A hybrid Compressed Sensing and Primal-Dual Wavelet (CSP-PDW) technique is proposed for the compression and reconstruction of ECG signals. The compression and reconstruction algorithms are implemented using four key concepts: Sparsifying Basis, Restricted Isometry Principle, Gaussian Random Matrix, and Convex Minimization. In addition to the conventional compression sensing reconstruction approach, wavelet-based processing is employed to enhance reconstruction efficiency. A mathematical model of the proposed algorithm is derived analytically to obtain the essential parameters of compression sensing, including the sparsifying basis, measurement matrix size, and number of iterations required for reconstructing the original signal and determining the type and level of wavelet processing. The low time complexity of the proposed algorithm makes it an ideal candidate for ECG monitoring systems in IoT-based e-healthcare applications. A feature extraction algorithm is also developed to show that the important ECG peaks remain unaltered after reconstruction. The clinical relevance of the reconstructed signal and the efficiency of the developed algorithm are evaluated using four validation parameters at three different compression ratios.