An Edge-Assisted and Smart System for Real-Time Pain Monitoring

Naeini, Emad Kasaeyan; Shahhosseini, Sina; Subramanian, Ajan; Yin, Tingjue; Rahmani, Amir M.; Dutt, Nikil

doi:10.1109/chase48038.2019.00023

Cited by 16 publications

(5 citation statements)

References 28 publications

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“…It is thus meaningful to develop better tools to assess pain intensity for continuous real-time pain monitoring. Such a tool not only improves the care process of noncommunicative patients but can also benefit other patient populations with timelier treatment, accurate assessment, and reduced monitoring burden on clinicians [ 10 ].…”

Section: Introductionmentioning

confidence: 99%

“…Objective pain assessment leverages using wearable devices to capture the physiological parameters. Internet-of-Things (IoT) devices, including wearable devices, play a significant role in objective pain monitoring systems [ 10 ]. As an example, Vatankhah et al [ 14 ] measured and diagnosed pain levels of human using discrete wavelet transform via electroencephalographic signals.…”

Section: Introductionmentioning

confidence: 99%

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

Naeini¹,

Subramanian²,

Calderon³

et al. 2021

J Med Internet Res

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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

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Naeini¹,

Subramanian²,

Calderon³

et al. 2021

J Med Internet Res

Self Cite

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“…Wearable technology is a promising paradigm to integrate several technologies and communication solutions [ 12 , 13 ]. The aim of this research project is to develop an automatic and versatile pain assessment tool algorithm for detection and assessment of pain in a reliable and objective way in noncommunicative patients.…”

Section: Introductionmentioning

confidence: 99%

Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement

Naeini¹,

Jiang²,

Syrjälä³

et al. 2020

JMIR Res Protoc

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Background Assessment of pain is critical to its optimal treatment. There is a high demand for accurate objective pain assessment for effectively optimizing pain management interventions. However, pain is a multivalent, dynamic, and ambiguous phenomenon that is difficult to quantify, particularly when the patient’s ability to communicate is limited. The criterion standard of pain intensity assessment is self-reporting. However, this unidimensional model is disparaged for its oversimplification and limited applicability in several vulnerable patient populations. Researchers have attempted to develop objective pain assessment tools through analysis of physiological pain indicators, such as electrocardiography, electromyography, photoplethysmography, and electrodermal activity. However, pain assessment by using only these signals can be unreliable, as various other factors alter these vital signs and the adaptation of vital signs to pain stimulation varies from person to person. Objective pain assessment using behavioral signs such as facial expressions has recently gained attention. Objective Our objective is to further the development and research of a pain assessment tool for use with patients who are likely experiencing mild to moderate pain. We will collect observational data through wearable technologies, measuring facial electromyography, electrocardiography, photoplethysmography, and electrodermal activity. Methods This protocol focuses on the second phase of a larger study of multimodal signal acquisition through facial muscle electrical activity, cardiac electrical activity, and electrodermal activity as indicators of pain and for building predictive models. We used state-of-the-art standard sensors to measure bioelectrical electromyographic signals and changes in heart rate, respiratory rate, and oxygen saturation. Based on the results, we further developed the pain assessment tool and reconstituted it with modern wearable sensors, devices, and algorithms. In this second phase, we will test the smart pain assessment tool in communicative patients after elective surgery in the recovery room. Results Our human research protections application for institutional review board review was approved for this part of the study. We expect to have the pain assessment tool developed and available for further research in early 2021. Preliminary results will be ready for publication during fall 2020. Conclusions This study will help to further the development of and research on an objective pain assessment tool for monitoring patients likely experiencing mild to moderate pain. International Registered Report Identifier (IRRID) DERR1-10.2196/17783

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“…A multitude of sensors can measure the relationship between foods and the individual through the dynamic health state variables [34]. These include readily available sensors that provide continuous data collection for blood glucose, heart rate, perspiration rate, and body temperature [19].…”

Section: Introductionmentioning

confidence: 99%

Personal Food Model

Rostami

Pandey

Nag

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Food is central to life. Food provides us with energy and foundational building blocks for our body and is also a major source of joy and new experiences. A significant part of the overall economy is related to food. Food science, distribution, processing, and consumption have been addressed by different communities using silos of computational approaches [29]. In this paper, we adopt a person-centric multimedia and multimodal perspective on food computing and show how multimedia and food computing are synergistic and complementary. Enjoying food is a truly multimedia experience involving sight, taste, smell, and even sound, that can be captured using a multimedia food logger. The biological response to food can be captured using multimodal data streams using available wearable devices. Central to this approach is the Personal Food Model. Personal Food Model is the digitized representation of the food-related characteristics of an individual. It is designed to be used in food recommendation systems to provide eating-related recommendations that improve the user's quality of life. To model the food-related characteristics of each person, it is essential to capture their food-related enjoyment using a Preferential Personal Food Model and their biological response to food using their Biological Personal Food Model. Inspired by the power of 3-dimensional color models for visual processing, we introduce a 6-dimensional taste-space for capturing culinary characteristics as well as personal preferences. We use event mining approaches to Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for thirdparty components of this work must be honored. For all other uses, contact the owner/author(s).

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An Edge-Assisted and Smart System for Real-Time Pain Monitoring

Cited by 16 publications

References 28 publications

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

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

Prospective Study Evaluating a Pain Assessment Tool in a Postoperative Environment: Protocol for Algorithm Testing and Enhancement

Personal Food Model

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