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Energy efficiency is still the obstacle for long-term real-time wireless ECG monitoring. In this paper, a digital compressed sensing- (CS-) based single-spot Bluetooth ECG node is proposed to deal with the challenge in wireless ECG application. A periodic sleep/wake-up scheme and a CS-based compression algorithm are implemented in a node, which consists of ultra-low-power analog front-end, microcontroller, Bluetooth 4.0 communication module, and so forth. The efficiency improvement and the node’s specifics are evidenced by the experiments using the ECG signals sampled by the proposed node under daily activities of lay, sit, stand, walk, and run. Under using sparse binary matrix (SBM), block sparse Bayesian learning (BSBL) method, and discrete cosine transform (DCT) basis, all ECG signals were essentially undistorted recovered with root-mean-square differences (PRDs) which are less than 6%. The proposed sleep/wake-up scheme and data compression can reduce the airtime over energy-hungry wireless links, the energy consumption of proposed node is 6.53 mJ, and the energy consumption of radio decreases 77.37%. Moreover, the energy consumption increase caused by CS code execution is negligible, which is 1.3% of the total energy consumption.

As a new signal processing tool, a Modified frequency slice wavelet transform (MFSWT) is proposed for physiological signal time-frequency analysis in this study. The transform generates time-frequency representation from the frequency domain, and the reconstruction is independent of frequency slice function (FSF). To realize accurate time-frequency location of signal components, a bound signal-adaptive FSF was introduced to serve as a dynamic frequency filter for the transform. This method avoids troublesome parameter selection, is signal-adaptive and easy to use. The results of two case studies demonstrate the validity of the proposed method, which has good interpretability with high time-frequency resolution, and hence has significant potential for bio-signal time-frequency analysis.