Standard compressive sensing (CS) scenario assumes a single sparsifying basis used to reconstruct the signals from a small set of incoherent measurements. However, in many cases, the signal cannot be sparsely represented using a single transformation. Particularly, in ECG signal analysis, each signal segment is specific in nature and reflects different physical phenomena. Hence, using the same transformation for all segments may be inappropriate for efficient analysis and reconstruction. Moreover, in the CS scenario, it would be necessary to combine different transforms to achieve compact signal support and to provide successful reconstruction from randomly under-sampled data. This work proposes a hybrid CS reconstruction algorithm that combines different transform basis, based on the concept of orthogonal matching pursuit. The performance of the proposed approach is verified experimentally using the combination of the Fourier and the Hermite transform on the real ECG signals.

标准压缩感测（CS）场景假设使用一个稀疏基础，用于从一小组不相干的测量结果中重建信号。但是，在许多情况下，无法使用单个变换来稀疏表示信号。特别是，在ECG信号分析中，每个信号段本质上都是特定的，反映了不同的物理现象。因此，对所有段使用相同的变换可能不适用于有效的分析和重建。此外，在CS场景中，有必要组合不同的变换以实现紧凑的信号支持并从随机欠采样的数据中获得成功的重建。这项工作基于正交匹配追踪的概念，提出了一种结合了不同变换基础的混合CS重建算法。