Optimizing Wavelet ECG Watermarking to Maintain Measurement Performance According to Industrial Standard

Abstract

Watermarking is currently investigated as an efficient and safe method of embedding additional patient or environment-related data into the electrocardiogram. This paper presents experimental work on the assessment of the loss of ECG (electrocardiogram signal) diagnostic quality from the industrial standard EN60601-2-25:2015 point of view. We implemented an original time-frequency watermarking technique with an adaptive beat-to-beat lead-independent data container design. We tested six wavelets, six coding bit depth values (including the automatic noise-dependent one) and two types of watermark content to find the conditions that are necessary for watermarked ECG to maintain the compliance with International Electrotechnical Commission (IEC) requirements for interpretation performance. Unlike other authors, we did not assess the differences of signal values, but errors in ECG wave delineation results. The results of a total of 7300 original and watermarked 10 s ECGs were statistically processed to reveal possible interpretation quality degradation due to watermarking. Finally we found (1) the Symlet of 11-th order as the best of the wavelets that were tested; (2) the important role of ECG wave delineation and noise tracking procedures; (3) the high influence of the watermark-to-noise similarity of amplitude and values distribution and (4) the stability of the watermarking capacity for different heart rates in atrial rhythms.

Keywords: CSE Database; ECG watermarking; interpretation performance standard; time-frequency steganography; wavelets.

Figure 1

The instantaneous bandwidth of one heartbeat dividing its time-frequency representation into two parts: cardiac components and noise components with the resulting design of data containers: (a) the data itself are stored in the 1st scale of the bandgap with specified bit depth (e.g., 3 bits/sample); (b) the data description is stored in the 2nd scale of the bandgap with LSB method [17].

Figure 2

Irreversible watermarking process (a) watermark embedment; (b) watermark extraction.

Figure 3

Signals in irreversible watermarking (in this example: CSE MA001, lead I, sym6 wavelet, textual watermark in 2-bit representation): (a) clean ECG (electrocardiogram signal); (b) watermarked ECG and (c) difference due to watermarking (the amplitude scale is 100 times lower); a 47 character watermark requires 188 2-bit samples and has been divided into two data containers of adjacent heartbeats.

Figure 4

Experiment workflow.

Figure 5

Difference of time-frequency representation with use of different wavelets (example of CSE MA001). (a) clean carrier; (b) time-frequency representation with db5 wavelet; (c) time-frequency representation with db10 wavelet; (d) difference between time-frequency representations with db5 and db10 wavelets.

Figure 5

Difference of time-frequency representation with use of different wavelets (example of CSE MA001). (a) clean carrier; (b) time-frequency representation with db5 wavelet; (c) time-frequency representation with db10 wavelet; (d) difference between time-frequency representations with db5 and db10 wavelets.

Figure 6

Histograms of QRS length error distribution for 1 bit depth watermark coding (worst case) with different wavelets (top to bottom): db5, db10, sym6, sym11, bior2.4 and bior 4.4.

Figure 7

Histograms of QRS length error distribution for db5 wavelet and n = {1…5} bit depth watermark coding (top to bottom); the lowest plot shows the automatic adjustment of coding bit depth.