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SUMMARYWireless patient monitoring is an active research area with the goal of ubiquitous health care services. This study presents a novel means of exploiting the distributed source coding (DSC) in low-complexity compression of ECG signals. We first convert the ECG data compression to an equivalent channel coding problem and exploit a linear channel code for the DSC construction. Performance is further enhanced by the use of a correlation channel that more precisely characterizes the statistical dependencies of ECG signals. Also proposed is a modified BCJR algorithm which performs symbol decoding of binary convolutional codes to better exploit the source's a priori information. Finally, a complete setup system for online ambulatory ECG monitoring via mobile cellular networks is presented. Experiments on the MIT-BIH arrhythmia database and real-time acquired ECG signals demonstrate that the proposed system outperforms other schemes in terms of encoder complexity and coding efficiency.

Packet delay and loss are two essential problems to realtime voice transmission over IP networks. In the proposed system, the playout delay is adaptively adjusted based on a simplified version of the conversational-quality E-model. Perceptual-based buffer design is formulated as an unconstrained optimization problem leading to a better balance between end-to-end delay and packet loss. Experimental results show that the proposed playout buffer algorithm can achieve the optimum perceived speech quality under various network conditions.