JMIR Publications

ABSTRACT

Background:

There is a strong demand for an accurate and objective means for assessing acute pain among hospitalized patients to help clinicians provide a proper dosage of pain medications 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 electrocardiogram (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 on 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:

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 threshold 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 (NRS). 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-Subject-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 binary classification of no pain (NP) vs. 4 distinct pain levels (PL1 through PL4). Highest validation accuracy using 3 time-domain HRV features of BioVid research paper for no pain vs. any other pain level was achieved by SVM 62.72% (NP vs. PL4) to 84.14% (NP 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% (NP vs. PL4) to 84.79% (NP vs. PL2).

Conclusions:

We propose a novel pain assessment method for postoperative patients using the 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.