Abstract
Digital physiological signals in telecare medicine information systems have been widely applied in remote medical applications, such as telecare, tele-examination, and telediagnosis, via computer networking transmission or wireless communication. However, these medical records need to ensure authorization demands in the channel model for human body communication and remote medical servers and enhance the confidentiality, recoverability, and availability of transmission data. Hence, this study proposes a symmetric cryptography scheme with a chaotic map and a multilayer machine learning network (MMLN) to achieve physiological signal infosecurity. A chaotic pseudorandom number generator within specific control parameters can dynamically produce unordered sequence numbers to set the secret keys for a regular secret key update, thereby improving the security of private cipher codes. The chaotic map is quickly iterated to produce a pseudorandom key stream for real-time applications, and the private cipher codes are selected using the initial and specific control parameters at the data emitter and receiver ends. A general regression neural network is used to map the high-dimensional input–output pair of cipher codes for substitution and permutation processes. Its adaptive MMLN with an optimization algorithm can rapidly train the random cipher code protocol to achieve an encryptor and a decryptor for a regular encrypted communication. Using the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) Arrhythmia Database, 100 electrocardiogram fragments are used to verify the proposed model, and the peak signal-to-noise ratio (PSNR) as a quantitative quality metric is used to evaluate the visual quality after encryption and decryption processes for further diagnosis applications. Experimental results show that the proposed scheme has a higher mean PSNR (35.26 ± 3.77 dB) and shorter mean executing time (0.16 ± 0.01 s) compared with traditional cryptography protocol schemes.
Highlights
In the human body communication (HBC) channel, physiological signals are obtained via biopotential electrodes and transducers over time, digitized by an analog-to-digitalThe associate editor coordinating the review of this manuscript and approving it for publication was Jun Wang .conversion (ADC), and stored in a memory unit
EXPERIMENTAL RESULTS AND DISCUSSION This section presents the experiment results to validate the effectiveness of the proposed symmetric cryptography protocol for ECG infosecurity in computer networks (IEEE 802.3 standard [45]) or wireless communication networks (IEEE 802.15 standard[10]), including (1) the ECG ADC process, (2) the encryptor and decryptor training, (3) the ECG encryption and decryption processes, and (4) recovery quality evaluation
Experimental ECG records were collected from archived files from MIT#100 to MIT#234 in the Massachusetts Institute of Technology–Beth Israel Hospital (MIT–BIH) arrhythmia database [40], including women and 25 men (32 to 89 years); approximately 60% of these records was obtained from inpatients and 20 major classes
Summary
In the human body communication (HBC) channel, physiological signals are obtained via biopotential electrodes and transducers over time, digitized by an analog-to-digitalThe associate editor coordinating the review of this manuscript and approving it for publication was Jun Wang .conversion (ADC), and stored in a memory unit. Various QRS waveforms are used to identify the normal beat (), atrial premature beat (A), ventricular premature contraction (V), right/left bundle branch block beat (R/L), paced beat (P), and fusion of ventricular and normal beats (F) [6], [8], [9]. These symptom signals can be transmitted via wired (computer networks) or wireless communication for applications in remote cardiac diagnosis. The security and privacy of personal physiological data should be protected while being transmitted in public communication channel
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