importance of having reliable annotations for the algorithm training and evaluation.
On page 14, reference 40 and reference 43 should be changed from:

Table 3 .
Comparison of the results of the proposed methods with that of the other algorithms proposed for the Physionet Challenge 2017 obtained in the follow-up phase.

Table 3 .
Comparison of the results of the proposed methods with that of the other algorithms proposed for the Physionet Challenge 2017 obtained in the follow-up phase.