Original adaptive line enhancer (ALE) is used for denoising periodic signals from white noise. ALE, however, relies mainly on second order similarity between the signal and its delayed version and is more effective when the signal is narrowband. A new ALE based on singular spectrum analysis (SSA) is proposed here. In this approach in the reconstruction stage of SSA, the eigentriples are adaptively selected (filtered) using the delayed version of the data. Unlike the conventional ALE where (second) order statistics are taken into account, here the full eigen-spectrum of the embedding matrix is exploited. Consequently, the system works for non-Gaussian noise and wideband periodic signals. By performing some experiments on synthetic signals it is demonstrated that the proposed system is very effective for separation of biomedical data, which often have some periodic or quasi-periodic components, such as EMG affected by ECG artefacts. This data are examined here.

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Continual assessment of rehabilitation progress is necessary to enhance the effectiveness of therapy. In a previous work [1] this has been addressed by looking into the eigenvalues of the arm movement signals using singular spectrum analysis (SSA). The method however ignores the effect of data nonstationarity which includes at least three different trajectory segments. In this paper, the above work is refined by separating the effective signal segments using time-frequency transform before applying the SSA. The automation of Action Research Arm Test (ARAT), which is widely used as a valid and effective test to monitor the rehabilitation effects, is the objective of this paper. The proposed SSA is informed by the timefrequency properties of the data, resulting in a more accurate selection of eigenvalues to aid in signal filtering. The improvement over our previous attempt is evident and very promising.