Postoperative Pain Assessment Indices Based on Photoplethysmography Waveform Analysis

Effect of the Location of Tetanic Stimulation on Autonomic Responses: A Randomized Cross-Over Pilot Study

Background

Tetanic stimuli are used as standardized noxious inputs to investigate nociception. Previous studies have applied tetanic stimuli to various anatomical locations without validating that the resulting physiological responses were independent of the location where tetanic stimuli were applied. Our aim was to investigate the effects of three anatomical tetanic stimulus application sites on physiological variables reflecting autonomic nervous system responses as measured by photoplethysmography (PPG).

Methods

Under general anesthesia, a five second, 100 hertz, 70 milliamp tetanic stimulus was applied to the ulnar nerve, medial side of the tibia, and thorax (T5 dermatome) (N=12). The effect of tetanic stimuli on PPG-derived variables (AC, DC, and ACDC) and pulse rate at each stimulus location was determined using repeated-measures analysis of variance (ANOVA) followed by Dunnett's post hoc test. Maximum tetanic stimulus-induced changes in PPG-derived variables and pulse rates were compared among the three stimulus locations using ANOVA.

Results

AC and ACDC values of PPG decreased, and the DC values of PPG increased in response to tetanic stimuli-induced vasoconstriction at each location (p<0.001 for all). The maximum changes in the AC, ACDC, and DC values did not differ between locations (p=NS). There were no significant changes in pulse rate (p=NS).

Conclusion

The results showed that tetanic stimulation at either of these three locations provides the same autonomic nervous system responses, as measured by PPG.

Clinical Trial Registration

ClinicalTrials.gov; NCT03648853.

Use of Wearable Sensor Device and Mobile Application for Objective Assessment of Pain in Post-surgical Patients: A Preliminary Study

Effective post-operative pain management requires an accurate and frequent assessment of the pain experienced by the patients. The current gold-standard of pain assessment is through patient self-evaluation (e.g., numeric rating scale, NRS) which is subjective, prone to recall-bias, and does not provide comprehensive information of the pain intensity and its trends. We conducted a study to explore the potential of wearable biosensors and machine learning-based analysis of physiological parameters to estimate the pain intensity. The results from our study of post-operative knee surgery patients monitored over a period of 30 days demonstrate the feasibility of the system in ambulatory setting, with a substantial agreement (Cohen's Kappa = 0.70, 95% CI 0.68-0.72) between the pain intensity estimation and the patient reported numerical rating scale. Therefore, the wearable biosensors coupled with the machine learning-derived pain estimation are capable of remotely assessing the pain intensity.

Preliminary study: quantification of chronic pain from physiological data

Supplemental Digital Content is Available in the Text.

Preliminary evidence suggests that physiological variables collected with our low-cost pain meter are correlated with chronic pain, both for individuals and populations.

Introduction:

It is unknown if physiological changes associated with chronic pain could be measured with inexpensive physiological sensors. Recently, acute pain and laboratory-induced pain have been quantified with physiological sensors.

Objectives:

To investigate the extent to which chronic pain can be quantified with physiological sensors.

Methods:

Data were collected from chronic pain sufferers who subjectively rated their pain on a 0 to 10 visual analogue scale, using our recently developed pain meter. Physiological variables, including pulse, temperature, and motion signals, were measured at head, neck, wrist, and finger with multiple sensors. To quantify pain, features were first extracted from 10-second windows. Linear models with recursive feature elimination were fit for each subject. A random forest regression model was used for pain score prediction for the population-level model.

Results:

Predictive performance was assessed using leave-one-recording-out cross-validation and nonparametric permutation testing. For individual-level models, 5 of 12 subjects yielded intraclass correlation coefficients between actual and predicted pain scores of 0.46 to 0.75. For the population-level model, the random forest method yielded an intraclass correlation coefficient of 0.58. Bland–Altman analysis shows that our model tends to overestimate the lower end of the pain scores and underestimate the higher end.

Conclusion:

This is the first demonstration that physiological data can be correlated with chronic pain, both for individuals and populations. Further research and more extensive data will be required to assess whether this approach could be used as a “chronic pain meter” to assess the level of chronic pain in patients.

Emotion Recognition: Photoplethysmography and Electrocardiography in Comparison

Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests.

Deep convolutional neural network-based signal quality assessment for photoplethysmogram

Quality assessment of bio-signals is important to prevent clinical misdiagnosis. With the introduction of mobile and wearable health care, it is becoming increasingly important to distinguish available signals from noise. The goal of this study was to develop a signal quality assessment technology for photoplethysmogram (PPG) widely used in wearable healthcare. In this study, we developed and verified a deep neural network (DNN)-based signal quality assessment model using about 1.6 million 5-s segment length PPG big data of about 29 GB from the MIMIC III PPG waveform database. The DNN model was implemented through a 1D convolutional neural network (CNN). The number of CNN layers, number of fully connected nodes, dropout rate, batch size, and learning rate of the model were optimized through Bayesian optimization. As a result, 6 CNN layers, 1,546 fully connected layer nodes, 825 batch size, 0.2 dropout rate, and 0.002 learning rate were needed for an optimal model. Performance metrics of the result of classifying waveform quality into 'Good' and 'Bad', the accuracy, specificity, sensitivity, area under the receiver operating curve, and area under the precision-recall curve were 0.978, 0.948, 0.993, 0.985, 0.980, and 0.969, respectively. Additionally, in the case of simulated real-time application, it was confirmed that the proposed signal quality score tracked the decrease in pulse quality well.

Photoplethysmogram Analysis and Applications: An Integrative Review

Beyond its use in a clinical environment, photoplethysmogram (PPG) is increasingly used for measuring the physiological state of an individual in daily life. This review aims to examine existing research on photoplethysmogram concerning its generation mechanisms, measurement principles, clinical applications, noise definition, pre-processing techniques, feature detection techniques, and post-processing techniques for photoplethysmogram processing, especially from an engineering point of view. We performed an extensive search with the PubMed, Google Scholar, Institute of Electrical and Electronics Engineers (IEEE), ScienceDirect, and Web of Science databases. Exclusion conditions did not include the year of publication, but articles not published in English were excluded. Based on 118 articles, we identified four main topics of enabling PPG: (A) PPG waveform, (B) PPG features and clinical applications including basic features based on the original PPG waveform, combined features of PPG, and derivative features of PPG, (C) PPG noise including motion artifact baseline wandering and hypoperfusion, and (D) PPG signal processing including PPG preprocessing, PPG peak detection, and signal quality index. The application field of photoplethysmogram has been extending from the clinical to the mobile environment. Although there is no standardized pre-processing pipeline for PPG signal processing, as PPG data are acquired and accumulated in various ways, the recently proposed machine learning-based method is expected to offer a promising solution.

A Data-Driven Model with Feedback Calibration Embedded Blood Pressure Estimator Using Reflective Photoplethysmography

Ambulatory blood pressure (BP) monitoring (ABPM) is vital for screening cardiovascular activity. The American College of Cardiology/American Heart Association guideline for the prevention, detection, evaluation, and management of BP in adults recommends measuring BP outside the office setting using daytime ABPM. The recommendation to use night-day BP measurements to confirm hypertension is consistent with the recommendation of several other guidelines. In recent studies, ABPM was used to measure BP at regular intervals, and it reduces the effect of the environment on BP. Out-of-office measurements are highly recommended by almost all hypertension organizations. However, traditional ABPM devices based on the oscillometric technique usually interrupt sleep. For all-day ABPM purposes, a photoplethysmography (PPG)-based wrist-type device has been developed as a convenient tool. This optical, noninvasive device estimates BP using morphological characteristics from PPG waveforms. As measurement can be affected by multiple variables, calibration is necessary to ensure that the calculated BP values are accurate. However, few studies focused on adaptive calibration. A novel adaptive calibration model, which is data-driven and embedded in a wearable device, was proposed. The features from a 15 s PPG waveform and personal information were input for estimation of BP values and our data-driven calibration model. The model had a feedback calibration process using the exponential Gaussian process regression method to calibrate BP values and avoid inter- and intra-subject variability, ensuring accuracy in long-term ABPM. The estimation error of BP (ΔBP = actual BP-estimated BP) of systolic BP was -0.1776 ± 4.7361 mmHg; ≤15 mmHg, 99.225%, and of diastolic BP was -0.3846 ± 6.3688 mmHg; ≤15 mmHg, 98.191%. The success rate was improved, and the results corresponded to the Association for the Advancement of Medical Instrumentation standard and British Hypertension Society Grading criteria for medical regulation. Using machine learning with a feedback calibration model could be used to assess ABPM for clinical purposes.

Normalization of photoplethysmography using deep neural networks for individual and group comparison

Photoplethysmography (PPG) is easy to measure and provides important parameters related to heart rate and arrhythmia. However, automated PPG methods have not been developed because of their susceptibility to motion artifacts and differences in waveform characteristics among individuals. With increasing use of telemedicine, there is growing interest in application of deep neural network (DNN) technology for efficient analysis of vast amounts of PPG data. This study is about an algorithm for measuring a patient's PPG and comparing it with their own data stored previously and with the average data of several groups. Six deep neural networks were used to normalize the PPG waveform according to the heart rate by removing uninformative regions from the PPG, distinguishing between heartbeat and reflection pulses, dividing the heartbeat waveform into 10 segments and averaging the values according to each segments. PPG data were measured using telemedicine in both groups. Group 1 consisted of healthy people aged 25 to 35 years, and Group 2 consisted of patients between 60 and 75 years of age taking antihypertensive medications. The proposed algorithm could accurately determine which group the subject belonged with the newly measured PPG data (AUC = 0.998). On the other hand, errors were frequently observed in identification of individuals (AUC = 0.819).

Physiological effects of mixed-gas deep sea dives using a closed-circuit rebreather: a field pilot study

PURPOSE

Deep diving using mixed gas with closed-circuit rebreathers (CCRs) is increasingly common. However, data regarding the effects of these dives are still scarce. This preliminary field study aimed at evaluating the acute effects of deep (90-120 msw) mixed-gas CCR bounce dives on lung function in relation with other physiological parameters.

METHODS

Seven divers performed a total of sixteen open-sea CCR dives breathing gas mixture of helium, nitrogen and oxygen (trimix) within four days at 2 depths (90 and 120 msw). Spirometric parameters, SpO₂, body mass, hematocrit, short term heart rate variability (HRV) and critical flicker fusion frequency (CFFF) were measured at rest 60 min before the dive and 120 min after surfacing.

RESULTS

The median [1st-3rd quartile] of the forced vital capacity was lower (84% [76-93] vs 91% [74-107] of predicted values; p = 0.029), whereas FEV1/FVC was higher (98% [95-99] vs 95% [89-99]; p = 0.019) after than before the dives. The other spirometry values and SpO₂ were unchanged. Body mass decreased from 73.5 kg (72.0-89.6) before the dives to 70.0 kg (69.2-85.8) after surfacing (p = 0.001), with no change of hematocrit or CFFT. HRV was increased as indicated by the higher SDNN, RMSSD and pNN50 after than before dives.

CONCLUSION

The present observation represents the first original data regarding the effects of deep repeated CCR dives. The body mass loss and decrease of FVC after bounce dives at depth of about 100 msw may possibly impose an important physiological stress for the divers.

Novel Analgesic Index for Postoperative Pain Assessment Based on a Photoplethysmographic Spectrogram and Convolutional Neural Network: Observational Study

Background

Although commercially available analgesic indices based on biosignal processing have been used to quantify nociception during general anesthesia, their performance is low in conscious patients. Therefore, there is a need to develop a new analgesic index with improved performance to quantify postoperative pain in conscious patients.

Objective

This study aimed to develop a new analgesic index using photoplethysmogram (PPG) spectrograms and a convolutional neural network (CNN) to objectively assess pain in conscious patients.

Methods

PPGs were obtained from a group of surgical patients for 6 minutes both in the absence (preoperatively) and in the presence (postoperatively) of pain. Then, the PPG data of the latter 5 minutes were used for analysis. Based on the PPGs and a CNN, we developed a spectrogram–CNN index for pain assessment. The area under the curve (AUC) of the receiver-operating characteristic curve was measured to evaluate the performance of the 2 indices.

Results

PPGs from 100 patients were used to develop the spectrogram–CNN index. When there was pain, the mean (95% CI) spectrogram–CNN index value increased significantly—baseline: 28.5 (24.2-30.7) versus recovery area: 65.7 (60.5-68.3); P<.01. The AUC and balanced accuracy were 0.76 and 71.4%, respectively. The spectrogram–CNN index cutoff value for detecting pain was 48, with a sensitivity of 68.3% and specificity of 73.8%.

Conclusions

Although there were limitations to the study design, we confirmed that the spectrogram–CNN index can efficiently detect postoperative pain in conscious patients. Further studies are required to assess the spectrogram–CNN index’s feasibility and prevent overfitting to various populations, including patients under general anesthesia.

Trial Registration

Clinical Research Information Service KCT0002080; https://cris.nih.go.kr/cris/search/search_result_st01.jsp?seq=6638

Hepatoprotective effect of the unsaponifiable matter from olive, linseed and sesame oils against carbon tetrachloride-induced liver injury in rats

In the present study, the hepatoprotective activity of the unsaponifiable matter (UNSAP) of olive oil, linseed, and sesame oils against CCl4-induced liver toxicity in rats was investigated. In a preliminary antioxidant study, UNSAP showed pronounced DPPH radical scavenging activity (IC50 6.2-10.8 mg/mL). The constituents of UNSAP were determined by GC-MS. The subcutaneous administration of CCl4, caused liver injury. The hepatoprotective effect of UNSAP was comparable to that of α-tocopherol, a standard antioxidant agent. The co-administration of the investigated UNSAP normalized the activities of serum marker enzymes, alanine aminotransferase (ALT), and aspartate aminotransferase (AST). Furthermore, the serum alkaline phosphatase (ALP) activity and hepatic malondialdehyde (MDA) level were found to be alleviated by pre-treatment with the UNSAP. A histopathological evaluation showed marked improvement in the liver of UNSAP- and α-Tocopherol-treated animals. The hepatoprotective effect could be attributed to the antioxidant characteristics of UNSAP.

Evaluation of Surgical Pleth Index and Analgesia Nociception Index as surrogate pain measures in conscious postoperative patients: an observational study

We evaluated the performance of the Surgical Plethysmographic Index (SPI) and the Analgesia Nociception Index (ANI) as surrogate pain measures and determined their respective cut-off values for detecting pain in conscious postoperative patients. In total, 192 patients after elective surgery were enrolled. Baseline SPI and ANI data were acquired for 10 min in the operating room prior to surgery when the patients rated their pain as 0 on the numerical rating scale (NRS). Upon arrival in the post-anaesthesia care unit (PACU) after surgery, SPI and ANI data were recorded for 10 min. The means of the recorded data at OR and PACU were defined as the values representing baseline and postoperative pain, respectively. SPI and ANI data obtained from 189 patients were analysed, who were anesthetized with propofol (n = 149) or sevoflurane (n = 40). Remifentanil was continuously infused intraoperatively in all patients. The values of SPI and ANI were significantly different in conscious patients without (NRS = 0) and with pain (NRS > 0). The areas under the receiver operating curves for SPI and ANI were 0.73 (P < 0.0001) and 0.67 (P < 0.0001), respectively. The cut-off values for SPI and ANI in predicting postoperative pain were 44 (sensitivity: 84%, specificity: 53%) and 63 (sensitivity: 52%, specificity: 82%), respectively, which are different from those suggested by their respective manufacturers for use in intraoperative state under general anaesthesia. The cut-off values of SPI and ANI for detecting pain were similar regardless of the type of anesthesia.

Respiratory variations in pulse pressure and photoplethysmographic waveform amplitude during positive expiratory pressure and continuous positive airway pressure in a model of progressive hypovolemia

PURPOSE

Respiratory variations in pulse pressure (dPP) and photoplethysmographic waveform amplitude (dPOP) are used for evaluation of volume status in mechanically ventilated patients. Amplification of intrathoracic pressure changes may enable their use also during spontaneous breathing. We investigated the association between the degree of hypovolemia and dPP and dPOP at different levels of two commonly applied clinical interventions; positive expiratory pressure (PEP) and continuous positive airway pressure (CPAP).

METHODS

20 healthy volunteers were exposed to progressive hypovolemia by lower body negative pressure (LBNP). PEP of 0 (baseline), 5 and 10 cmH2O was applied by an expiratory resistor and CPAP of 0 (baseline), 5 and 10 cmH2O by a facemask. dPP was obtained non-invasively with the volume clamp method and dPOP from a pulse oximeter. Central venous pressure was measured in 10 subjects. Associations between changes were examined using linear mixed-effects regression models.

RESULTS

dPP increased with progressive LBNP at all levels of PEP and CPAP. The LBNP-induced increase in dPP was amplified by PEP 10 cmH20. dPOP increased with progressive LBNP during PEP 5 and PEP 10, and during all levels of CPAP. There was no additional effect of the level of PEP or CPAP on dPOP. Progressive hypovolemia and increasing levels of PEP were reflected by increasing respiratory variations in CVP.

CONCLUSION

dPP and dPOP reflected progressive hypovolemia in spontaneously breathing healthy volunteers during PEP and CPAP. An increase in PEP from baseline to 10 cmH2O augmented the increase in dPP, but not in dPOP.

Postoperative Pain Assessment Model Based on Pulse Contour Characteristics Analysis

This study aims to develop a new postoperative pain assessment model based on pulse contour analysis and to evaluate its effectiveness in postoperative pain assessment. We derived candidate features from photoplethysmography (PPG) and developed an assessment model based on multiple logistic regressions with a combination of features. This study also includes investigations into the optimal unit of analysis and number of features. For model development, PPGs obtained from 78 surgical patients with a six-min duration in pre- and post-operation conditions, including a training set of 56 pairs and a test set of 22 pairs, were used. We tested models with 5, 10, 20, 30, 40, 50, 60, 70, and 80 beats as an analysis unit, and with 1 to 8 of features for optimization, then determined 20 beats and three features to be the simplest optimal unit of analysis and number of features, respectively. The selected features were RMSSD-ACV_onset/ACA_bl, AV-A_sys/A_total, and SD-RS, where RMSSD-ACV_onset/ACA_bl is the root mean square of the successive difference of the ratio of pulse onset amplitude to the pulse onset-to-peak amplitude, AV-A_sys/A_total is the average value of a normalized systolic area of a pulse with a total pulse area, and SD-RS is the standard deviation of a rising slope of a pulse. The accuracy (AC) and the area under the curve (AUC) of the proposed model were 0.793 and 0.872 in the development set (N = 56), respectively, which were superior to those of SPI (AC: 0.643, AUC: 0.716) and ANI (AC: 0.633 AUC: 0.671). In the test set (N = 22), the AC and AUC of the proposed model were 0.712 and 0.808, respectively, which were superior to those of SPI (AC: 0.640, AUC: 0.709) and ANI (AC: 0.640, AUC: 0.680).