Adaptive vector quantisation for electrocardiogram signal compression using overlapped and linearly shifted codevectors

Abstract

A discrete semi-periodic signal can be described as x(n)=x(n+T+ΔT) +Δx,∀n, where T is the fundamental period, ΔT represents a random period variation, and Δx is an amplitude variation. Discrete ECG signals are treated as semi-periodic, where T and Δx are associated with the heart beat rate and the baseline drift, respectively. These two factors cause coding inefficiency for ECG signal compression using vector quantisation (VQ). First, the periodic characteristic of ECG signals creates data redundancy among codevectors in a traditional two-dimensional codebook. Secondly, the fixed codevectors in traditional VQ result in low adaptability to signal variations. To solve these two problems simultaneously, an adaptive VQ (AVQ) scheme is proposed, based on a one-dimensional (1D) codebook structure, where codevectors are overlapped and linearly shifted. To further enhance the coding performance, the Δx term is extracted and encoded separately, before 1D-AVQ is applied. The data in the first 3 min of all 48 ECG records from the MIT/BIH arrhythmic database are used as the test signals, and no codebook training is carried out in advance. The compressed data rate is 265.2±92.3 bits s⁻¹ at 10.0±4.1% PRD. No codebook storage or transmission is required. Only a very small codebook storage space is needed temporarily during the coding process. In addition, the linearly shifted nature of codevectors makes this easier to be hardware implemented than any existing AVQ method.