Secure EEG Signal Transmission for Remote Health Monitoring Using Optical Chaos

Shahzadi, Romana; Anwar, Syed Muhammad; Qamar, Farhan; Ali, Mudassar; Rodrigues, Joel J. P. C.; Alnowami, Majdi R.

doi:10.1109/access.2019.2912548

Cited by 28 publications

(17 citation statements)

References 33 publications

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“…For secure transmission of EEG signal optical chaos has been introduced [8]. To hide EEG signal, author has used source of semiconductor laser to generate optical chaos, at the time of signal transmission and combination of EEG signal and optical chaos has been transmitted over optical fiber medium.…”

Section: Introductionmentioning

confidence: 99%

Secure Transmission of EEG Data Using Watermarking Algorithm for the Detection of Epileptical Seizures

Gupta¹,

Chakraborty²,

Gupta³

2021

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Internet of things (IoT) has a collection of multiple network-enabled devices like sensors, gateways, smartphones, and communication links (short and long ranges). Tremendous capacity of IoT system has made possible to monitoring and detection of epileptical seizures in real time. For this purpose, various smart devices and applications, helps to transmit information securely. Amalgamation of IoT with healthcare system provides opportunity to deal issues like security, detection of seizures and real time monitoring. The proposed model of cloud-enabled Health IoT system has been presented in this paper, gives the idea about monitoring of epileptical patients. For secured transmission of Electroencephalogram (EEG) data, digital watermarking technique has been used over two dimensional EEG data obtained through one dimensional EEG data by applying Short Time Fourier Transform (STFT). In this paper, watermarking of two dimensional EEG data has been done using discrete wavelet transform - discrete cosine transform (DWT-DCT) based Bacterial Foraging Optimization (BFO) technique and its performance has been figure out. Here, satisfactory watermarking performance in terms of Peak Signal to Noise Ratio (PSNR) 49.50 for class Z and 49.61 for class S EEG data along with Normalized Cross Correlation (NCC) 0.0039 for both classes of EEG data have been achieved.

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Section: Introductionmentioning

confidence: 99%

Secure Transmission of EEG Data Using Watermarking Algorithm for the Detection of Epileptical Seizures

Gupta¹,

Chakraborty²,

Gupta³

2021

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“…Data encryption is usually done by chaotic or hyper-chaotic methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 ]. In some researches, chaotic or hyper-chaotic systems are used to encrypt peripheral data [ 38 , 39 , 40 , 41 , 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

“…In some researches, chaotic or hyper-chaotic systems are used to encrypt peripheral data [ 38 , 39 , 40 , 41 , 42 , 43 ]. In recent years, researchers have focused on the encryption of medical data using chaotic methods [ 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 ]. Medical data contains important information about patients [ 41 , 44 , 45 , 46 ].…”

Section: Introductionmentioning

confidence: 99%

“…The idea of the optical chaos method for secure EEG signals transmission in telemedicine applications has been discussed in Shahzadi et al’s research [ 19 ]. In the proposed scheme, a semiconductor laser source is utilized to generate optical chaos and conceals the EEG signal in the chaotic signal before transmission over the optical fiber.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

Javan

Jafari

Shoeibi

et al. 2021

Sensors

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In this paper, a novel medical image encryption method based on multi-mode synchronization of hyper-chaotic systems is presented. The synchronization of hyper-chaotic systems is of great significance in secure communication tasks such as encryption of images. Multi-mode synchronization is a novel and highly complex issue, especially if there is uncertainty and disturbance. In this work, an adaptive-robust controller is designed for multimode synchronized chaotic systems with variable and unknown parameters, despite the bounded disturbance and uncertainty with a known function in two modes. In the first case, it is a main system with some response systems, and in the second case, it is a circular synchronization. Using theorems it is proved that the two synchronization methods are equivalent. Our results show that, we are able to obtain the convergence of synchronization error and parameter estimation error to zero using Lyapunov’s method. The new laws to update time-varying parameters, estimating disturbance and uncertainty bounds are proposed such that stability of system is guaranteed. To assess the performance of the proposed synchronization method, various statistical analyzes were carried out on the encrypted medical images and standard benchmark images. The results show effective performance of the proposed synchronization technique in the medical images encryption for telemedicine application.

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“…Data security applications can be made for biomedical signals or biomedical images using chaotic systems or chaotic neural networks [54][55][56][57][58][59][60][61][62][63][64][65][66][67][68]. However, when the literature is examined, it is seen that data security application is carried out for signals such as EEG, EMG, ECG, which are recorded as multi-channel, with a single signal belonging to a single channel every time.…”

Section: Introductionmentioning

confidence: 99%

A new data-hiding algorithm for multi-channel biomedical signals based on variable-order fractional chaotic neural networks with frequency effect

Kaçar

2021

Eur. Phys. J. Spec. Top.

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In this study, a new variable-order fractional chaotic neural network with frequency effect is proposed based on Hopfield Neural Network Under Electromagnetic Radiation, which is used to model brain functions in the literature. The numerical solution of this proposed system is carried out by the Grünwald-Letnikov (G-L) method, and time series and phase portraits are presented. In addition, the chaotic behavior is analyzed by Lyapunov exponent analysis according to the frequency parameter used for the variable-order function. A Pseudo-Random Number Generator (PRNG) is designed for data security applications using the obtained state variables and the usability of the PRNG is demonstrated with the NIST-800-22 test. Afterward, a data-hiding algorithm for personal data is presented for multi-channel biomedical signals based on the designed PRNG. With this algorithm, implementations are made for EEG, ECG, and EMG signals. It is shown in time and frequency domains that the method is usable for data-hiding applications. In addition, the effectiveness of the newly developed algorithm is proven with statistical methods.

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Secure EEG Signal Transmission for Remote Health Monitoring Using Optical Chaos

Cited by 28 publications

References 33 publications

Secure Transmission of EEG Data Using Watermarking Algorithm for the Detection of Epileptical Seizures

Secure Transmission of EEG Data Using Watermarking Algorithm for the Detection of Epileptical Seizures

Medical Images Encryption Based on Adaptive-Robust Multi-Mode Synchronization of Chen Hyper-Chaotic Systems

A new data-hiding algorithm for multi-channel biomedical signals based on variable-order fractional chaotic neural networks with frequency effect

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