An Improved ECG Steganography Using Integer Wavelet Transform and Offset Coecient with LSB Substitution

. In this work, we present an improved steganography for electrocardiogram (ECG) hosts to solve the issues of existing ECG steganographic methods, which have less hiding capacity and insufficient signal-to-noise ratio (SNR)/ peak SNR (PSNR). Based on the integer wavelet transform (IWT) domain, sensitive (or private) data such as patients’ data and personal information can be efficiently embedded in an ECG host via the IWT coefficient adjustment and the least significant bit (LSB) technique. Simulations confirmed that the SNR/ PSNR, and payload of the proposed method outperform those of existing techniques. In addition, the proposed method is capable of resisting attacks, such as cropping, Gaussian noise-addition inversion, scaling, translation, and truncation attacks from third parties (or adversaries). Due to the fast computation time, the proposed method can be employed in portable biometric devices or wearable electronics.


Introduction
Recently, the COVID-19 pandemic has caused organizations and individuals to stay at home and work via the Internet. One of the important services provided by the Internet is the medical treatment of the elderly care and personal health maintenance. For example, elders who need help from families or individuals can evaluate their own biometric status, such as electrocardiogram (ECG), glucose, and blood pressure, and transmit the sensitive measurement data to community hospitals (medical centers) through intelligent networks. Because data packets are prone to be eavesdropped and tampered during transmissions between senders and receivers via public networks, care must be taken to ensure the security (or privacy) of the sensitive data. Typically, data hiding techniques provide an economic and easy way to achieve this goal. Data hiding can be classified into steganography and digital watermarking [1][2]. One main goal of steganography [3][4] is to embed a large volume of data in host multimedia, such as text, images, or video, while maintaining a good (or acceptable) perceived quality. Generally, a good perceived quality attracts no attention of third parties (or adversaries). By contrast, robust performance is one of the major aims of (robust) digital watermarking [5][6]. The watermarked multimedia generated by a robust watermarking scheme often has good performance in resisting attacks. Thus, most watermarks extracted from the watermarked multimedia, which have undergone manipulation, can still be recognized. However, they often provide a limited payload size. Furthermore, some authors have proposed reversible (or lossless) data hiding techniques to completely restore the original content of (valuable) host media, such as medical or satellite images, after data extraction [7][8]. Because the proposed ECG steganography is a lossy data hiding approach, only the related articles are surveyed in the following subsection.
Recently, several research works have developed data hiding methods [9][10][11][12][13][14][15][16][17][18][19] for the protection (or privacy) of sensitive information in ECG signals. ´Swierkosz and Augustyniak [9] proposed an ECG watermarking scheme based on the discrete wavelet transform (DWT) domain. With the use of a continuous noise measurement and adaptive coding bit depth, the watermarked ECGs generated by the approach were capable of resisting noise attacks. The values of resultant percentage residual difference (PRD) ranged from 0.06 to 0.51 with a bit depth from 2 to 5, and was capable of introducing an optimal payload of approximately 6.5 Kb. Using the DWT approach with QR image decomposition, Sanivarapu et al. [10] successfully embedded patient information in an ECG host. An input ECG was first converted to a two-dimensional (2D) ECG image using the Pan-Tompkins algorithm. The 2D image was subsequently decomposed by DWT. Then, data bits can be effectively hidden into the target coefficients via DWT and QR decomposition techniques. Simulations indicated that the method was tolerant of additive white Gaussian noise (AWGN) noise attacks. In addition, the average PRD and SNR were 0.0020 and 53.81 dB with a payload size of 4 Kb.
Based on curvelet transforms, Jero and Ramu [11] developed a data hiding technique for ECG signals.
Demonstrations showed that the PRD was 0.11 with a payload size of 4 Kb, and the bit error rate (BER) was 31.84%. In addition, the BER linearly increased as the payload increased. Yang and Wang [12] employed the coefficient adjustment technique and proposed two types of ECG steganography, namely, a high-perceived quality and a high-capacity ECG steganography. Simulations indicated that the average SNR and payload for the high-perceived quality and high-capacity ECG steganography were 54 and 43 dB with payload sizes of 7 and 14 Kb, respectively. Based on the DWT and singular value decomposition, Jero et al. [13] presented a continuous ant colony optimization skill to embed patient information in a 2D ECG signal. Simulations indicated that the PRD and PSNR were 0.0018 and 62.87 dB with a payload size of 0.89 Kbytes. In addition, the method was resistant from cropping and noise (or high-frequency) attacks. To design a suitable ECG steganography for wireless transmission, Pandey et al. [14] employed a chaotic map and sample inversion technique to accomplish the goal.
Experimental results revealed that the average PRD and PSNR were 0.26 and 55.49 dB with a payload size of 21 Kbytes (the input of all datasets took 20 min).
Without the use of auxiliary information, Yang and Wang [15] utilized the absolute-value decision policy and proposed an ECG steganography. The number of input bits can be designed on-demand using host bundles in various sizes. Simulations indicated that the average payload and SNR of the method was approximately 19 Kb and 48 dB, respectively. In addition, the average SNR of 58 dB can be obtained with a payload size of 10 Kb. To further obtain a lower distortion and security, Pandey et al. [16] used a fused coupled chaotic map with the LSB technique and presented an upgraded ECG steganography. Simulations confirmed that the average PRD and PSNR were 0.21 and 56.83 dB with a payload size of 21 Kbytes (the input of all datasets took 30 min).
Furthermore, a good perceptual quality with a PSNR value of approximately 70 dB and payload size of 2.4 Kbytes can be achieved by the method. Christian et al. [17] presented an ECG steganography based on the discrete cosine transform (DCT) domain. The method embedded secret bits in the second decimal place of the DCT coefficients and generated a very high perceptual quality. Although a high SNR with high payload can be obtained by their method, the size of the resultant ECG signal is increased by about two times larger than that of the original one, which may lead to the storage burden of the mobile measurement devices.
Based on a 2D bit-embedding and bit-extraction approach, Yang et al. [18] proposed an effective ECG steganography, where a patient's data can be hidden into the host blocks of an ECG. The performance of the method was demonstrated with host blocks in various sizes. Compared with existing techniques, the method with a 2  2 block generated the best SNR and payload values, that is, 53 dB and 14 Kb, respectively, whereas the 4  4 block provided the largest payload (21 Kb) among the compared methods. The method has a merit of resisting manipulations because it embedded data bits in the blocks of a 2D ECG host. To obtain high hiding capacity and robustness performance, Yang and Wang [19] embedded data bits in the low sub-band (LB) and high sub-band (HB) coefficients of the integer wavelet transform (IWT) domain. First, an input ECG host was decomposed into the LB and HB using level 1 IWT. Then, the predetermined criteria for bit embedding/ extraction were employed to hide a secret message into both sub-bands. Simulations indicated that their average SNRs were 50 and 40 dB with payload sizes of size 21 and 25 Kb, respectively, when the control parameters  = 9 and  = 55 were used.

Research Issues
From the above survey we can see that the major issues of existing steganographic methods for ECG hosts have less hiding capacity and insufficient SNR (or PSNR). Thus, the motivation of this study is to propose an improved ECG steganography for the protection of patients' data and personal privacy.

Contributions of the Study
The major contributions of the study include: 1) The proposed method has merits of good perceived quality, high hiding capacity, and the support of robustness, which is rarely seen in conventional ECG steganography methods.
2) The hiding storage and SNR (or PSNR) provided by the proposed method are superior to those provided by existing techniques. 3) Due to the fast computation time, the proposed method can be employed in portable biometric devices or wearable electronics.
The remainder of the paper is organized as follows. Section 2 describes the proposed bit embedding and bit extraction technique and the capacity analysis. Section 3 presents the experimental results and discussions, and Section 4 concludes this work.

Proposed Method
To achieve high hiding capacity, good perceived quality, and robustness, an input ECG host was first decomposed into low sub-band coefficients (IL) and high sub-band coefficients (IH) via 1D IWT [20]. Then, a series of host blocks with a size of n  n were sequentially derived from the IL and IH. The number of 2  (n − 1) bits can be virtually embedded in the IWT coefficients of the first two rows of a host block according to the coefficient adjustment. In addition, the number of (2  n) bits can be hidden into the IWT coefficients at the last row of the host block via the LSB technique. If a sub-block of the first (or the second) row of host block failed to be used for hiding bit after the adjustment, it is referred to as a skipped sub-block. Moreover, the skipped sub-blocks carry no data bits. Because a skipped sub-block can be easily detected by the receiver using the above criteria, no auxiliary information is required to record their positions. The major steps of bit embedding and bit extraction for our proposed method are summarized in the following sections.

Bit Embedding
The main procedure of bit embedding is described in the following algorithm. Algorithm 1. Hiding data bits in the ECG host.
Input: Host ECG ,  size of a host block n, a control integer , and a secret message W.
Step 1. Set parameter m = 0 and input a block Ej = from IL (or IH). If the end of input is encountered, then go to Step 10.
Step 2. If m < (n − 1), then set index r = m  n and go to the next step; otherwise, go to Step 9.
Step 3. Compute the offset  = sjrsj(r+1), if the condition || >  is satisfied, which means that the sub-block carries no data bit, and then go to Step 6; otherwise, input a data bit bp from W.
Step 4. If both conditions of bp = 1 and -   < 0 are satisfied, which means that the sub-block carries data bit "1," and then go to Step 6. Otherwise, if bp = 1 is satisfied, which implies the occurrence of a violation, then repeatedly adjust the value of  by increasing sj(r+1) by 1 and decreasing sjr from 1 simultaneously until -   < 0 is achieved, and go to Step 6.
Step 5. If both conditions of bp = 0 and 0     are satisfied, which means that the sub-block carries data bit "0," and then go to Step 6. If bp = 0 is satisfied, which implies the occurrence of a violation, and then repeatedly adjust the value of  by increasing sjr by 1 and decreasing sj(r+1) from 1 simultaneously until the condition 0     is satisfied.
Step 7. If both conditions of bq = 1 and -   < 0 are satisfied, which means that the sub-block carries data bit "1," then set m = m + 1 and go to Step 2. Otherwise, if bq = 1 is satisfied, then repeatedly adjust the value of  by increasing sj(r+2) by 1 and decreasing both sjr and sj(r+1) from 1 simultaneously until the condition -   < 0 is satisfied. Set m = m + 1 and go to Step 2.
Step 8. If both conditions of bq = 0 and 0     are satisfied, which means that the sub-block carries data bit "0," then set m = m + 1 and go to Step 2. Otherwise, if bq = 0 is satisfied, then repeatedly adjust the value of  by increasing sjr and sj(r+1) by 1 and decreasing sj(r+2) from 1 simultaneously, until the condition 0     is met. Set m = m + 1 and go to Step 2.
Step 9. Embed (2  n) bits in the three coefficients at the last row of Ej via the LSB, and return to Step 1.
Step 10. Perform 1D inverse IWT from IL and IH to obtain mark ECG .

Bit Extraction
Bit extraction is considerably simpler than the proposed bit embedding. The major steps of bit extraction are listed in the following algorithm.
Algorithm 2. Extracting hidden bits from marked ECG.
Input: Marked ECG , size of a host block n, and an integer .
Step 1. Set parameter m = 0 and input a block Ej = 1 n 0 i ji 2 } {s'   . If the end of input is encountered, then go to Step 8.
Step 2. If m < (n − 1), then set index r = m  n and go to the next step; otherwise, proceed to Step 7.
Set m = m + 1 and return to Step 2.
Step 7. Extract (2  n) bits from the three coefficients of Ej via the LSB technique, and return to Step 1.
Step 8. Assemble all extracted bits and rebuild the secret message W.
Notably, the value of the control parameter  was not necessarily constant. When  was small, SNR increased, and the PRD and payload size decreased. Moreover, the proposed method with a large  provides more a robust performance than with any smaller . The block diagram of the bit embedding and bit extraction of the proposed method is summarized in Fig. 1

Capacity Analysis
If the number of skipped sub-blocks is null and an input ECG consists of K samples, then the optimal payload size of the proposed method is   stands for the number of host blocks. However, if we choose to embed n bits in the last row of a host block via the LSB technique, then the optimal payload of the proposed method would be In addition, our simulations have revealed that the distortion caused by embedding secret bits in IH was considerably less than that caused by embedding secret bits in IL. To further pursue high-perceived quality (or high SNR value), data bits can only be embedded in the IH sub-band with the constraint of embedding n bits in the last row of a host block via the LSB technique. However, in this case, the optimal payload (with no skipped sub-blocks) would be .
the hiding capability of this version is no more than 50% of the original payload size. Actually, this version with a host block of size 3  3 introduced approximately 30% to 45% size of the aforementioned (optimal) payload when  < 9 was used. Furthermore, data bits can be forcedly hidden into the skipped sub-blocks via the LSB technique. Although the payload size can be slightly promoted, the resultant SNR and robustness performance would be significantly degraded. The authors did not employ this methodology in the proposed method.

Experimental Results
To evaluate the performance of our method, simulations were implemented on an Intel® Core™ i5 1.  To further examine the hiding capability of each ECG host by the proposed method, the resulting payloads of these ECGs with a lower and larger value of  are illustrated in Fig. 4. The x-axis in both figures numbered from 1 to 48 stands for the 48 sets of ECG inputs. The corresponding name of the ECG input of the serial number is listed in Table 1. Figure 4(a) shows that the average hiding capacity provided by serial numbers of 1 (or ECG100), 20 (or ECG121), and 28 (or ECG205) with lower values of  ( 8) was higher than that of other ECGs. On the contrary, the average hiding capacity provided by serial numbers of 8 (or ECG107), 27 (or ECG203), 30 (or ECG208), and 34 (or ECG213) was less than that of other ECGs. Figure 4(b) presents that the average hiding storage provided by serial numbers of 9 (or ECG108), 20 (or ECG121), 29 (or ECG207), and 46 (or ECG232) with larger values of  ( 20) was higher than that of other ECGs, and the average hiding storage provided by serial numbers of 8 (or ECG107), 27 (or ECG203), and 34 (or ECG213) was less than that of other ECGs.  For a clear examination of the ECG hosts, their full waveforms and standard deviation (SD) and entropy values are given in Fig. 5. The figure shows that the SD and entropy of ECG107, ECG203, and ECG213 were significantly larger than those of other ECGs. Moreover, the value of the y-axis in these figures ranges either between −2 and 1.2 or between −4 and 3. Generally, the larger the SD/entropy, the more drastic the variation of the waves, and the less the hiding capability obtained by the proposed method, and vice versa. The SD and entropy are defined as follows:  (6) where  and ) ( i s p denote the mean and possibility of the coefficients in the ECG host.

Performance Comparison
Performance comparison between various methods is illustrated in Fig. 7. The proposed method (represented as LSB2) provided the largest payload among the compared methods, and the SNR of value 55.63 dB (with a payload size of around 25 Kb) is still better than that of Yang and Wang's technique [19]. In addition, the second version of the proposed method (represented as LSB) provided the best SNR performance in the payload between 15 Kb and 22 Kb among the various techniques. The other three methods [15,18,19] provided similar SNR values when the payload size is less than 15 Kb. The second version of the proposed method embedded only three data bits in the last row of a host block. The main difference between the proposed two versions is that the former method can be implemented in situations that require a payload greater than 25 Kb, whereas the latter method can be applied to environments where a good perceived quality (or high SNR) is an important indicator (or a concern factor). As specified in Section 2.3, in the third version of the proposed method, to further obtain good perceived  [16] used the PSNR as their perceived quality measurement, and the performance comparison in terms of the payload size and PSNR is given in Table 2. Evidently, the PSNR value of the proposed method (with our three versions) is the best among the methods, and our payload is still larger than that of the other two techniques. Figure 7 and Table 2 shows that the proposed method generates better perceived quality and higher payload than existing techniques.  The third version of our method. 2 The second version of our method. 3 The original version of our method.

Extra Functions
Several examples of survived watermarks from the manipulations of the marked ECG100 (with  = 16) are given in Table 3. A binary image of size 180 × 180 was used as an input watermark. The resultant SNR of the marked ECG100 was approximately 52 dB. Table 3 indicates that a PRD value of 0 for a marked ECG was not manipulated. The second and third rows of the table indicated that the watermark extracted from the marked ECG and attacked by AWGN attacks of signal strengths 0.001 and 1 dB, respectively, were recognizable. Although the PRDs of the survived watermarked from the "cropping" attack were around 0.8, they were identified. Moreover, the survived watermarks manipulated by "Inversion" and "Scaling" were still recognized. In addition, the extracted watermark (at the 9th row of the table) identified as the last three bits of the marked samples was truncated. The last two rows of the table show that our method has good performance against the "translation (with scale of +1500 and −1500)" attack. Table 5 shows that our method is capable of resisting several kinds of attacks. Table 3. Examples of survived watermarks from the manipulations of the marked ECG100

Discussion
To evaluate hiding performance, several objective assessments such as SNR, PSNR, MAE, and PRD have been commonly used in ECG steganography [13][14][15][16][17][18][19][20]. Simulations have indicated that the less value of , the lower the MAE, the larger the SNR/PSNR, implying that a good perceived quality can be obtained by the proposed method, and vice versa. In addition, the average payload and SNR/PSNR of the proposed method outperforms existing techniques. Moreover, the proposed method has an extra characteristic of robustness, which is rarely existed in conventional ECG steganographic methods.

Conclusions
In this article, we proposed an improved ECG steganography for the protection of the sensitive personal information of patients. Based on the IWT domain with coefficient offset and LSB technique, data bits can be effectively embedded in the host blocks of an ECG signal. Experiments confirmed that the average SNR (or PSNR) and payload of the proposed method are superior to those of the existing ECG steganography. Moreover, our method equipped with robustness performance was rarely seen in existing ECG steganography techniques.
That is, the proposed method is tolerant of attacks, such as cropping, inversion, scaling, translation, truncation, and Gaussian noise-addition attacks. Because the processing time is fast, our method can be applied in mobile biometric devices or wearable electronics. To further promote hiding capacity and robustness, our future work will focus on an a priori analysis and statistics of each ECG input and performed steganography in other transform domain. Informed consent: Not applicable.