Atrial fibrillation (AF) is a common health issue, not only in developed countries but also in developing ones. AF can lead to strokes, heart failures, and even death if it is not diagnosed and treated on time, therefore automatic detection of AF is an urgent need, particularly using Internet- connected devices that can alert healthcare services. Detection of AF typically involves the analysis of electrocardiogram (ECG) recordings, where P-waves that characterize the atrial activity are substituted with f-waves of variable amplitude and duration. In this paper, we used the discrete wavelet transform to decompose the ECG signal into detail and approximation coefficients with different time-frequency resolutions. Features extracted from ECG signals, RR interval time series and detail and approximation coefficients were used as inputs to an artificial neural network trained to identify four classes of heart rhythms: normal sinus rhythm (NSR), AF, other rhythms (OR) and noisy signals (NS). By performing a Monte Carlo 10- fold cross-validation of 10 iterations approach, average micro F₁ scores of 83.64%, 61.61%, 56.88% and 53.88% to classify NSR, AF, OR and NS respectively, and average macro F₁ of 64.00% were obtained on the publicly available training set of PhysioNet/Computing in Cardiology Challenge 2017. In addition, in a one-vs.-the-rest strategy, i.e., AF-vs-the-rest, averages sensitivity and specificity of 95.70% and 72.39% respectively were achieved to classify AF recordings.