Adaptive Sampling of the Electrocardiogram Based on Generalized Perceptual Features.

Piotr Augustyniak

doi:10.3390/s20020373

Abstract

A non-uniform distribution of diagnostic information in the electrocardiogram (ECG) has been commonly accepted and is the background to several compression, denoising and watermarking methods. Gaze tracking is a widely recognized method for identification of an observer’s preferences and interest areas. The statistics of experts’ scanpaths were found to be a convenient quantitative estimate of medical information density for each particular component (i.e., wave) of the ECG record. In this paper we propose the application of generalized perceptual features to control the adaptive sampling of a digital ECG. Firstly, based on temporal distribution of the information density, local ECG bandwidth is estimated and projected to the actual positions of components in heartbeat representation. Next, the local sampling frequency is calculated pointwise and the ECG is adaptively low-pass filtered in all simultaneous channels. Finally, sample values are interpolated at new time positions forming a non-uniform time series. In evaluation of perceptual sampling, an inverse transform was used for the reconstruction of regularly sampled ECG with a percent root-mean-square difference (PRD) error of 3–5% (for compression ratios 3.0–4.7, respectively). Nevertheless, tests performed with the use of the CSE Database show good reproducibility of ECG diagnostic features, within the IEC 60601-2-25:2015 requirements, thanks to the occurrence of distortions in less relevant parts of the cardiac cycle.

Highlights

Huge amounts of ECG data are nowadays collected worldwide due to achievements made in the storage of media technology during the last decade
In evaluation of perceptual sampling, an inverse transform was used for the reconstruction of regularly sampled ECG with a percent root-mean-square difference (PRD) error of
Unlike in many signal compression methods or compressed sensing based on statistical description of information density, in our method the local information distribution is estimated on the background of perceptual studies [19,20]

Summary

Introduction

Huge amounts of ECG data are nowadays collected worldwide due to achievements made in the storage of media technology during the last decade. The scientific problem of intelligent adaptive coding remains valid [1,2,3,4,5] and, in the context of cardiac-based home care and surveillance, smart solutions have considerable impact on performance and costs. As such systems commonly use a wireless link, millions of recording hours are difficult to manage and cause high expenses for data transmission [6,7,8]. In the remaining part of the heartbeat, the discrete time series is significantly oversampled causing high correlation of neighboring samples unless blurred with noise

Methods

Results

Conclusion