OBJECTIVE: Use of wearable ECG devices for arrhythmia screening is limited due 
to poor signal quality, small number of leads and short records, leading to 
incorrect recognition of pathological events. This paper introduces a novel 
approach to classification (normal/'N', atrial fibrillation/'A', other/'O', and 
noisy/'P') of short single-lead ECGs recorded by wearable devices.
APPROACH: Various rhythm and morphology features are derived from the separate 
beats ('local' features) as well as the entire ECGs ('global' features) to 
represent short-term events and general trends respectively. Various types of 
atrial and ventricular activity, heart beats and, finally, ECG records are then 
recognised by a multi-level approach combining a support vector machine (SVM), 
decision tree and threshold-based rules.
MAIN RESULTS: The proposed features are suitable for the recognition of 'A'. The 
method is robust due to the noise estimation involved. A combination of radial 
and linear SVMs ensures both high predictive performance and effective 
generalisation. Cost-sensitive learning, genetic algorithm feature selection and 
thresholding improve overall performance. The generalisation ability and 
reliability of this approach are high, as verified by cross-validation on a 
training set and by blind testing, with only a slight decrease of overall 
F1-measure, from 0.84 on training to 0.81 on the tested dataset. 'O' recognition 
seems to be the most difficult (test F1-measures: 0.90/'N', 0.81/'A' and 
0.72/'O') due to high inter-patient variability and similarity with 'N'.
SIGNIFICANCE: These study results contribute to multidisciplinary areas, 
focusing on creation of robust and reliable cardiac monitoring systems in order 
to improve diagnosis, reduce unnecessary time-consuming expert ECG scoring and, 
consequently, ensure timely and effective treatment.