The advent of wearable recorders poses new challenges to electrocardiogram (ECG) analysis, such as robust feature extraction in front of long-term recordings with intervals of extreme noise. This paper proposes a robust approach to improve the estimates of one particular feature, the R-R interval (RRI), extracted by an arbitrary QRS detector operating in these scenarios. The proposal performs three steps. First, a voting schema is used to detect noisy intervals. Second, a rough estimate of the RRI evolution with time is obtained. Finally, this estimate is used to guide the Kalman filter in charge of refining the RRI estimates. Two groups of experiments have been performed. The first relies on 1674 real ECG corrupted with controlled amounts of noise. The second one tests our proposal using the MIT-BIH Noise Stress Test Database. Results show that our approach is barely influenced by the initial error, leading to a large improvement in front of highly corrupted electrocardiograms at the cost of reducing the quality of the RRI estimates in absence of significant noise. Accordingly, the presented approach is suitable to process data obtained from portable ECG devices in which localized intervals of severe noise are present.