Acute pain intensity monitoring with the classification of multiple physiological parameters

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain.</p

Classification of experimental acute pain intensity with multimodal biosignals

Pain is an unpleasant sensation associated with an emotional experience. To provide an effective and safe pain treatment, accurate pain assessment is required. Until today, no adequate methods have been available to reliably detect or assess pain. Earlier studies have shown that some of the most common non-verbal features of acute pain include are facial grimaces and signs of increased autonomic activity. This thesis introduces a preclinical study where multimodal biosignals were collected from 31 healthy volunteers to build up a composite signal which would be as close to a pain-specific measure as possible. Continuous measurements of physiological parameters (heart rate, respiratory rate and galvanic skin response) were used together with several facial surface electromyography (sEMG) signals to classify three levels of self-reported pain intensity (no pain, mild pain, moderate/severe pain). Acute pain was simulated with a slowly increasing heat or electrical stimulus induction. Physiological signals were obtained as one value per second density, no additional feature extraction was performed. Facial sEMG values were adjusted with down sampling from the collection frequency of 1000 Hz to analysis frequency 1Hz with root mean square transformations. Meta-analysis was performed with kNN nested leave-one-subject-out (LOSO) cross-validation (CV). The final kNN analysis with non-nested LOSO CV showed 0.829 and 0.827 concordance on subject and test level self-reported pain intensity, respectively. Classification of experimental acute pain intensity with the composition of multimodal biosignals shows very promising results. The pain related quantitative measures, such as biosignals, provide valuable information. Combining further studies of the signals with the development of machine learning algorithms best suitable for this domain, brings us closer to more reliable pain assessment and better pain management.Kipu on epämiellyttävä aistikokemus, johon liittyy tunneperäinen elämys. Tehokas ja turvallinen kivunhoito vaatii tarkkaa kivun arviointia. Toistaiseksi ei ole olemassa riittävän hyviä menetelmiä, joilla kipu havaitaan tai voidaan arvioida luotettavasti. Aiemmat tutkimukset ovat osoittaneet, että akuutin kivun tavallisimpiin nonverbaalisiin piirteisiin kuuluvat irvistys kasvoilla ja autonomisen aktiivisuuden lisääntymisen merkit. Tämä tutkielma esittelee prekliinisen tutkimuksen, jossa keräämällä multimodaalisia biosignaaleja 31:ltä terveeltä vapaaehtoiselta, rakennettiin komposiittisignaali, joka vastaisi mahdollisimman hyvin kipu-spesifistä mittausta. Kolmea kipuastetta (ei kipua, lievä kipu, kohtalainen/kova kipu) luokiteltiin jatkuvasti mitattavien fysiologisten parametrien (sydämen syke, hengitystiheys, galvaaninen ihoreaktio) sekä useiden kasvojen pinnallisten elektromyografisten mittausten avulla. Akuuttia kipua simuloitiin aiheuttamalla hitaasti lisääntyvää lämpö- tai sähköstimulaatiota. Fysiologisia signaaleja käytettiin sekunnin tarkkuudella, ilman lisäpiirteiden luomista. Kasvojen sEMG-arvot säädettiin keräystaajuudesta 1000 Hz analyysitaajuuteen 1 Hz neliöllisen keskiarvon (RMS) muunnoksella. Meta-analyysi tehtiin kNN-menetelmä käyttäen nestatulla, henkilöittäin ositetulla (LOSO), ristiinvalidoinnilla. Lopullinen kNN-analyysi, joka suoritettiin ei-nestatulla LOSO-ristiinvalidoinnilla, tuotti 0.829 ja 0.827 konkordanssin henkilö- sekä testitasolla mitattuina. Kokeellisen akuutin kiputason luokittelu multimodaalisten biosignaalien kompositiolla osoittaa hyvin lupaavia tuloksia. Kipuun liittyvät kvantitatiiviset mittaukset, kuten biosignaalit, tuottavat arvokasta informaatiota. Tätä tietoa eteenpäin tutkimalla ja sille parhaiten sopivia koneoppimisalgoritmeja kehittämällä pääsemme lähemmäs luotettavampia kivun arviointimenetelmiä ja parempaa kivun hallintaa

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography.

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography.

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience

Developing a pain intensity prediction model using facial expression: A feasibility study with electromyography.

The automatic detection of facial expressions of pain is needed to ensure accurate pain assessment of patients who are unable to self-report pain. To overcome the challenges of automatic systems for determining pain levels based on facial expressions in clinical patient monitoring, a surface electromyography method was tested for feasibility in healthy volunteers. In the current study, two types of experimental gradually increasing pain stimuli were induced in thirty-one healthy volunteers who attended the study. We used a surface electromyography method to measure the activity of five facial muscles to detect facial expressions during pain induction. Statistical tests were used to analyze the continuous electromyography data, and a supervised machine learning was applied for pain intensity prediction model. Muscle activation of corrugator supercilii was most strongly associated with self-reported pain, and the levator labii superioris and orbicularis oculi showed a statistically significant increase in muscle activation when the pain stimulus reached subjects' self -reported pain thresholds. The two strongest features associated with pain, the waveform length of the corrugator supercilii and levator labii superioris, were selected for a prediction model. The performance of the pain prediction model resulted in a c-index of 0.64. In the study results, the most detectable difference in muscle activity during the pain experience was connected to eyebrow lowering, nose wrinkling and upper lip raising. As the performance of the prediction model remains modest, yet with a statistically significant ordinal classification, we suggest testing with a larger sample size to further explore the variables that affect variation in expressiveness and subjective pain experience

Detection of Prostate Cancer Using Biparametric Prostate MRI, Radiomics, and Kallikreins: A Retrospective Multicenter Study of Men With a Clinical Suspicion of Prostate Cancer

Background: Accurate detection of clinically significant prostate cancer (csPCa), Gleason Grade Group >= 2, remains a challenge. Prostate MRI radiomics and blood kallikreins have been proposed as tools to improve the performance of biparametric MRI (bpMRI). Purpose: To develop and validate radiomics and kallikrein models for the detection of csPCa. Study Type Retrospective. Population: A total of 543 men with a clinical suspicion of csPCa, 411 (76%, 411/543) had kallikreins available and 360 (88%, 360/411) did not take 5-alpha-reductase inhibitors. Two data splits into training, validation (split 1: single center, n = 72; split 2: random 50% of pooled datasets from all four centers), and testing (split 1: 4 centers, n = 288; split 2: remaining 50%) were evaluated. Field strength/Sequence: A 3 T/1.5 T, TSE T2-weighted imaging, 3x SE DWI. Assessment: In total, 20,363 radiomic features calculated from manually delineated whole gland (WG) and bpMRI suspicion lesion masks were evaluated in addition to clinical parameters, prostate-specific antigen, four kallikreins, MRI-based qualitative (PI-RADSv2.1/IMPROD bpMRI Likert) scores. Statistical Tests: For the detection of csPCa, area under receiver operating curve (AUC) was calculated using the DeLong's method. A multivariate analysis was conducted to determine the predictive power of combining variables. The values of P-value Results: The highest prediction performance was achieved by IMPROD bpMRI Likert and PI-RADSv2.1 score with AUC = 0.85 and 0.85 in split 1, 0.85 and 0.83 in split 2, respectively. bpMRI WG and/or kallikreins demonstrated AUCs ranging from 0.62 to 0.73 in split 1 and from 0.68 to 0.76 in split 2. AUC of bpMRI lesion-derived radiomics model was not statistically different to IMPROD bpMRI Likert score (split 1: AUC = 0.83, P-value = 0.306; split 2: AUC = 0.83, P-value = 0.488). Data Conclusion The use of radiomics and kallikreins failed to outperform PI-RADSv2.1/IMPROD bpMRI Likert and their combination did not lead to further performance gains.Level of Evidence: 1 Technical Efficacy: Stage 2</p

Acute pain intensity monitoring with the classification of multiple physiological parameters.

Current acute pain intensity assessment tools are mainly based on self-reporting by patients, which is impractical for non-communicative, sedated or critically ill patients. In previous studies, various physiological signals have been observed qualitatively as a potential pain intensity index. On the basis of that, this study aims at developing a continuous pain monitoring method with the classification of multiple physiological parameters. Heart rate (HR), breath rate (BR), galvanic skin response (GSR) and facial surface electromyogram were collected from 30 healthy volunteers under thermal and electrical pain stimuli. The collected samples were labelled as no pain, mild pain or moderate/severe pain based on a self-reported visual analogue scale. The patterns of these three classes were first observed from the distribution of the 13 processed physiological parameters. Then, artificial neural network classifiers were trained, validated and tested with the physiological parameters. The average classification accuracy was 70.6%. The same method was applied to the medians of each class in each test and accuracy was improved to 83.3%. With facial electromyogram, the adaptivity of this method to a new subject was improved as the recognition accuracy of moderate/severe pain in leave-one-subject-out cross-validation was promoted from 74.9 ± 21.0 to 76.3 ± 18.1%. Among healthy volunteers, GSR, HR and BR were better correlated to pain intensity variations than facial muscle activities. The classification of multiple accessible physiological parameters can potentially provide a way to differentiate among no, mild and moderate/severe acute experimental pain