Compression of ECG Signal Using Neural Network Predictor and Huffman Coding

Medical signals and images need special treatment especially when the data become bigger and bigger. One of the treatment that will be considered in this experiment is about the data compression. ECG (Electro Cardio Graph) signals will create very big data when the signals were collected in a long period of time. Several methods can be used to compress the ECG data. In this experiment we used neural network to predict the incoming data and huffman coding to minimize the codes. The ECG data was collected from MIT-BIH arrhythmia database. The experiment gave low compression rasio when the predicted data was very close to the incoming data

A Nonlinear Dynamical Model For Compression And Detection Of Ecg Data

We propose a low-dimensional nonlinear model explaining the ECG dynamics, suitable for data compression and possibly feature detection. Tests on real clinically measured ECG signals confirmed very good performance of the model in terms of modeling errors and compression ratio. 1 INTRODUCTION The Electrocardiogram (ECG) is a recording (measurement) of the electrical activity generated by the heart carried out using sensors positioned on the body surface. Analysis of this signal provides the most common non-invasive method to diagnose cardiac disfunctions. With the development of computerized electrocardiography a wide range of applications have been already implemented, e.g. ambulatory ECG for the detection of heart block transients, real time patient monitoring in coronary care units and intensive care units, etc... The recent advances in the development of mobile communications is now making feasible and affordable the concept of long distance real time patient monitoring. However, ..