Classification of blood pressure in critically ill patients using photoplethysmography and machine learning
Introduction
Blood pressure (BP), the force with which the blood is pumped around the circulatory system [1], is one of the main vital signs measured in clinical and non-clinical environments [2], [3]. It is usually reported as a ratio between the BP during the systole (systolic blood pressure, SBP) and diastole (diastolic blood pressure, DBP) [4]. Ideal BP values are considered to be between 90/60 and 120/80 mmHg, for diastolic and systolic BP respectively, whereas sustained high BP, also called hypertension, and sustained low BP, known as hypotension, are considered to be BP measurements higher than 140/90 mmHg and lower than 90/60 mmHg [1]. Both hypertension and hypotension are abnormal conditions that may affect the blood flow to tissues and, hence, regular BP monitoring is essential for the detection, prevention and treatment of related diseases [2].
Hypotension is related to light-headedness, blurred vision, weakness, confusion, and fainting, but also to pregnancy, medicines, and medical conditions, such as diabetes mellitus [5]. Some kinds of hypotension are now considered clinically significant and recognized as a cause of impairment of quality of life and potentially worse outcomes [6]. Hypertension, on the other hand, is a condition in which the BP values are persistently high, reflecting an increase in the force applied by the heart to pump the blood throughout the circulatory system [7]. Although it usually does not have noticeable symptoms, hypertension increases the risk of various serious disorders, such as heart attacks and strokes [8], [9]. Since most patients are asymptomatic at the early stages of hypertension, it is usually detected only after substantial vascular damage has occurred [4] and when more serious diseases appear, not only in the cardiovascular system but in other vital organs [2], [10], [11].
The measurement of BP has been traditionally performed using invasive, direct methods based on catheters, or non-invasive indirect techniques based on the inflation of a cuff [2]. However, both techniques pose several challenges and limitations. The invasive alternative can only be performed during surgery or in patients in intensive care units and carries with an increased risk of infection [2]. On the other hand, although the cuff-based methods can be applied in any environment, it does not allow for continuous measurements due to the inflation and deflation of the cuff, and having repetitive measures in short periods can be cumbersome and impractical for certain applications, such as in sleep studies [12], [13].
In the last few decades, increased attention has been given to non-invasive, continuous and cuff-less alternatives for the estimation of BP [9], [11], [14]. Most of these novel techniques are based on the analysis of physiological signals, especially photoplethysmography (PPG) [2], [14], [15]. PPG is a non-invasive, optical technique, used for the measurement of blood volume changes in peripheral tissue [16], and due to its widespread use and simplicity, is one of the most commonly used signals in wearable devices [17]. Some proposed strategies for the estimation of BP using PPG are based on machine learning (ML) algorithms and the extraction of PPG features that may reflect BP-related changes in PPG signals [2], [3]. With the increasing availability of data and the development of powerful ML techniques, BP estimation based on this kind of analysis seems like a promising alternative for the ubiquitous, continuous measurement of BP values, as well as for the identification of hypertensive and hypotensive events in a real-time manner.
Blood pressure is primarily regulated by the sympathoadrenal system on a beat-to-beat basis [4] and PPG-based Pulse Rate Variability (PRV) might contain information related to BP that allows for the identification of hypertension and hypotension using ML techniques. PRV refers to the changes in pulse rate over time and has been applied to the indirect assessment of autonomic activity in our previous work [17]. Moreover, PRV has been associated with changes in PTT [18], [19], and the appearance of essential hypertension (i.e. high BP without underlying conditions) has been related with autonomic dysfunction [20]. Therefore, the aim of this study was to evaluate the applicability of PRV for the identification of hypertensive, normotensive and hypotensive events in critically ill patients, and to assess the capability of PRV-based ML algorithms for the estimation of BP values.
Section snippets
Signal acquisition
A subset of 500 records was obtained from the MIMIC-II Waveform Database [21], [22]. The MIMIC-II database is a freely available, de-identified database which contains physiological signal and vital signs time series obtained from critically ill, adult patients receiving treatment in an intensive care unit (ICU). Waveforms available in the records of this database include continuous high-resolution physiological signals and numeric time series of physiological measurements. In the selected
Signal selection and segmentation
The data set was filtered and 230 records with poor-quality ABP signals and duration of less than 5-min were discarded. The signals from the remaining 270 records were used in the subsequent analysis. From these signals, 4937 5-min segments were extracted and merged, of which 54% were labeled as hypertensive, 25% as hypotensive, and the remaining 22% as normotensive events. Similarly, 11417 1-min segments were obtained and merged. From these, 51% corresponded to hypertensive events, 31% to
Discussion
PRV has been used in several applications in recent years, due to its relationship with Heart Rate Variability (HRV) and to the simplicity and ubiquity of PPG. Although it is not yet clear if PRV can be considered as a valid surrogate of HRV, it has been increasingly studied as a marker of several somatic and mental disorders, and showed promise in the identification of cardiovascular changes [17].
In this study, PRV was used for the identification of BP states and the estimation of BP values in
Conclusion
The aim of this study was to evaluate the applicability of PRV-derived features for the classification and estimation of BP values using ML algorithms, using data obtained from critically ill subjects. The results obtained show promise for the use of PRV for both tasks, although several aspects should be further optimised in order to increase performance, especially for the classification of BP states. Specifically, the identification of hypertensive events in critically ill subjects using the
Declaration of Competing Interest
There are no conflicts of interest in this study.
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