A wavelet-based denoising technique is investigated for suppressing EMG noise and motion artifact in ambulatory ECG. EMG noise is reduced by thresholding the wavelet coefficients using an improved thresholding function combining the features of hard and soft thresholding. Motion artifact is reduced by limiting the wavelet coefficients. Thresholds for both the denoising steps are estimated using the statistics of the noisy signal. Denoising of simulated noisy ECG signals resulted in an average SNR improvement of 11.4 dB, and its application on ambulatory ECG recordings resulted in L(2) norm and max-min based improvement indices close to one. It significantly improved R-peak detection in both the cases.