## 1 Introduction  
The patient population with end-stage renal disease (ESRD) is increasing worldwide [1]. Hemodialysis (HD) is one of the three renal replacement therapies for ESRD laterally with peritoneal dialysis and kidney transplant. HD is the second most convenient treatment after kidney transplant, but it has severe negative outcomes, as well as high costs and quality assurance (QA) difficulties [1]. The intradialytic hypotension rate is the principal HD difficulty since it is associated with high lethality and a loss of QA. In Mexico, more than 65% of clinics have poor QA figures and report hypotension rates above 30%, leading to unacceptable lethality rates as high as 21% in less than 5 years. This situation has concerned the Mexican Health Care Delivery System since 2011, which has devoted research efforts to find a new method for prognosis to prevent hypotension events in patients undergoing HD [2; 3]. The problem is that hypotension events frequently occur without any alert or previous notice since they depend on the cardiovascular (CV) response of each patient when they gradually lose blood fluids and solutes through HD treatment, despite the use of well-established medical standards, such as appropriate ultrafiltration rates and other HD therapy factors [2; 4]. According to the Kidney Disease Outcomes Quality Initiative (KDOQI) Guidelines 2015, hypotension is defined as a decrease in the systolic blood pressure by 20 mmHg or of the mean blood pressure by 10 mmHg, accompanied by symptoms such as nausea, vomiting, muscle cramps, and dizziness or fainting [4]. A specific difficulty well established in the literature is that the continuous monitoring of heart rate and mean blood pressure in clinical practice is not sufficient to prognose or prevent the CV system response [5; 6; 7]. The analysis of heart rate variability (HRV) has already proven its ability to indirectly assess the activity of the autonomic nervous system (ANS). HRV estimates the cyclic modulatory control of the CV system and as a consequence, the link to the blood pressure mechanism. The sympathetic and parasympathetic activity of the ANS is classically measured in the time or frequency domain by using linear tools [8; 9]. Specifically, the power spectral analysis of HRV requires a tachogram, which is built using the R-R intervals from a standard electrocardiogram (ECG). The standard is that the total power spectrum from 0 to 0.5 Hz is divided into four bandwidths, identified as ultra-low frequency (ULF, \(<\)0.003 Hz), very low frequency (VLF, 0.003-0.04 Hz), low frequency (LF, 0.04-0.15 Hz), and high frequency (HF, 0.15-0.4 Hz) bands. The normal assumption is that each bandwidth is related to different neural controls in the CV system [10]. Thus, the accepted premises are (a) that the HF band is associated with the activity of the parasympathetic nervous system (PNS), (b) that the LF band reflects the activity of the sympathetic nervous system (SNS) [11], (c) that the sympathovagal ratio (LF/HF) measures the proportion of the SNS and the PNS that interacts to control the CV system [12; 13], and (d) that the VLF and ULF bands are associated with slow-acting physiological processes, such as circadian rhythm, metabolism, thermoregulation, vasomotor tone, and the renin-angiotensin-aldosterone system (RAAS), which in turn participates in controlling blood pressure and has been closely linked to hypotension events [14; 15; 16; 17]. The spectral power of HRV can be estimated through several mathematical tools [18; 19; 20], including the fast Fourier transform, autoregressive models, and approximate entropy [21; 22]. However, these tools do not provide information related to power variation over time, which is useful in some studies. Therefore, it is important to run a spectrotemporal or time-frequency analysis of HRV. Tools like short-time Fourier transform (STFT), wavelet transform (WT), and the less known Hilbert-Huang Transform (HHT) have been explored for this purpose [23].