Recent studies have shown that cardiovascular diseases remain the leading cause of death worldwide. Since many cardiovascular conditions are characterized by event-related symptoms, the development of wearable devices for long-term electrocardiographic (ECG) monitoring has increased. However, their widespread requires robust signal processing techniques capable of operating under dynamic conditions. In real-life scenarios, body movements introduce motion artifacts and muscle interference that degrade ECG signal quality, affecting R-peak detection, heart rate variability (HRV) metrics, and respiratory rate (RR) estimation. This work proposes a robust signal processing pipeline for extracting cardiovascular and respiratory parameters from wearable ECG recordings. The framework includes ECG denoising based on bandpass filtering combined with singular value decomposition (SVD), followed by R-peak detection and subsequent extraction of relevant time-domain HRV metrics.