Compression of the ECG by Prediction or Interpolation and Entropy Encoding

Abstract

Compression of digital electrocardiogram (ECG) signals is desirable for two reasons: economic use of storage space for data bases and reduction of the data transmission rate for compatibility with telephone lines. In a sample of 220 Frank4ead ECG's the removal of signal redundancy by second-order prediction or interpolation with subsequent entropy encoding of the respective residual errors was investigated. At the sampling rate of 200 Hz, interpolation provided a 6 dB smaller residual error variance than prediction. A near-optimal value for the interpolation coefficients is 0.5, permitting simple implementation of the algorithm and requiring a word length for arithmetic processing of only 2 bits in extent of the signal precision. For linear prediction, the effects of occasional transmission errors decay exponentially, whereas for interpolation they do not, necessitating error control in certain applications. Encoding of the interpolation errors by a Huffman code truncated to ±5 quantization levels of 30 μV, required an average word length of 2.21 bits/sample (upper 96 percentile 3 bits/sample), resulting in data transmission rates of 1327 bits/s (1800 bits/s) for three simultaneous leads sampled at the rate of 200 Hz. Thus, compared with the original signal of 8 bit samples at 500 Hz, the average compression is 9:1. Encoding of the prediction errors required an average wordlength of 2.67 bits/sample with a 96 percentile of 5.5 bits/sample, making this method less suitable for synchronous transmission.