Detection of atrial fibrillation (AFib) using wearable ECG monitors has recently gained popularity. The signal quality of such recordings is often much lower than that of traditional monitoring systems such as Holter monitors. Larger noise contamination can lead to reduced accuracy of the QRS detection which is the basis of the heart rate variability (HRV) analysis. Hence, it is crucial to accurately classify short ECG recording segments for AFib monitoring. A comparative study was conducted to investigate the applicability and performance of a variety of HRV feature extraction methods applied to short single lead ECG recordings to detect AFib. The data employed in this study is the publicly available dataset of the 2017 PhysioNet challenge. In particular, detection of AFib against non-AFib instances, including normal sinus rhythm, other types of arrhythmias and noisy signals, is investigated in this study. The HRV features can be divided into the categories of statistical, geometrical, frequency, entropy, Poincare plotand Lorentz plot-based. For feature selection, stepwise forward selection approach was employed and support vector machines with linear and radial basis function kernels were used for classification. The results indicate that a combination of features from all the categories leads to the highest accuracy levels. The feasibility of using different HRV features for short signals is discussed as well. In conclusion, AFib can be detected with high accuracy using short single-lead ECG signals using HRV features.