6. Conclusions

Figure 17. Power spectral density of 10-s ECG signal of a patient with supraventricular arrhythmia, (a) unanonymised ECG and (b) anonymised ECG. The sampling frequency was fs = 250 Hz, the power spectral method was Welch

Figure 18. Time-domain representation of the first node cð Þ 2; 0 coefficients for the 10-s abnormal ECG signal in Figure 10

periodogram.

60 Wavelet Theory and Its Applications

(a). This node was used to create the secure key.

A generalised wavelet packet-based ECG anonymisation framework has been presented in this chapter. This proposed anonymisation technique was used to conceal fiducial and non-fiducial features from normal and abnormal ECG signal for secure transmission over the public internet. Normal and abnormal ECG signals with different sampling frequencies have been investigated by the proposed method. Signal transformations other than wavelet packet transform can be used in this framework. Such transformations should have inverse property.

The performance analysis revealed that the proposed method is able to conceal both fiducial and non-fiducial features in normal and abnormal ECG signals under examination. Moreover, the analysis showed that the reconstructed ECG is highly correlated with the original ECG signal. It achieved a lossless reconstruction of the ECG data and proved the robustness of the proposed method. The security measures taken to secure the key and other information such as the level of decomposition and the knowledge of the reversible function make attacks using methods such as brute force is infeasible.
