Physiological responses to pain in cancer patients: A systematic review

A novel approach for ECG signal classification using sliding Euclidean quantization and bitwise pattern encoding

This study aims to introduce a novel, computationally lightweight feature extraction technique called Sliding Euclidean Pattern Quantization (SEPQ), which encodes local morphological patterns of ECG signals using Euclidean distance-based binary representations within sliding windows. The proposed SEPQ method was evaluated using two ECG datasets. The first dataset contained three labeled classes (CHF, ARR, and NSR), while the second included four classes (ventricular beats (VB), supraventricular beats (SVB), fusion beats (FB), and NSR). Extracted features were classified using several machine learning models, with LightGBM achieving the highest performance-over 99% accuracy on the first dataset and above 93% on the second. A convolutional neural network (CNN) model was also employed for comparative analysis, both on raw data and in a hybrid configuration with SEPQ, yielding moderate yet noteworthy performance. Experimental results confirm that SEPQ offers a robust, interpretable, and highly accurate solution for ECG signal classification.

The Frequency of Massage Use in Nursing Research: Bibliometric and Visualization Analysis of Hotspots and Global Trends

PURPOSE

The aim of the study was to examine the hotspots and global trends of massage in nursing research.

METHODS

Based on 241 articles published between 1993 and 2023 obtained from the Web of Science database, methods such as trend analysis and keyword frequency analysis were employed to analyze the evolution of research over time and identify key topics. Additionally, topic clustering of abstracts was conducted to examine thematic areas and connections within massage research.

RESULTS

The analysis shows that the use of massage is prominent in specific fields such as oncology, pediatrics, gynecology, and obstetrics. An analysis of the most cited articles revealed that topics such as the effects of massage on cancer pain and anxiety and the use of complementary therapies in newborns were important. It was determined that the leading authors of massage research generally work in the fields of "massage," "nursing," "pain," "aromatherapy," "anxiety" and "complementary therapies." Collaboration between authors and countries, which is important in terms of global knowledge sharing, is almost nonexistent.

CONCLUSIONS

The results of the study show that massage research is particularly prominent in certain medical fields such as cancer and that studies on the effects of massage in these fields are important. Furthermore, the fact that the leading authors of massage research are often specialized in specific fields and that there is limited collaboration between authors plays an important role in determining the future directions of massage research and highlights the need to encourage interdisciplinary collaboration.

Pain detection using biometric information acquired by a wristwatch wearable device: a pilot study of spontaneous menstrual pain in healthy females

OBJECTIVE

Pain is subjective, and self-reporting pain might be challenging. Studies conducted to detect pain using biological signals and real-time self-reports pain are limited. We evaluated the feasibility of collecting pain data on healthy females' menstrual pain and conducted preliminary analysis.

RESULTS

Five healthy adult females participated. They wore two wristwatch devices (Silmee and Fitbit) and a Holter ECG (electrocardiogram) during menstruation to record the pain intensity and timing. Subsequently, we analyzed the correlation between heart and pulse rates and assessed pre- and post-pain biometric differences. We collected sixty pain records from five participants. The correlation coefficients between heart rate and pulse rate ranged from 0.79 to 0.95 with Holter ECG vs. Fitbit and 0.32 to 0.74 with Holter ECG vs. Silmee. Analysis revealed significant changes in motion frequency post-pain (p = 0.04). For abdominal pain with a numerical rating scale score of ≥ 4 (n = 13), motion frequency (p < 0.001) and pulse rate (p = 0.02) showed significant differences post-pain compared to baseline values. Healthy females could wear the wristwatch device in daily life and report pain in real time. Wristwatch devices can effectively collect biological data to detect moderate pain by focusing on acceleration and pulse rate.

Contactless radar-based heart rate estimation in palliative care - a feasibility study and possible use in symptom management

BACKGROUND

Heart rate (HR) monitoring is a medical standard to provide information about a patient's health status. In palliative care, relationship and social engagement are crucial therapeutic concepts. For fear of disrupting communication, social contact, and care, continuous HR monitoring is underutilised despite its potential to inform on symptom burden and therapeutic effects. This study investigates radar-based HR monitoring as an innovative and burden-free approach for palliative care patients, compares its accuracy with conventional ECG methods, and shows potential for therapeutic guidance.

METHODS

A single-centre, comparative clinical trial was conducted with palliative care patients at the ward of the Department of Palliative Medicine of the University Hospital of Erlangen. The HR measurements obtained with radar were compared with Holter ECG (study arm I, overnight) and Task Force® Monitor (TFM)-based ECG validation recordings (study arm II, one hour). In addition, long-term radar measurements without validation were analysed in comparison with clinical health records (study arm III).

RESULTS

Both validation methods showed correlation by scatter plot, modified Bland-Altman plot, and equivalence testing. N = 34 patients participated in study arm I. HR of 4,079 five-minute intervals was analysed. Radar measurements and ECG showed high agreement: difference of HRs was within [Formula: see text]5 bpm in 3780 of 4079 (92.67%) and within ±13.4 bpm ([Formula: see text]1.96 times the SD of the mean) in 3979 (97.55%) intervals, respectively. In study arm II, n = 19 patients participated. 57,048 heart beats were analysed. The HR difference was within [Formula: see text]5 bpm for 53,583 out of 57,048 beats (93.93%) and within [Formula: see text]8.2 bpm ( ± 1.96 times the SD of the mean) in 55,439 beats (97.25%), respectively. Arm III showed HR changes extracted from radar data in correlation with symptoms and treatment.

CONCLUSION

Radar-based HR monitoring shows a high agreement in comparison with ECG-based HR monitoring and thus offers an option for continuous and above all burden-free HR assessment, with the potential for use in symptom management in palliative care, among others. Further research and technological advancements are still necessary to fully realize this innovative approach in enhancing palliative care practices.

Automatic Pain Detection Algorithm for Patients with Cancer Pain Using Wristwatch Wearable Devices

Pain assessment becomes challenging for patients unable to self-report, given the subjective nature of pain. This study introduces an automatic pain detection model utilizing biological signals from wristwatch wearables and time series data from patients with cancer experiencing pain. Biological signals and pain data were obtained from 10 patients with cancer pain for 7 days during their hospitalization. A total of 73,154 minutes of data and 407 pain reports were obtained. We developed automatic classifiers to detect moderate or severe pain and pain above the personalized pain goal by several machine learning algorithms using per-patient and mixed data sets. The best-performing algorithm achieved an F1 score of 0.87, with enhanced performance using the personalized pain goal as the cutoff. While the generalized model requires improvement, the study demonstrates the feasibility of automatic pain detection using extended real-world patient data.

Application of AI in Multilevel Pain Assessment Using Facial Images: Systematic Review and Meta-Analysis

BACKGROUND

The continuous monitoring and recording of patients' pain status is a major problem in current research on postoperative pain management. In the large number of original or review articles focusing on different approaches for pain assessment, many researchers have investigated how computer vision (CV) can help by capturing facial expressions. However, there is a lack of proper comparison of results between studies to identify current research gaps.

OBJECTIVE

The purpose of this systematic review and meta-analysis was to investigate the diagnostic performance of artificial intelligence models for multilevel pain assessment from facial images.

METHODS

The PubMed, Embase, IEEE, Web of Science, and Cochrane Library databases were searched for related publications before September 30, 2023. Studies that used facial images alone to estimate multiple pain values were included in the systematic review. A study quality assessment was conducted using the Quality Assessment of Diagnostic Accuracy Studies, 2nd edition tool. The performance of these studies was assessed by metrics including sensitivity, specificity, log diagnostic odds ratio (LDOR), and area under the curve (AUC). The intermodal variability was assessed and presented by forest plots.

RESULTS

A total of 45 reports were included in the systematic review. The reported test accuracies ranged from 0.27-0.99, and the other metrics, including the mean standard error (MSE), mean absolute error (MAE), intraclass correlation coefficient (ICC), and Pearson correlation coefficient (PCC), ranged from 0.31-4.61, 0.24-2.8, 0.19-0.83, and 0.48-0.92, respectively. In total, 6 studies were included in the meta-analysis. Their combined sensitivity was 98% (95% CI 96%-99%), specificity was 98% (95% CI 97%-99%), LDOR was 7.99 (95% CI 6.73-9.31), and AUC was 0.99 (95% CI 0.99-1). The subgroup analysis showed that the diagnostic performance was acceptable, although imbalanced data were still emphasized as a major problem. All studies had at least one domain with a high risk of bias, and for 20% (9/45) of studies, there were no applicability concerns.

CONCLUSIONS

This review summarizes recent evidence in automatic multilevel pain estimation from facial expressions and compared the test accuracy of results in a meta-analysis. Promising performance for pain estimation from facial images was established by current CV algorithms. Weaknesses in current studies were also identified, suggesting that larger databases and metrics evaluating multiclass classification performance could improve future studies.

TRIAL REGISTRATION

PROSPERO CRD42023418181; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=418181.

The Impact of Palliative Care on Mitigating Pain and Its Associated Effects in Determining Quality of Life among Colon Cancer Outpatients

Pain continues to be a significant problem for cancer patients, and the impact of a population-based strategy on their experiences is not completely understood. Our study aimed to determine the impact of palliative care on mitigating pain and its associated effects in determining the quality of life (QoL) among colon cancer outpatients. Six collection databases were used to perform a structured systematic review of the available literature, considering all papers published between the year 2000 and February 2023. PRISMA guidelines were adopted in our study, and a total of 9792 papers were evaluated. However, only 126 articles met the inclusion criteria. A precise diagnosis of disruptive colorectal cancer (CRC) pain disorders among patients under palliative care is necessary to mitigate it and its associated effects, enhance health, promote life expectancy, increase therapeutic responsiveness, and decrease comorbidity complications. Physical activities, the use of validated pain assessment tools, remote outpatient education and monitoring, chemotherapeutic pain reduction strategies, music and massage therapies, and bridging social isolation gaps are essential in enhancing QoL. We recommend and place a strong emphasis on the adoption of online training/or coaching programs and the integration of formal and informal palliative care systems for maximum QoL benefits among CRC outpatients.

Exploring Biosignals for Quantitative Pain Assessment in Cancer Patients: A Proof of Concept

Perception and expression of pain in cancer patients are influenced by distress levels, tumor type and progression, and the underlying pathophysiology of pain. Relying on traditional pain assessment tools can present limitations due to the highly subjective and multifaceted nature of the symptoms. In this scenario, objective pain assessment is an open research challenge. This work introduces a framework for automatic pain assessment. The proposed method is based on a wearable biosignal platform to extract quantitative indicators of the patient pain experience, evaluated through a self-assessment report. Two preliminary case studies focused on the simultaneous acquisition of electrocardiography (ECG), electrodermal activity (EDA), and accelerometer signals are illustrated and discussed. The results demonstrate the feasibility of the approach, highlighting the potential of EDA in capturing skin conductance responses (SCR) related to pain events in chronic cancer pain. A weak correlation (R = 0.2) is found between SCR parameters and the standard deviation of the interbeat interval series (SDRR), selected as the Heart Rate Variability index. A statistically significant (p < 0.001) increase in both EDA signal and SDRR is detected in movement with respect to rest conditions (assessed by means of the accelerometer signals) in the case of motion-associated cancer pain, thus reflecting the relationship between motor dynamics, which trigger painful responses, and the subsequent activation of the autonomous nervous system. With the objective of integrating parameters obtained from biosignals to establish pain signatures within different clinical scenarios, the proposed framework proves to be a promising research approach to define pain signatures in different clinical contexts.

Automated Electrodermal Activity and Facial Expression Analysis for Continuous Pain Intensity Monitoring on the X-ITE Pain Database

This study focuses on improving healthcare quality by introducing an automated system that continuously monitors patient pain intensity. The system analyzes the Electrodermal Activity (EDA) sensor modality modality, compares the results obtained from both EDA and facial expressions modalities, and late fuses EDA and facial expressions modalities. This work extends our previous studies of pain intensity monitoring via an expanded analysis of the two informative methods. The EDA sensor modality and facial expression analysis play a prominent role in pain recognition; the extracted features reflect the patient's responses to different pain levels. Three different approaches were applied: Random Forest (RF) baseline methods, Long-Short Term Memory Network (LSTM), and LSTM with the sample-weighting method (LSTM-SW). Evaluation metrics included Micro average F1-score for classification and Mean Squared Error (MSE) and intraclass correlation coefficient (ICC [3, 1]) for both classification and regression. The results highlight the effectiveness of late fusion for EDA and facial expressions, particularly in almost balanced datasets (Micro average F1-score around 61%, ICC about 0.35). EDA regression models, particularly LSTM and LSTM-SW, showed superiority in imbalanced datasets and outperformed guessing (where the majority of votes indicate no pain) and baseline methods (RF indicates Random Forest classifier (RFc) and Random Forest regression (RFr)). In conclusion, by integrating both modalities or utilizing EDA, they can provide medical centers with reliable and valuable insights into patients' pain experiences and responses.

Artificial Intelligence for Automatic Pain Assessment: Research Methods and Perspectives

Although proper pain evaluation is mandatory for establishing the appropriate therapy, self-reported pain level assessment has several limitations. Data-driven artificial intelligence (AI) methods can be employed for research on automatic pain assessment (APA). The goal is the development of objective, standardized, and generalizable instruments useful for pain assessment in different clinical contexts. The purpose of this article is to discuss the state of the art of research and perspectives on APA applications in both research and clinical scenarios. Principles of AI functioning will be addressed. For narrative purposes, AI-based methods are grouped into behavioral-based approaches and neurophysiology-based pain detection methods. Since pain is generally accompanied by spontaneous facial behaviors, several approaches for APA are based on image classification and feature extraction. Language features through natural language strategies, body postures, and respiratory-derived elements are other investigated behavioral-based approaches. Neurophysiology-based pain detection is obtained through electroencephalography, electromyography, electrodermal activity, and other biosignals. Recent approaches involve multimode strategies by combining behaviors with neurophysiological findings. Concerning methods, early studies were conducted by machine learning algorithms such as support vector machine, decision tree, and random forest classifiers. More recently, artificial neural networks such as convolutional and recurrent neural network algorithms are implemented, even in combination. Collaboration programs involving clinicians and computer scientists must be aimed at structuring and processing robust datasets that can be used in various settings, from acute to different chronic pain conditions. Finally, it is crucial to apply the concepts of explainability and ethics when examining AI applications for pain research and management.

Using artificial intelligence to improve pain assessment and pain management: a scoping review

CONTEXT

Over 20% of US adults report they experience pain on most days or every day. Uncontrolled pain has led to increased healthcare utilization, hospitalization, emergency visits, and financial burden. Recognizing, assessing, understanding, and treating pain using artificial intelligence (AI) approaches may improve patient outcomes and healthcare resource utilization. A comprehensive synthesis of the current use and outcomes of AI-based interventions focused on pain assessment and management will guide the development of future research.

OBJECTIVES

This review aims to investigate the state of the research on AI-based interventions designed to improve pain assessment and management for adult patients. We also ascertain the actual outcomes of Al-based interventions for adult patients.

METHODS

The electronic databases searched include Web of Science, CINAHL, PsycINFO, Cochrane CENTRAL, Scopus, IEEE Xplore, and ACM Digital Library. The search initially identified 6946 studies. After screening, 30 studies met the inclusion criteria. The Critical Appraisals Skills Programme was used to assess study quality.

RESULTS

This review provides evidence that machine learning, data mining, and natural language processing were used to improve efficient pain recognition and pain assessment, analyze self-reported pain data, predict pain, and help clinicians and patients to manage chronic pain more effectively.

CONCLUSIONS

Findings from this review suggest that using AI-based interventions has a positive effect on pain recognition, pain prediction, and pain self-management; however, most reports are only pilot studies. More pilot studies with physiological pain measures are required before these approaches are ready for large clinical trial.

An Automatic System for Continuous Pain Intensity Monitoring Based on Analyzing Data from Uni-, Bi-, and Multi-Modality

Pain is a reliable indicator of health issues; it affects patients' quality of life when not well managed. The current methods in the clinical application undergo biases and errors; moreover, such methods do not facilitate continuous pain monitoring. For this purpose, the recent methodologies in automatic pain assessment were introduced, which demonstrated the possibility for objectively and robustly measuring and monitoring pain when using behavioral cues and physiological signals. This paper focuses on introducing a reliable automatic system for continuous monitoring of pain intensity by analyzing behavioral cues, such as facial expressions and audio, and physiological signals, such as electrocardiogram (ECG), electromyogram (EMG), and electrodermal activity (EDA) from the X-ITE Pain Dataset. Several experiments were conducted with 11 datasets regarding classification and regression; these datasets were obtained from the database to reduce the impact of the imbalanced database problem. With each single modality (Uni-modality) experiment, we used a Random Forest [RF] baseline method, a Long Short-Term Memory (LSTM) method, and a LSTM using a sample weighting method (called LSTM-SW). Further, LSTM and LSTM-SW were used with fused modalities (two modalities = Bi-modality and all modalities = Multi-modality) experiments. Sample weighting was used to downweight misclassified samples during training to improve the performance. The experiments' results confirmed that regression is better than classification with imbalanced datasets, EDA is the best single modality, and fused modalities improved the performance significantly over the single modality in 10 out of 11 datasets.