A new approach of ECG steganography and prediction using deep learning

In this paper, a new approach of ECG steganography of hiding patient's confidential information is proposed. As steganography results in distortion within the ECG signal which hampers the clinical features, in this work, encryption was performed within TP-segment of ECG. Additionally, segment classification and feature extraction were used for data concealing within normal TP-segments, while keeping abnormal segments unaffected. To reduce the computational complexity and execution time, encryption was performed in time domain signal, using a new approach. Finally, after decryption of hidden data, to predict original sample values of modified TP-segments, a long short-term memory recurrent neural network (LSTM-RNN) was used which efficiently reduced the error between the original and predicted signal. This algorithm was successfully implemented on mitdb, ptbdb and European ST-T database, available in physionet and percent root mean square difference (PRD), PRD normalized (PRDN) were obtained less than 1% along with signal to noise ratio (SNR) and peak SNR (PSNR) more than 80 dB. It was observed that this algorithm provided better result among other frequency domain techniques and recently published works.