Efficient ECG Signal Compression Using Adaptive Heart Model.

摘要

This paper presents an adaptive, heart-model-based electrocardiography (ECG) compression method. After conventional pre-filtering the waves from the signal are localized and the model's parameters are determined. The structure of the algorithm allows real-time adaptation to the heart's state. The compression, for better comparison, was performed for one and more channels from the MIT/BIH database samples. The compression ratio depends on the maximal allowed root mean square reconstruction error (RMSRE). As a second classification criterion the authors applied the performance of the signal detection method from the compacted data. They used an adaptive entropy encoder to reduce the redundancy. The major advantage of this method is that it allows the possibility of accomplishing a real-time, adaptive, and patient- specific encoding with relatively low computational power, which would be ideal for telemetry measurements.