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Sudden cardiac death (SCD) is one of the most prominent causes of death among patients with cardiac diseases. Since ventricular arrhythmia is the main cause of SCD and it can be predicted by T wave alternans (TWA), the detection of TWA in the body-surface electrocardiograph (ECG) plays an important role in the prevention of SCD. But due to the multi-source nature of TWA, the nonlinear propagation through thorax, and the effects of the strong noises, the information from different channels is uncertain and competitive with each other. As a result, the single-channel decision is one-sided while the multichannel decision is difficult to reach a consensus on. In this paper, a novel multichannel decision-level fusion method based on the Dezert-Smarandache Theory is proposed to address this issue. Due to the redistribution mechanism for highly competitive information, higher detection accuracy and robustness are achieved. It also shows promise to low-cost instruments and portable applications by reducing demands for the synchronous sampling. Experiments on the real records from the Physikalisch-Technische Bundesanstalt diagnostic ECG database indicate that the performance of the proposed method improves by 12%-20% compared with the one-dimensional decision method based on the periodic component analysis.

A multichannel decision-level fusion method for T wave alternans detection