The Electrocardiogram (ECG) has been established as a powerful diagnostic tool in medicine which provides important information about the patient's heart condition. The correct identification of the QRS complexes is a fundamental step in every automated or semi-automated ECG analysis method. A major problem that is often encountered in automatic QRS detection is the presence of artifacts in the ECG data, which cause considerable alterations to the signal. In this work, the objective was to develop a method, based on Time-Frequency Analysis (TFA), which would be able to automatically detect and remove artifacts in order to increase the reliability of automatic QRS detection. The TFA method used for the analysis of the ECG data, was based on a time-varying Autoregressive (AR) model whose solutions were obtained using Burg's method. The algorithm could detect and remove 95.6% of the artifact areas and correctly identify 92.0% of QRS complexes (322 out of 335 annotated QRS complexes). The proposed method was compared with one of the most commonly used methods in ECG analysis, which is based on the use of wavelets. The wavelet-based method resulted in an accuracy of QRS detection of 65.3% mainly due to the large number of false positive detections in the regions of artifact.