Secured ECG signal transmission for human emotional stress classification in wireless body area networks
- Hansong Xu^{1} and
- Kun Hua^{1}Email author
https://doi.org/10.1186/s13635-015-0024-x
© Xu and Hua. 2016
Received: 1 March 2015
Accepted: 25 November 2015
Published: 15 January 2016
Abstract
Information security is key important when we are trying to interconnect the wireless body sensor network with the healthcare social network via mobile facilities. In this paper, we specially work on a secured electrocardiogram (ECG) signal transmission scheme to prevent further injuries for patients with heart diseases from human emotional stress. We proposed a dynamic encryption method via biometric information among frequency spectrums of ECG signals, which can guarantee both high classification rate (>90 %) and system energy efficiency. At the same time, cooperative relays are applied for an additional spatial diversity gains. Simulation results show that the improved transmission rate and signal power capacity can lower the probability of data intercept (LPI) and detection (LPD) by taking the advantages of both temporal and spatial diversities. The network security thereby can be further improved.
Keywords
1 Introduction
Over the last few decades, smart devices have improved digital signal communication performances greatly, and thus brought us numerous benefits in our daily lives. State-of-the-art smartphones are equipped with advanced processors, which provide high speed data transmission rate, efficiency usage of battery life, and multi-task running ability. In wireless communication network, such features promise high quality information delivery, efficient energy cost, and low bit error rate, etc. For smartphones based on signal encryption and transmission, traditional encryption methods, such as “RSA,” are too slow for the computational process and are limited for key generation. While, in this paper, a time-varying encryption method “dynamic AES” is proposed, where its symmetric key is selected from dynamic key sets, which are generated from the changing biometric information of electrocardiogram (ECG) signals. Through this “dynamic AES” encryption scheme, the security and robustness of the wireless transmission is improved greatly.
Meanwhile, we use cooperative relays to improve data transmission quality [1] and constrain the probability of intercept and detection. In our work, low probability of intercept can be achieved by increasing the spatial and temporal diversity of the transmission, and low probability of detection can be realized by multiplexing among multiple virtual multiple-input and multiple-output (MIMO) channels [2]. At the same time, the battery energy efficiency is also guaranteed by sharing transmission power cost with cooperative relays.
Indexed body health condition information can be shown as a certain form of continual signals, which are called “biomedical signals.” Biomedical signal analysis can provide us a probability of estimation for our health condition from both physical and mental sides, which mainly focuses on skin temperature (ST), electromyogram (EMG), blood pressure (BP), ECG, etc. Among those signals, ST is a physiological signal, which is often used to indicate stress conditions and is similar to BP, but BP is often applied to measure different levels of stresses during induce stress level task [3, 4]. For EMG and ECG signals, the former is used more on-body movement control or pain identification and monitoring through muscles. The latter is used more for heart activity monitoring and heart information carrier. ECG signal carries heart activity information, which can be analyzed for stress level detection, identification, and predication [3, 5].
The remainder of this paper is organized as follows: Section II reviews relevant literature. The proposed dynamic ECG signal encryption scheme with cooperative relayed network is studied in Section III. Simulation results are given and analyzed in Section IV, and Section V summarizes our research conclusions.
2 Peer work review
For stress level identification assessment, Paper [3], introduced ST for stress level change identification and monitoring. In their work, a plenty of experiments have been placed for stress level classification through ST parameters. They induced stress levels to healthy volunteers by using Stroop color test for different stress levels and received the accuracy rate of 88 %.
In 2012, paper [7] applied statistical features from ECG signals for stress level identification and classification, which is considered as a better technique. In their paper, the stress was induced by the method called “MAT” [8, 9], which was designed to increase stress level from one to another, gradually from “normal” to “low stress level,” then “medium stress level” and finally “strong stress level.” Then, using “DWT” for statistical features extraction, in which “DWT” was able to decompose the ECG signal both in time and frequency domain.
ECG signals are more meaningful at the frequency range of 0–0.5 Hz, which was separated into three frequency ranges (VLF, LF, HF) [7] for stress classification. Meanwhile, paper [5] used wavelet transform for ECG signal decomposition and feature extraction; totally, six statistical features in combinations of LF, HF, and LF/HF give a best of 96.41 % classification accuracy rate.
From wired to wireless communication, security problem is increasingly severe because of the publicly shared bandwidth and open access in wireless network. Paper [6] proposed a biometric-information-based key distribution for WBSN application; in their paper, the encryption method is proposed w.r.t the limitation from biomedical sensors such as the following: energy supply, computational capacity, and communication capabilities. In their work, the 128 bits symmetric keys for Advanced Encryption Standard (AES) are generated from the coefficients, where the coefficients are from fast Fourier transform (FFT) for a short period of time domain ECG.
As is well known, AES is a widespread encryption standard and works very well for WBSN-based ECG signal protection. Since DWT has already converted the temporal ECG signal into frequency domain through an efficient and also practical way, in this case, our biometric key sets are generated from the coefficients of DWT, instead of FFT on ECG signal.
Then, further research found that the improved transmission data rate and signal power are related to the higher level of transmission security in wireless communication. In paper [2], the authors investigated the information security of MIMO links (multiple-input and multiple-output) with a proposed theoretic framework; space-time communication was provided for improve the security of digital data transmission. In their work, a well-designed secure link was built to lower the probability of information intercept and detection by the eavesdroppers, such as the transmitter and destination’s communication channel was informed, while the eavesdropper’s channel was uninformed, putting an eavesdropper at an accordingly disadvantage.
At the same time, applying space coding over multiple transmitter and receiver antennas can also lower the data intercept and detection rates. Besides, encrypted data stream from transmitter side, which can only be decrypted by paired receiver side with time-varying biometric key. Meanwhile, the channel security was improved by applying MIMO and channel coding technologies, plus it may be applicable for several kinds of communication channels. In 2012, paper [12] considered careful physical position and smart cooperation of antennas and focused more on the specific absorption rate (SAR) performance of the increasing mobile phone terminals. In their work, the relationship of SAR with antenna position, chassis size, antenna height, etc. were evaluated.
Peer work review
Time | Researcher | Pros | Cons |
---|---|---|---|
Sep. 2009 | Fen Miao, Lei Jiang, Ye Li, and Yuan-Ting Zhang [6] | The biometric information-based key sets have higher security than the regular key. The encryption method is computational-friendly, as well as energy efficient. | Additional frequency transform can be saved with our scheme. |
Dec. 2003 | Hero, A.O. [2] | Using MIMO in wireless communication network, with space coding to constraint low interpret and low detection probability, communication security is improved. | Lack of an encryption method for transmitted signal, in their work, only protection of communication channels was considered. |
Dec. 2012 | Kun Hua [1] | A cooperative cellular network module based on Alamouti for multimedia communication was built in their work, and the BER performance was improved. | Do not have an encryption method; this module was built based on multimedia communication, which is 2D signal such as images, not for biomedical signal. |
Aug. 2012 | Karthikeyan Palanisamy [3] | Using Stroop color test for inducing stress, using ST for identifying stress levels. Improved classification accuracy. | Not applicable for on-body real-time stress monitoring system. |
Nov. 2011 | P. Karthikeyan [5] | Using real-time stress prediction and identification system over WBSN, and improved classification accuracy. | Do not considered security performance during signal transmission. |
Oct. 2012 | P. Karthikeyan [7] | Using DWT for decomposition and feature extraction | Do not apply in an application for further test; the classification rate is not promised |
July. 2012 | Kun Zhao [12] | Physically analyzed the position and placement of antenna contribution to improvement of communication performance. | Only analyzed the physical antenna placement, lack of channel protection for improvement the performance. |
3 Methodology
Frequency bands after each level’s decomposition
Frequency domain {f _{ i }}, i ∈ [1, 16] | f _{1} | f _{2} | …… | f _{12} | f _{13} | f _{14} | f _{15} | f _{16} |
---|---|---|---|---|---|---|---|---|
Frequency bands (Hz) | 1024–512 | 512–256 | …… | 0.5–0.25 | 0.25–0.125 | 0.125–0.0625 | 0.0625–0.03125 | 0.03125–0.01562 |
3.1 ECG signal pre-processing and stress level classification
In Eq. (1), “DWT” is applied for ' i ' level’s ECG signal decomposition. By using “DWT,” the original ECG signal is decomposed to detail coefficient (CD) by high frequency pass filter and approximation coefficient (CA) by low frequency pass filter. After the first level decomposition, the coefficient (CA) continues to decompose into the second level, then, with continuous decomposition, ECG signal will be decomposed into the 16th level. The frequency ranges (VLF, LF, HF) will be extracted, the very low frequency (VLF) bands signals are {f _{15}, f _{16}} (0 − 0.04) Hz; the low frequency (LF) bands signals are {f _{14}}(0.04-0.15) Hz; the high frequency (HF) signals are {f _{12}, f _{13}} (0.15-0.5) HZ [5]; since after each level’s decomposition, the lower half of last level’s frequency bands becomes the new level’s frequency bands, which is also shown in Table 2.
In the above six features, ' f _{ i } x ' is i level wavelet coefficients, “x” means statistic in that level’s wavelet coefficients, for “entropy” equation. ' p(f _{ i } x) ' is the probability of ' f _{ i } x '.
3.2 ECG signal encryption “dynamic AES”
Once the stress level is classified, such as “strong stress,” it is highly possible to cause emergency accident. In both secure and privacy concerns, it appears extra important to protect the ECG signal from eavesdroppers and unauthorized receiver. Meanwhile, transmitting the corresponding ECG signal with minimum computational process (minimum delay) and the highest security protection is necessary for smartphone based ECG signal transmission.
In this section ECG signal, ‘ f _{ i } k ’, stands for totally k levels of frequency signals that satisfies the classification rate from equation (4.e) and (4.f). To encrypt ‘ f _{ i } k ’,, we applied Advanced Encryption Standard (AES) algorithm, also called as “AES algorithm.” Generally, AES scheme is based on block cipher encryption and has been widely spread as a standard encryption application. It sufficiently satisfies the requirements of encryption strength and computational process.
The following is an integration design of compression and encryption for biomedical signals:
Input: cipher sets ‘ P(n) ’, plaintext ' f _{ i } k '
- 1.
Dynamic key generation
‘ P(n) ’ is a 128 bits cipher for ECG signal ' f _{ i } k ' based on Table 3.Table 3Keys sets according to transmitted signal
Frequency bands (Hz)
Key sets P
LF (0.04–0.15)
P _{1}(n)
LF (0.04–0.15), HF (0.15–0.5)
P _{2}(n)
LF (0.04–0.15), HF (0.15–0.5), f _{11} (0.5–1)
P _{3}(n)
\( \begin{array}{c}\hfill \vdots \hfill \\ {}\hfill \vdots \hfill \end{array} \)
\( \begin{array}{c}\hfill \vdots \hfill \\ {}\hfill \vdots \hfill \end{array} \)
LF
⋮
f _{1}
P _{ k }(n)
- 2.
Key expansion
One hundred twenty-eight bits key are expand to 4 × 11 words from four words and can be calculated by:$$ \begin{array}{c}\hfill {w}_{i+4}={w}_i\otimes g\left({w}_{i+3}\right)\hfill \\ {}\hfill {w}_{i+5}={w}_{i+4}\otimes {w}_{i+1}\hfill \\ {}\hfill {w}_{i+6}={w}_{i+5}\otimes {w}_{i+2}\hfill \\ {}\hfill {w}_{i+7}={w}_{i+6}\otimes {w}_{i+3}\hfill \end{array} $$Where ' g ' function is known as round constant ' Rcon[j] '.
- 3.ECG encryption
- (a)
Byte substitution operation
Plaintext ' f _{ i } k ' was input as 4 × 4 matrix in byte in their hex value. Then, one hex value as row input and the other hex value as column input were substituted through the S-box, where the S-box are constructed by arithmetic operations of finite field of form GF(2^{8}).
- (b)
Shift rows
To diffuse the cipher, each row in the state array shift to left with according order, such as the following: no shift in first row, shift by one byte in second row, shift by two bytes in third row, and shift by three bytes in third row.
- (c)
Mix columns
A linear transformation, which performs each column with multiplication and addition, is shown as follows:$$ \left[\begin{array}{c}\hfill \begin{array}{c}\hfill \begin{array}{c}\hfill f\mathit{\hbox{'}}\left(0,j\right)\hfill \\ {}\hfill f\mathit{\hbox{'}}\left(1,j\right)\hfill \end{array}\hfill \\ {}\hfill f\mathit{\hbox{'}}\left(2,j\right)\hfill \end{array}\hfill \\ {}\hfill f\mathit{\hbox{'}}\left(3,j\right)\hfill \end{array}\right]=\left[\begin{array}{c}\hfill \begin{array}{c}\hfill \begin{array}{c}\hfill 2f\left(0,0\right)+3f\left(0,1\right)+f\left(0,2\right)+f\left(0,3\right)\hfill \\ {}\hfill f\left(1,0\right)+2f\left(1,1\right)+3f\left(1,2\right)+f\left(1,3\right)\hfill \end{array}\hfill \\ {}\hfill f\left(2,0\right)+f\left(2,1\right)+2f\left(2,2\right)+3f\left(2,3\right)\hfill \end{array}\hfill \\ {}\hfill 3f\left(3,0\right)+f\left(3,1\right)+f\left(3,2\right)+2f\left(3,3\right)\hfill \end{array}\right] $$The addition is meant as XOR.
- (d)
Add round key
The four sub-key words from previous key expansion are applied for each round literation (10 rounds for 128 bits key). Each byte in state array is XOR-ed with according sub-keys.
- (a)
- 4.
Encrypted ciphertext ' F _{ i } k '
The last round of encryption does not include “mix columns” step. After totally 10 rounds, followed with (a), (b), (c), and (d) four steps, the output is 128 bits ciphertext ' F _{ i } k '.
- 5.
Decryption components
Decryption follows the process order as inverse shift rows, followed with inverse substitution and inverse add round key, where the process is inverse corresponding transformation, note that no “inverse mix columns” at last round as well.
Additionally, as abovementioned, the AES encryption algorithm is improved by providing the dynamic key from time-varying ECG signal, as “dynamic AES,” the dynamic key sets (cipher key), for current use, are generated from a function of the frequency domain of a short period of last transmitted ECG signal (plaintext), which offers time-varying candidates for biometrics-information-based cryptographic system.
Specifically, the frequency bands ECG signal is varying for each time transmission. Thus, for example, the LF and HF bands ECG signal were transmitted; meanwhile, the key sets were extracted from those two frequency bands followed with the following: (1) For each frequency levels, we extract 180 coefficient samples, totally 360 samples from two frequency bands. (2) Divided all coefficients into 20 blocks, 18 coefficients in, and then, quantized 18 coefficients individually into binary. (3) Quantization process provides 4 binary bits from each coefficient, totally 72 bits for each block. Finally, the key set ' P(n) ' from transmitted LF and HF, and for next time encryption, is generated as 20 blocks *72 bits.
For each time encryption, the transmitter selects a 128-bits cipher from the generated cipher sets, where the encryption process followed with the proposed “AES algorithm.” By this way, concerning about the energy constraint and resource limitation, our biometrics cryptosystem based on AES algorithm provides high secure strength as well as the minimum energy consumption.
3.3 Encrypted signal transmission Fig. 6
After Alamouti is applied for channel coding, encrypted signal is sent to the receiver. Such low probability of interception and low probability of detection features will greatly improve the transmission security. Regarding low probability of interception (LPI), we try to speed up the information data rate in multiple channels from sender to receiver and zero out the data rate in eavesdropper’s channel by considering the research in [2].
Regarding the low probability of detection (LPD), we evaluate the data error rate for the performance improvement. The relation of error rate to low detection probability is mathematically mapped to the constraint of mean squared power, as explained in [2]. We set ' P _{ n } ' as maximum of tolerable error rate, then eigenvalues = cov(F _{ i }(x)_{ n }) of the matrix
For both sender and receiver informed communication channel, ' H ' conditioned denotes squared transmitter power.
4 Experiment result
In this paper, for wireless ECG signal preprocessing, we firstly applied LPF and HPF for artificial and machine noise removal; then, for on-body stress level classification, we used “DWT” for ECG signal decomposition, for better analysis in both frequency and time domain; third, six statistical features were extracted from dynamic frequency bands for stress level classification.
Especially, in this classification technology, the features are extracted from dynamic multiple frequency levels, which achieve the stable and outstanding classification accuracy, instead of from individual frequency level, such as “LF.” Then, we applied time-varying biometric key sets based on “AES” to improve the security feature in the proposed system. In this work, the dynamic encryption method is achieved from different width of frequency spectrum, which is representing the biometric information of ECG signals. The encryption keys are individual for each time’s transmission. After that, specifically for “Mode2 or 3,” we need to transmit the corresponding ECG signal for further analysis.
Also, due to the limitation on power supply, we involved cooperative transmission for energy efficiency concern, which is also connected to the system security performance indirectly, as pointed out in [2].
Then, for the existence of information intercept and detection probabilities by eavesdroppers, we explored that increasing the signal power can achieve low probability of intercept (LPI), at the same time, increasing the communication rate leads to low probability of information detection (LPD). Finally, following simulations proves that communication security performance is largely improved.
Based upon experimental results, the classification accuracy gains are 90 % higher, in which the classification features are extracted from the combined LF&HF signal. As expected, it gains better accuracy rate when more frequency levels are added. Then, comparing to the traditional AES, our encryption experiment results achieved an improved energy efficiency and security by the biometric information-based key sets generation scheme and the time-varying random-like encrypted data. Finally, as shown in the experimental results, Figs 7, 8, 9, and 10, data transmission quality (bit error rate) is improved by the cooperative relay and Alamouti encoding techniques. For instance, cooperative relay transmission reduces the energy consuming at sender side, and by increasing sender relays, an even better transmission quality can be achieved.
5 Conclusions
In this paper, we try to classify heart disease patients’ emotional stress levels to prevent further dangerous situation by proposing a secure and energy efficient ECG signal transmission solution in WBAN. We especially designed a dynamic keying method for signal encryption through biometric information among ECG signals, which can guarantee both high classification rate (>90 %) and energy efficiency. At the same time, cooperative relays were applied during data transmission for an additional security transmission purpose. Through this way, the improvement of transmission rate and signal power capacity are able to lower the probability of data intercept and data detection (LPI and LPD) by taking the advantages of both temporal and spatial diversities.
Declarations
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
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