## 4 Experimental Results  
In this section, we evaluate the performance of an end-to-end model designed to convert a CW (continuous-wave) radar signal recorded in a non-contact manner into an ECG through frequency decomposition and feature extraction from the public dataset. The performance of the proposed model is compared with the conventional CNN with an FC (fully connected) layer through the mean square error (MSE). The compared deep learning networks generate complex nonlinear features from the input data, and the FC layers derive the final classification results by applying weights to these features. However, this approach does not fully reflect the interaction between the feature channels, potentially overlooking important relationships, and has the disadvantage of limiting nonlinearity. Therefore, the proposed method can preserve the relationships between the input channels and improves the nonlinear data-processing power, enabling a more accurate reconstruction. The synchronized radar ECG dataset consisted of synchronized data collected over 24 h through a CW radar and a reference device, the Task Force Monitor 3040i from CNSystems Medizintechnik GmbH(Graz, Austria) [20]. The equipment operated at 24 GHz in the ISM band based on a six-port technology. For efficient learning, a portion of the data acquired (data from 6 subjects out of 30 healthy subjects) were used. All subjects were required to complete a questionnaire on epidemiological data such as age, sex, weight, and medical history. In addition, the condition of the subjects was briefly checked by examining their blood pressure, heart rate, and heart sounds. If all the criteria were confirmed to be positive, the subject was included in the study, and their measurements were made available. This had been based on the six-port technology but was expanded into individual components to become a portable radar system. Figure 4 shows the CW radar signal and the corresponding ECG signal. We can recognize that it is almost impossible to identify any correlation between the CW radar signals and the corresponding reference ECG measurements. Figure 4: Plot of demodulated radar signal and synchronized ECG: (**a**) CW radar samples and (**b**) synchronized ECG samples. We used an Adam optimizer and chose to shuffle the data at every epoch for 600 epochs as the training option parameters. The first convolution1 dLayer was replaced by an MODWT layer. The MODWT layer was configured to have the same filter size and number of output channels to maintain the same number of learning parameters. Based on our previous observation, only components in a certain frequency range were kept. Instead of the conventional FC layer, we replaced the ANFN layer based on FCM clustering to effectively process the nonlinear characteristics and have a robust processing power for ambiguous results. Here, FCM clustering was selected for processing a high-dimensional representation. The rules were generated by the membership value of the input data point, and then the weights were obtained so that each rule could make a valid decision through the ANFN. Figure 5 shows the membership function of each input channel. We could obtain a higher accuracy compared to conventional linear processing by learning the center value, the sigma value of each MF, and the shape through the ANFN layer. Figure 6 shows some samples of reconstructed ECG signals and measured ECG signals. As shown in Figure 6, it can be confirmed that the proposed method predicted the pattern of the heart rate more accurately. Figure 7 compares the ECG signal reconstructed by the proposed model from CW radar signals recorded in a non-contact manner and the measured ECG signals were recorded in a non-contact manner. Figure 5: Membership function for each input channel. ECG signals. As shown in Figure 7, the experimental results reveal that the proposed deep learning model showed a good reconstruction performance with a small loss.