The performance of wearable Photoplethysmogram (PPG) sensors is highly influenced by noise. This thesis describes the methods and results of designing a filtering systemfor PPG in context of Atrial Fibrillation (AF) detection. The developed work is an adaptive filtering system combined with a robust heart rate detection mechanism for validation of the proposed method. Additionally, the heart rate estimation can potentially be used to detect AF episodes using machine learning. Research has been done regarding an optimal reference signal for the adaptive filtering structure. Accelerometer data, being commonly used as reference signal for the noise did not showgood correlationwith the motion induced artefacts in the signal. Therefore, a reference for the signal component is generated from the PPG itself, which is achieved by applying a narrow bandpass filter. Here the center frequency is determined from an autocorrelation of the signal in a sliding-window. The optimal settings for the sliding window in AF context were found to be 2 seconds with 80% overlap. Furthermore, a comparison is made between NLMS, RLS and Kalman adaptive algorithms, in which RLS showed the best overall performance. The validation of the filtering structure is based on peak detection from the enhanced signal compared with the ECG reference peaks. The results indicates that the system significantly improves the heart rate error in signal disturbed by noise and during AF episodes.