## 3 Results
The HR signal exhibits significant frequency variations over time, indicative of natural fluctuations in heart rate due to physiological processes such as respiration, physical activity, etc. Dominant frequency components, corresponding to the fundamental heart rate and its harmonics, suggest periodicity in the HR signal, characteristic of regular cardiac cycles. Changes in the color intensity of the spectrograms reflect amplitude modulations in the HR signal, attributable to variations in cardiac output influenced by factors such as stress (which could have been the case for some of the subjects). Figure 9: (**A**) Tissue damping \(G_{r}\) (kPa s/L), tissue elastance \(H_{r}\) (kPa s/L), and tissue hysteresivity \(\eta_{r}\) for all subjects using the 4P-FOT device. (**B**) Tissue damping \(G_{r}\) (kPa s/L), tissue elastance \(H_{r}\) (kPa s/L), and tissue hysteresivity \(\eta_{r}\) for all subjects using RESMON. The HR spectrograms in Figures 10-12 demonstrate the utility of continuous EQV sensor monitoring combined with spectrogram analysis for the comprehensive assessment of heart rate dynamics, enabling real-time tracking of cardiovascular function and potentially improving clinical decision making and patient outcomes. This integration offers a robust framework for understanding the complex interactions between physiological processes and heart rate variability, with future studies having the potential to investigate the correlation between spectrogram features and specific clinical conditions to enhance the diagnostic and prognostic capabilities of this technique. To generate these spectrograms, the HR signal was first segmented into shorter intervals, each representing a five-minute segment of data. For each segment, a moving window Fourier transform was applied to compute the spectrogram. The window size and overlap were carefully chosen to balance time and frequency resolution, ensuring that both rapid and slow variations in the HR signal could be accurately captured. Figure 10: Spectrograms of heart rate variability across 24 intervals using EQV sensors. The parameters for the spectrograms were determined with a window size of 20 samples to balance frequency resolution and time localization, an overlap appropriate to ensure continuity and reduce spectral leakage, a frequency range of 0 to 0.5 Hz to capture typical HR variability components, and an FFT length of 20 to match the window size for an efficient computation. These parameters were optimized based on preliminary analyses using a grid search with the meshgrid and ndgrid functions, along with cross-validation by employing the crossval function in MATLAB--Mathworks, which are techniques commonly used in Artificial Intelligence (AI) tools--to ensure that the spectrograms accurately reflected the temporal and frequency dynamics of the HR signals. The HR spectrograms for all patients can be found in the Supplementary Materials. Figure 11: Spectrograms of heart rate variability across 24 intervals using EQV sensors. Figure 13 presents the correlation matrices between the impedance model parameters and the physiological parameters measured by the EQV sensor for six different subjects. Across all matrices, certain correlations consistently stand out. For instance, the parameter \(\eta_{r}\) shows a strong correlation with ECG Lead 1 in three subjects (subjects 1, 2, and 6), with values reaching up to 0.94. The heart rate (HR) also exhibits notable correlations with \(\mathrm{Hr}\) and \(\eta_{r}\) in multiple subjects, e.g., patient 1 and patient 6. Some correlations remain strong across different subjects, and others vary. For example, the correlation between the R and ECG Lead 1 is high for patient 3 but not consistently strong across others. Likewise, the parameter \(G_{r}\) shows moderate to high correlations with the HR in several subjects, indicating a less consistent but still significant relationship.