Abstract:We present an extended heterogeneous oscillator model of cardiac conduction system for generation of realistic 12 lead ECG waveforms. The model consists of main natural pacemakers represented by modified van der Pol equations, and atrial and ventricular muscles, in which the depolarization and repolarization processes are described by modified FitzHugh-Nagumo equations. We incorporate an artificial RR-tachogram with the specific statistics of a heart rate, the frequency-domain characteristics of heart rate var… Show more
“…4 we show ECG’s corresponding to ( a ) normal rhythm ( H = 3), ( c ) quasiperiodicity ( H = 2.729) and ( e ) VF ( H = 2.164), with their corresponding power spectra in the right hand side. For all rhythms in this subsection, the numerical simulations of (4) were carried out with C = 1.35, β = 4, and scaling coefficients: α 1 = −0.024, α 2 = 0.0216, α 3 = −0.0012, and α 4 = 0.12. α i coefficients were calculated using a modification of the supervised learning algorithm called perceptron 38,39 . The time scaling factors, Γ t = 7, for normal and period doubling rhythms, and Γ t = 17 for VF were computed using (5), in order to recover the physiological times.Figure 4ECG’s corresponding to: ( a ) normal rhythm ( H = 3), ( c ) quasiperiodicity ( H = 2.729) and ( e ) VF ( H = 2.164).…”
We propose a model to generate electrocardiogram signals based on a discretized reaction-diffusion system to produce a set of three nonlinear oscillators that simulate the main pacemakers in the heart. The model reproduces electrocardiograms from healthy hearts and from patients suffering various well-known rhythm disorders. In particular, it is shown that under ventricular fibrillation, the electrocardiogram signal is chaotic and the transition from sinus rhythm to chaos is consistent with the Ruelle-Takens-Newhouse route to chaos, as experimental studies indicate. The proposed model constitutes a useful tool for research, medical education, and clinical testing purposes. An electronic device based on the model was built for these purposes
“…4 we show ECG’s corresponding to ( a ) normal rhythm ( H = 3), ( c ) quasiperiodicity ( H = 2.729) and ( e ) VF ( H = 2.164), with their corresponding power spectra in the right hand side. For all rhythms in this subsection, the numerical simulations of (4) were carried out with C = 1.35, β = 4, and scaling coefficients: α 1 = −0.024, α 2 = 0.0216, α 3 = −0.0012, and α 4 = 0.12. α i coefficients were calculated using a modification of the supervised learning algorithm called perceptron 38,39 . The time scaling factors, Γ t = 7, for normal and period doubling rhythms, and Γ t = 17 for VF were computed using (5), in order to recover the physiological times.Figure 4ECG’s corresponding to: ( a ) normal rhythm ( H = 3), ( c ) quasiperiodicity ( H = 2.729) and ( e ) VF ( H = 2.164).…”
We propose a model to generate electrocardiogram signals based on a discretized reaction-diffusion system to produce a set of three nonlinear oscillators that simulate the main pacemakers in the heart. The model reproduces electrocardiograms from healthy hearts and from patients suffering various well-known rhythm disorders. In particular, it is shown that under ventricular fibrillation, the electrocardiogram signal is chaotic and the transition from sinus rhythm to chaos is consistent with the Ruelle-Takens-Newhouse route to chaos, as experimental studies indicate. The proposed model constitutes a useful tool for research, medical education, and clinical testing purposes. An electronic device based on the model was built for these purposes
“…The ECG data is generated by a script taken from Quiroz and et al work [11]. The main .m le has two parts.…”
Section: Synthetic Data Generated At Matlab Environment (The Proposed Method)mentioning
confidence: 99%
“…First the following 12 lead ECG are generated by the MATLAB script [11]: lead_I, lead_II, lead_III, lead_aVL, lead_aVL, lead_aVL, lead_V1, lead_V2, lead_V3, lead_V4, lead_V5, lead_V6…”
Section: Design Of Fcm Based Anfismentioning
confidence: 99%
“…In Section 4 we present the proposed estimation procedure for the recovery of the missing samples. Comparative simulation results on synthetic ECG data [11] are presented in Section 4.2.…”
This paper presents estimation of missed samples recovery of Synthetic electrocardiography (ECG) signals by an ANFIS (Adaptive neuro-fuzzy inference system) method. After designing the ANFIS model using FCM (Fuzzy C Means) clustering method. In MATLAB’s standard library for ANFIS, only least-square-estimation and the back-propagation algorithms are used for tuning membership functions and generation of fis (fuzzy inference system) file, but at current work we have used FCM method that shows better result. Root mean square error (difference of the reference input and the generated data by ANFIS) for the three synthetic data cases are: a. Train data: RMSE = 1.7112e-5b. Test data: RMSE = 5.184e-3c. All data: RMSE = 2.2663e-3
“…For example, an electrocardiogram (ECG) is commonly used to monitor the human heart's internal electrical activities for applications in computer-aided diagnosis systems. The raw data collected by ECG can be used to analyze heart rate variability (HRV), detect cardiac arrhythmia, diagnose cardiovascular diseases, recognize emotions, and screen obstructive sleep apnea [3][4][5][6][7]. For cardiac arrhythmia detection, 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].…”
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 highdimensional 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.
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