Pain Recognition With Electrocardiographic Features in Postoperative Patients: Method Validation Study

Abstract

Background There is a strong demand for an accurate and objective means of assessing acute pain among hospitalized patients to help clinicians provide pain medications at a proper dosage and in a timely manner. Heart rate variability (HRV) comprises changes in the time intervals between consecutive heartbeats, which can be measured through acquisition and interpretation of electrocardiography (ECG) captured from bedside monitors or wearable devices. As increased sympathetic activity affects the HRV, an index of autonomic regulation of heart rate, ultra–short-term HRV analysis can provide a reliable source of information for acute pain monitoring. In this study, widely used HRV time and frequency domain measurements are used in acute pain assessments among postoperative patients. The existing approaches have only focused on stimulated pain in healthy subjects, whereas, to the best of our knowledge, there is no work in the literature building models using real pain data and on postoperative patients. Objective The objective of our study was to develop and evaluate an automatic and adaptable pain assessment algorithm based on ECG features for assessing acute pain in postoperative patients likely experiencing mild to moderate pain. Methods The study used a prospective observational design. The sample consisted of 25 patient participants aged 18 to 65 years. In part 1 of the study, a transcutaneous electrical nerve stimulation unit was employed to obtain baseline discomfort thresholds for the patients. In part 2, a multichannel biosignal acquisition device was used as patients were engaging in non-noxious activities. At all times, pain intensity was measured using patient self-reports based on the Numerical Rating Scale. A weak supervision framework was inherited for rapid training data creation. The collected labels were then transformed from 11 intensity levels to 5 intensity levels. Prediction models were developed using 5 different machine learning methods. Mean prediction accuracy was calculated using leave-one-out cross-validation. We compared the performance of these models with the results from a previously published research study. Results Five different machine learning algorithms were applied to perform a binary classification of baseline (BL) versus 4 distinct pain levels (PL1 through PL4). The highest validation accuracy using 3 time domain HRV features from a BioVid research paper for baseline versus any other pain level was achieved by support vector machine (SVM) with 62.72% (BL vs PL4) to 84.14% (BL vs PL2). Similar results were achieved for the top 8 features based on the Gini index using the SVM method, with an accuracy ranging from 63.86% (BL vs PL4) to 84.79% (BL vs PL2). Conclusions We propose a novel pain assessment method for postoperative patients using ECG signal. Weak supervision applied for labeling and feature extraction improves the robustness of the approach. Our results show the viability of using a machine learning algorithm to accurately and objectively assess acute pain among hospitalized patients. International Registered Report Identifier (IRRID) RR2-10.2196/17783

A systematic review of neurophysiological sensing for the assessment of acute pain

“…The ECG signal is an indication of the heart’s contractile activity. The most significant parameters related to pain that have been derived from ECG signals are heart rate (HR) and heart rate variability (HRV) 46 . HR is a measure of the number (in heartbeats per minute or bpm) of contractions of the ventricles and HRV is a measure of the variance in time between heartbeats.…”

“…ECG can be measured on the thorax surface or in the limbs. Most surveyed studies used the thorax 34 – 41 , 49 , while other studies used either the wrists or the ankles 33 , 46 to monitor the heart activity.…”

“…Muscle noise (i.e., EMG cross-talk) also affects the ECG signals, in particular in recordings involving human activity or exercise. In the reviewed studies, a moving average window 40 , 46 or a linear detrend 37 , 49 were used to smooth the signal and remove baseline drift (0.05–1 Hz). A band-pass filter 35 , 38 , 39 was used to remove the baseline drift, movement (muscular and electrode) artefacts, and muscle cross-talk.…”

“… 39 several statistical features were further divided into stationarity, entropy, linearity, variability, and similarity. In addition, statistical features were obtained from most sensors, PPG 33 , 40 , 43 , 44 , ECG 36 – 41 , 46 , sEMG 34 , 37 – 39 , 41 , EDA 33 , 34 , 37 , 38 , 38 , 40 – 44 , MOVE 43 , 44 , PUPIL 56 , RESP 34 , 41 , and SKT 33 , 43 , 44 .…”

On the reliability of wearable technology: A tutorial on measuring heart rate and heart rate variability in the wild

“…The second source of cardiac variability – situational factors – refer to physical activity, stress level, and other more transient physiological states. For example elevated heart rate along with reduced HRV is associated with fever (Karjalainen & Viitasalo, 1986), inflammation (Williams et al, 2019), and acute pain (Chowdhury et al, 2021; Kasaeyan Naeini et al, 2021; Koenig et al, 2014; Lim et al, 2019), as well as mental stress (Brosschot et al, 2007; Hernando et al, 2018; Hovsepian et al, 2015) and physical effort (Perini & Veicsteinas, 2003; Tulppo et al, 1998). It is these situational factors that are spurring much of the current interest in wearable devices.…”