Agent-based beat-by-beat compression of 12-lead electrocardiogram signal using adaptive Fourier decomposition

Abstract

ObjectiveThe multilead electrocardiogram (MECG) signal provides much more detailed information related to the cardiac activity of the human heart than single channel signal. Long-term recording of MECG data needs a huge amount of storage space, thus data compression becomes truly necessary. MethodsIn this work, a beat-wise MECG data compression is proposed that is based on adaptive Fourier decomposition (AFD). To reduce dimensionality, an ECG beat was treated as a multiagent, upon which principal component (PC) analysis was used in non-linear space. A new Möbius transform was introduced along with AFD, to convert the dominant PCs in complex domain using Nevanlinna factorization. An offline trained multilayer perceptron neural network was also employed to provide optimal decomposition levels in AFD to limit the PRD within 3%. ResultThe entire work was tested on 546 ptbdb MECG records available in Physionet which yielded an average compression ratio and a percent root mean squared difference (PRD) of 48.21 and 3.88, respectively. Regardless of annotation type, PRD variance within each lead was found to be nearly uniform. ConclusionWithin a single beat, the agent-based compression established a hybrid compression technique, almost completely preserving critical clinical aspects. SignificanceBeat-wise compression, therefore requires less buffer memory, allowing it to be used for real-time data compression. The low reconstruction error enhanced the acceptability of proposed work in medical applications.