## 2 Proposed Scheme and RF Transmission Algorithm
### Signal Decryption
The predominant frequencies are recovered to diagnose pertinent pathologies from the signals transmitted through the RF transmission chain. \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]0 Photo of the experimental testbed. Equipment pertinent to the setup: (**A**) Altera Cyclone V FPGA SoC-Kit. (**B**) AD9361 RF Agile Transceiver operating at a center frequency of 2.45 GHz. (**C**) Mini-circuits ZFBP-2400-S+ bandpass filter. (**D**) Mini-circuits for power amplifiers ZX60-V63+. (**E**) Coupler mini-circuits ZFDC-16-63-S+. (**F**) SIGLENT SSA 3032X Spectrum Analyzer. (**G**) GW INSTEK GPS-3303 Power Supply. (**H**) Display HOST PC-MATLAB R2024a. The ECG signal under test is compared to the chaotic system signal implementations in Figures 9 and 10. When the chaotic system is implemented, the transmitted signals are evaluated with the decrypted signals, and a precision that improves an RMSE 0.0559 is obtained. The demodulation process and the elimination of the chaotic signal significantly enhance the accuracy of the resultant ECG. The synchronization matter, which is a result of the chaotic system synchronization error, is resolved by Algorithm 1 through the modulation and encryption of the ECG signal and Algorithm 2 through the generation of the LTE signal that will be transmitted through the AD9361 transceiver. \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]1 An ECG signal decrypted under a 128-QAM scheme. \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]2 A 128-QAM with a power amplifier using a scale factor of 0.05. \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]3 ECG signal with tachycardia encrypted under the 128-QAM modulation scheme. \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]4 ECG signal encrypted under the 128-QAM modulation scheme. The error vector measurement (EVM) validation of the encrypted ECG signal under a 128-QAM modulation scheme is illustrated in \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]5 An accuracy of -27 (db) EVM is achieved in the implementation. Upgrading to a higher-order n-QAM scheme can enhance this measurement. The original signal can be recovered by decompressing the 7-bit symbols. The ECG signal, sent for a future classification and diagnosis procedure, is obtained by subtracting the chaotic signal. Figure 12 illustrates two comparisons of the correlation between the ideal signal (blue) and the cross-correlation of the received signal in relation to the transmitted signal (brown). The correlation between the recovered and transmitted signals is comparable, as evidenced by the maximum amplitude at the origin. Figure 8 contains four ECG cycles with a population of 1402 samples, which are broken down along \[s(t)=\frac{1}{2}I(t)+\frac{1}{2}\big{(}I(t)\cos(4\pi f_{c}t)-Q(t)\sin(4\pi f_{ c}t)\big{)}, \tag{19}\]6 The QRS complexes of the recovered and transmitted signals are primarily located in the ranges of 100, 400, 1300, and 2050 sample numbers. Figure 13 shows the discrete Fourier transform (DFT) of the transmitted and received signals. The signals under test oscillate at a frequency of 1.66 Hz, and with DC components as shown in the origin, the power begins to dissipate right in the range of 8 Hz, which includes the inner frequencies observed in Figure 8 in the Q-T wave at the end of each period of the ECG. Analysis in the frequency domain allows us to analyze and diagnose pathologies and arrhythmias. This mainly takes place in the QRS complex, in addition to others such as short Q-T and long Q-T waves with high frequencies involved.