An improved ECG data compression scheme based on ensemble empirical mode decomposition

In recent years, electrocardiogram (ECG) monitoring has become the most effective method of monitoring cardiac rhythm in critically ill patients. It can detect a variety of arrhythmias, including atrial and ventricular premature beats, myocardial perfusion, etc. Nevertheless, the transmission and storage of large amounts of physiological data is a major challenge. To maintain signal integrity and increase transmission speed, data compression is necessary. Current research is increasingly focused on adaptive compression algorithms. These algorithms adapt coding strategies based on signal characteristics. ECG data compression technique combining empirical mode decomposition (EMD) and discrete wavelet transform (DWT) has been proposed. However, the intrinsic mode functions (IMFs) component generated from EMD decomposition suffers from a mode mixing problem. This paper proposes a scheme for decomposing ECG signals using ensemble empirical mode decomposition (EEMD) and recombining the components with DWT. The scheme compresses and quantizes the ECG signal using a uniform scalar dead-zone quantization method and further compresses the data using run-length coding. Evaluation parameters indicate that the proposed scheme has superior compression performance. Compressed signals can facilitate remote transmission and real-time monitoring, providing patients with more convenient medical services and promoting the development of healthcare.