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The performance of several adaptive filter (AdF) algorithm implementations was investigated in the context of cleaning noisy ambulatory ECGs. Together with a noisy ECG signal, both body movement measured with accelerometers and skin-electrode impedance (SEI) were considered as reference signals to the AdF. ECG with artificial motion artifacts were generated by combining clean ECGs with noise signals. Several implementations and combinations of AdFs, and two reference signals (accelerometers and SEI) were investigated. Performance was measured by evaluating the output (sensitivity (Se) and positive predictivity (+P)) of a beat detection (BD) algorithm. Using AdF algorithm improved the performance of a BD algorithm as compared to non-filtering. SEI used as reference signal outperformed accelerometers. A variant of LMS, LMS sign-error, gave the best performance from all implementations considered. However, distortion observed in the filtered signal is high and therefore, these results cannot be extended to other features within the ECG.