###### Abstract  
Continuous monitoring plays a crucial role in diagnosing hypertension, characterized by the increase in Arterial Blood Pressure (ABP). The gold-standard method for obtaining ABP involves the uncomfortable and invasive technique of cannulation. Conversely, ABP can be acquired non-invasively by using Photoplethysmography (PPG). This non-invasive approach offers the advantage of continuous BP monitoring outside a hospital setting and can be implemented in cost-effective wearable devices. PPG and ABP signals differ in scale values, which creates a non-linear relationship, opening avenues for the utilization of algorithms capable of detecting non-linear associations. In this study, we introduce Neural Model of Blood Pressure (NeuBP), which estimates systolic and diastolic values from PPG signals. The problem is treated as a binary classification task, distinguishing between Normotensive and Hypertensive categories. Furthermore, our research investigates NeuBP's performance in classifying different BP categories, including Normotensive, Prehypertensive, Grade 1 Hypertensive, and Grade 2 Hypertensive cases. We evaluate our proposed method by using data from the publicly available MIMIC-III database. The experimental results demonstrate that NeuBP achieves results comparable to more complex models with fewer parameters. The mean absolute errors for systolic and diastolic values are 5.02 mmHg and 3.11 mmHg, respectively. photoplethysmograph; arterial blood pressure; dilated convolution; hypertension; continuous monitoring +
Footnote †: journal: Medical  
flow: Electrocardiography (ECG) and Photoplethysmography (PPG). ECG signals are captured through devices employing either disposable electrodes placed on the torso or textile electrodes, similar to those used in conventional heart rate monitors [13]. This technique plays a pivotal role in diagnosing a wide range of cardiac arrhythmias [14]. In comparison, PPG signals are obtained non-invasively at a low cost, aiming to measure changes in blood flow through light absorption and reflection on the skin [15]. In clinical settings, PPG signals are found in pulse oximeters (SpO2) used to measure oxygen saturation. Data provided by these devices enable the extraction of essential physiological parameters, including heart rate, respiratory rate, and tissue/vascular perfusion, enabling the identification of heart conditions, all without the need for invasive blood pressure measurements [16]. Recent technological advancements have paved the way for the development of wearable devices, with a prominent focus on the utilization of PPG sensors capable of capturing and processing human biosignals. These sensors can be seamlessly integrated into a range of wearable accessories, including wristbands, watches, eyewear, headphones, belts, and smart jewelry [17]. In addition, there is a promising avenue that utilizes PPG signals for the estimation of Arterial Blood Pressure (ABP), a parameter traditionally associated with invasive techniques. PPG signals inherently exhibit a close relationship with ABP due to the fluctuations in blood flow. This intrinsic connection allows for the estimation of ABP through the inherent characteristics of PPG signals, achieved through the application of statistical techniques or machine learning algorithms [18; 19]. ABP estimation through PPG signals adheres to criteria set by the Association for the Advancement of Medical Instrumentation. Methods include the calculation of Pulse Transit Time (PTT), representing the interval of time taken by blood to reach venous terminations during a heartbeat [20], and of Pulse Wave Velocity (PWV), associated with blood speed in vessels [21]. The related literature highlights the existence of various proposals employing modules for estimating blood pressure through PPG signals [22; 23; 24]. However, most of these approaches rely on manual feature extraction processes.