Physiological Measurement     

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Detection of acute periodontal pain from physiological signalsTo cite this article: Daniel Teichmann et al 2018 Physiol. Meas. 39 095007

 

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1. Introduction

Pain has always been a most unpleasant condition and, being a sensation within the central nervous system, may affect on a person’s physiology. Intraoperative pain is associated with the effectiveness of postoperative recovery, and pain associated with dental treatment might prevent some persons from having regular check-ups (Milgrom et al 1992, Townend et al 2000). This has motivated research on the measurement and prevention of pain. To provide a measure of pain and to also provide this during situations in which pain feedback is not routinely available (e.g. during dental treatment or general anesthesia), the sensation of pain requires objectification and innovative means of pain detection.

Pain measurement (also referred to as algesimetry) can be divided into self-reported and quantitative meas-urements (Holdcroft and Jaggar 2011). The source of self-reported algesimetry are statements made by the patient, usually on a standard scale such as a numerical rating (Melzack and Torgerson 1971, Melzack 1975). These reported measurements are subjective because the sensation of pain is influenced by various psychologi-cal factors. To better quantify pain, quantitative testing methods were developed, which furthermore provide more objective measures. The most direct method of quantitative algesimetry is the recording of neural activity directly from the peripheral nerves (called microneurography) (Fors et al 1984, 1986). Applying signal process-ing methods to electroencephalographic signals is a noninvasive method of quantitative algesimetry (Bromm and Scharein 1982, Chapman et al 1985, Nir et al 2010, Shao et al 2012). Brain imaging techniques also allow one to study pain-related activity in the brain, as reviewed in Casey and Bushnell (2000). However, all these methods of quantitative algesimetry require sophisticated equipment that is generally not available during routine dental or surgical procedures.

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Detection of acute periodontal pain from physiological signals

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Physiol. Meas.

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10.1088/1361-6579/aadf0c

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Physiological Measurement

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Detection of acute periodontal pain from physiological signals

Daniel Teichmann1,2 , Jan Klopp3, Alexander Hallmann1, Katharina Schuett4, Stefan Wolfart5 and Maren Teichmann5

1 Philips Chair for Medical Information Technology, RWTH Aachen University, Aachen, Germany2 Institute for Medical Engineering & Science, Massachusetts Institute of Technology, Cambridge, MA, United States of America3 Graduate Institute of Electrical Engineering, National Taiwan University, Taipei, Taiwan4 Department of Internal Medicine I, University Hospital RWTH Aachen, Aachen, Germany5 Department of Prosthodontics and Biomaterials—Center of Implantology, University Hospital RWTH Aachen, Aachen, Germany

E-mail: teichmann@hia.rwth-aachen.de

Keywords: algesimetry, pain, vital sign monitoring, signal fusion, dental treatment

AbstractObjective: To investigate the feasibility of the detection of brief orofacial pain sensations from easily recordable physiological signals by means of machine learning techniques. Approach: A total of 47 subjects underwent periodontal probing and indicated each instance of pain perception by means of a push button. Simultaneously, physiological signals were recorded and, subsequently, autonomic indices were computed. By using the autonomic indices as input features of a classifier, a pain indicator based on fusion of the various autonomic mechanisms was achieved. Seven patients were randomly chosen for the test set. The rest of the data were utilized for the validation of several classifiers and feature combinations by applying leave-one-out-cross-validation. Main results: During the validation process the random forest classifier, using frequency spectral bins of the ECG, wavelet level energies of the ECG and PPG, PPG amplitude, and SPI as features, turned out to be the best pain detection algorithm. The final test of this algorithm on the independent test dataset yielded a sensitivity and specificity of 71% and 70%, respectively. Significance: Based on these results, fusion of autonomic indices by applying machine learning techniques is a promising option for the detection of very brief instances of pain perception, that are not covered by the established indicators.

PAPER2018

RECEIVED 6 April 2018

REVISED

22 August 2018

ACCEPTED FOR PUBLICATION

5 September 2018

PUBLISHED 27 September 2018

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During the last 20 years, a new generation of pain indicators has emerged that allows the monitoring moni-tor of pain during general anesthesia based on easily obtainable physiological signals. They take electrocardio-graphic (ECG) or photoplethysmographic (PPG) waveforms and map them to a single quantity that indicates the current pain perception of a patient. The most frequently used indicators include: the analgesia nociception index (ANI) (Logier et al 2010, 2006), the surgical pleth index (SPI) (Huiku et al 2007, Bonhomme et al 2010), the autonomic nervous system state index (ANSSI) (Paloheimo et al 2010), the noxious stimulation response index (NSRI) (Bouillon et al 2004), and the number of fluctuations of skin conductance (NFSC) (Storm et al 2002). These indicators were developed with the goal of maintaining the depth of general anesthesia during surgical interventions (e.g. percutaneous sperm aspiration, tooth extraction, breast surgery, gynecological inter-ventions, tonsillectomy, direct laryngoscopy, laparoscopic cholecystectomy) that can have longer-lasting pain sensations. Therefore, they were designed to be applied for a longer time interval (typically more than 60 s) and are probably unsuitable for application during dental treatments which generally have brief (but strong) intervals of pain sensation. Moreover, they are based (or at least tested) on physiological mechanisms that are influenced by anesthetics (e.g. propofol, remifentanil, fentanyl, sevoflurane) and other pharmaceutical agents (e.g. diazepam, rocuronium, cisatracurium) that are generally not used for ambulant dentistry.

In contrast, this study aims to investigate the feasibility of the detection of brief orofacial pain events (as often occur during dental treatment) from easily recordable physiological signals, and to develop a signal fusion algorithm combining the information in the signals in an optimal way.

In a clinical study (section 2.1), patients underwent periodontal probing and reported their pain events by pressing a push button while physiological signals were simultaneously recorded. Potential pain-indicating quantities (autonomic indices) were calculated from the physiological data (section 2.4) and (together with the pain event annotations) were used for supervised learning of different classifiers known from machine learning theory. In section 3 the performance of the machine learning algorithms is compared, and the best set of physi-ological features for accurate pain detection is determined.

2. Materials and methods

2.1. Study design2.1.1. ProtocolAll evaluations in this work are based on data acquired from a monocentric clinical trial (ethical approval no. EK128/13) conducted at the Department of Prosthodontics and Biomaterials—Center of Implantology, Medical Faculty, RWTH Aachen University (Aachen, Germany). Patients aged ⩾40 years who had at least one own tooth and were compos mentis were included in the study as long as none of the following exclusion criteria applied:

(i) neurologic disease, heart disease, uncontrolled hypertension (ii) professional tooth cleaning during the 3 months prior to the study (iii) body mass index ⩽30 (iv) current or past regular abuse of alcohol or drugs (v) current use of hypnotic, sedative, antidepressive, neuroleptic, or anti-allergic drugs as well as of

tranquilizers, analgesics, stimulants, or any drugs affecting heart rate.

After excluding three drop-out patients owing to corrupted data recording, 47 individuals (34 males and 13 females) aged 44–69 years (mean: 55.9 years; standard deviation (sd): ±6.8 years) and weighing 63.3–121.3 kg (mean: 85.8 kg; sd: ±13.9 kg) participated in the study. They were matched into pairs of same gender, differing in age by 5 years at maximum, where one subject responded positively to the prospective cardiovascular Münster (PROCAM) or PROCAM stroke quick check while the other responded negatively to both. Both of these quick checks are based on the study presented in Assmann and Schulte (1988) and provide the risk (depending on sev-eral variables) that a person will suffer from heart attack or stroke in the near future, respectively. The pairing (as well as the application of exclusion criteria 2) was performed in view of additional analyses of the present data (Teichmann et al 2015) and is not strictly necessary for the investigations performed in the present study. Here, participants are not separated by gender, weight, or height.

For all participants the same study protocol was applied (figure 1). After a baseline phase of 10 min, periodontal probing was performed, i.e. inserting a probe (here, a pressure-calibrated probe: Kerr-Hawe, Rast-statt, Germany) into the gap formed by a tooth and its surrounding tissue (hereafter called periodontal pocket). This is a common diagnostic method in periodontology, because high depth of the periodontal pocket indicates periodontal disease. This probing was conducted at six positions around each tooth. During the treatment each participant held a push button and was asked to press the button when and for as long as (s)he felt pain. The signal of the push button was recorded simultaneously with the patient’s physiological signals.

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2.1.2. Collected data

Pain assessmentPain feedback was given by means of a custom-made push button device held in the right hand. The value was stored with a sampling rate of fs = 125 Hz. In total 315 pain events were indicated by the patients. The mean duration of a pain event was 818 ms with a standard deviation of 685 ms.

Physiological signals and their pre-processingPhysiological signals of the patient were recorded by means of a patient monitor (IntelliVue MP70, Philips, the Netherlands) and custom-made software. These signals were ECG leads according to Einthoven and a photoplethysmographic (PPG) pulse signal. The respiratory signal which was also recorded by the device via bioimpedance measurement using the ECG electrodes turned out to provide very low signal quality with an immense amount of signal loss and was excluded from further analysis. In total 993 min of data have been recorded during periodontal probing.

Although a three-lead ECG was employed for the measurements, for each subject’s data-set only one lead was available, due to problems with the recording software; moreover, the type of lead available varies between our subjects. Trend removal by polynomial fitting was applied to the ECG data to reduce motion artifacts. The sampling rate of the ECG signals was fs = 250 Hz.

The PPG waveforms were recorded with fs = 125 Hz using a clip on the forefinger of the left hand. A sim-ple detrending by subtracting a Savitsky–Golay smoothed version was applied (Savitzky and Golay 1964); this results in a signal with fewer distortions and a zero mean.

2.2. Windowing of the time series data into sample intervalsThe continuous time series of physiological signals are segmented into smaller time intervals using a sliding window. Each time interval is categorized as a ‘pain’ or ‘no pain’ sample depending on whether or not a pain event took place within the period of the time excerpt. Based on the signal of each time interval features are calculated forming the classifier’s input feature vector, on which the decision will be based.

Time excerpts with ‘no pain’ were segmented by a common sliding window with fixed window length T = 5 s and shift l = 4.33 s.

Since the duration of pain events evoked by periodontal probing is only a few seconds, segmentation of the data into short time intervals yields a considerably smaller number of pain intervals compared with those of ‘no pain’ intervals. Furthermore, the modulation of physiological signals by pain will take place after the pain sen-sation, therefore, ‘pain’ intervals should cover this phase. On the other hand there likely will be a certain delay between the beginning of the actual pain sensation and the activation of the push button due to unknown reac-tion times of individual subjects. To take this into account, the following windowing method was applied for the generation of ‘pain’ samples: for each pain event five pain intervals are generated by choosing 5 s windows with an onset of 2.25, 1.625, 1, and 0.375 s before and 0.25 s after the actual pain annotation (figure 2). This way, very fast as well as very slow reaction times are considered and the unequal proportion between ‘pain’ and ‘no pain’ samples is compensated to some extent. In ten cases two windows belonging to different pain events overlapped. In these cases one out of the two overlapping windows was randomly chosen and discarded.

2.3. Pain detection algorithmIn this study, pain detection is achieved using binary classifiers which assign the periods of the recorded physiological signals to one of the two classes ‘pain’ and ‘no pain’. For this purpose, features are extracted from the physiological signals which enable the discrimination between ‘pain’ and ‘no pain’. These autonomic features are time-discrete quantities describing the underlying mechanisms within the continuous physiological signals. Section 2.4 provides information on the utilized features.

2.3.1. Applied learning procedure for classifier generationFor each time interval a feature vector is calculated and fed into the classifier which maps it to the ‘pain’ or ‘no pain’ class. Here, the mapping is inferred from data whose class labels are given (‘supervised learning’).

Figure 1. Protocol of the study.

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At the very beginning of this work a test data set was separated by randomly choosing seven patients. The rest of the patient data was used as the training and validation data set. Based on the windowing methodology described in section 2.2, this resulted in 1370 ‘pain’ and 10 503 ‘no pain’ samples for training and validation and 195 ‘pain’ and 2333 ‘no pain’ samples for the final test. The distribution of ‘pain’ and ‘no pain’ samples among the patients can be found in figure 3.

In a validation phase the best feature combination for each classifier was determined by using leave-one-(patient)-out cross validation (LOOCV) and Feature forward selection (FFS). To account for the unbalanced data set, 5-fold boot strapping was applied to the ‘no pain’ samples during LOOCV. (This means that each LOOCV-step was repeated five times, each time with a number of randomly chosen ‘no pain’ samples equaling the number of ‘pain’ samples.)

In a following test phase the one classifier and feature set combination that performed best in the validation phase was trained on the entire training and validation data and afterwards tested on the independent test set.

For this final test a threshold above which the classifier assumes input samples as ‘no pain’ intervals has to be defined. In order to conduct unbiased detection without a priori usage of the test data, the optimal operating point for the ROC of the entire validation data set was determined beforehand and then applied to the ROC of the actual test data. Here the point on the ROC curve closest to the point (0,1) was considered as optimal.

2.3.2. ClassifiersDifferent classifiers were examined for their performance as a pain indicator. These were the following:

• Support vector machine (SVM); • Artificial neural network (ANN); • Decision tree (in this study, an ensemble method was used: random forest (Breiman 2001)); • K-nearest neighbors (KNN).

For all classifiers the implementation provided by Matlab (Matlab 2014a, Mathworks, USA) was used.

2.4. Potential pain indicating features2.4.1. Heart beat intervals (HBI)HBI was extracted from the PPG. After the peaks in the PPG signal were identified by the ADAPIT algorithm as described in Yu et al (2006), the time between two heart beats was computed. Then, the HBI were filtered by the algorithm described in Logier et al (2004). The HBI was computed every 0.5 s.

2.4.2. PPG amplitude (PPGA)The amplitude of the PPG signal was computed by searching the local minima and maxima of the detrended PPG signal. Forming two waveforms (one of the maxima and one of the minima) an estimate of the amplitude was determined by interpolating each of the two waveforms and taking the difference. The PPGA was computed every 0.5 s.

Figure 2. The windowing process around a single pain event. The feature instances (value of a feature calculated at a specific point in time) numbered 1–4 are contained within the window which has an onset of 1 s before the pain peak (black solid line) and are, therefore, part of the feature vector formed to represent this ‘pain’ sample. Those feature instances indicated in gray dashed lines do not contribute to this sample as they lie outside the corresponding window. However, they are included in some of the other windows and, therefore, are used in the feature vectors representing those ‘pain’ intervals.

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2.4.3. Surgical pleth index (SPI)The SPI was formerly known as the surgical stress index (SSI) (Bonhomme et al 2010) and initially proposed under that name by Huiku et al (2007). It is based on normalized versions of PPGA and HBI and projects these values uniformly distributed into the interval [0, 100]. The SPI value was computed every 1 s.

2.4.4. Autonomic nervous system state index (ANSSi)The ANSS, and the corresponding index ANSSi, were proposed by Paloheimo et al to display activation of the autonomous nervous system, where a large value corresponds to more activity (Paloheimo et al 2010). It is based on HBI and a normalized version of PPGA. The ANSSi was computed every 1 s.

2.4.5. Frequency spectral bins (FSB)The frequency spectrum of the PPG and ECG signal was computed once for each 5 s input sample by fast Fourier transform. This yielded 313 frequency bins with a maximum frequency of 62.5 Hz for the PPG signal (FSBPPG) and 625 frequency bins with a maximum frequency of 125 Hz for the ECG signal (FSBECG). From both spectra the first 21 bins were used as features.

2.4.6. Levels of discrete wavelet transform (LDWT)The absolute wavelet energies in eight different levels, decomposed by the DWT (Rioul and Vetterli 1991) for each 5 s input sample, were calculated using the 13th Daubechies wavelet (db13) for the PPG (LDWTPPG) and ECG (LDWTECG).

2.5. Feature normalizationFeatures were normalized to (i) decrease the probability of providing identification of single patients, and to (ii) address different signal amplitudes among the patients due to different sensor positioning. For this purpose, z-transformation was applied to all features. Since the amount of pain sensations varied among the patients and

Figure 3. The distribution of ‘pain’ and ‘no pain’ samples among the patients.

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it was assumed that signal amplitudes are affected by pain sensation, only the standard deviation of the baseline phase was used for the z-transformation.

3. Results

3.1. Validation phase: selection of features and classifierTable 1 shows the results of the different steps of the feature forward selection for each classifier. The feature sets are enumerated as given in the table’s caption. While the KNN performed worst, it needed only one feature set (HBI) for its best result. Other classifiers performed best with a combination of 4–5 features with none of them using the ANSSi feature. The best result (AUC of 0.685) provided the Random forest classifier using LDWTECG,

FSBECG, PPGA, LDWTPPG, and SPI.

3.2. Test phase: performance of validated algorithm applied to an independent test setAs described in section 2.3.1, the data of seven patients were excluded from the validation process in order to use it as a data set for a final testing.

For this purpose, the best classifier (random forest with the feature combination of step 5 of the feature selec-tion process (see table 1) was trained using the entire validation set.

Applying this classifier to the entire test data set yields the Receiver operating characteristic (ROC) curve pre-sented in figure 4 with an AUC of 0.811.

As described in section 2.3.1, the operating point along this curve was chosen based on the ROC of the valida-tion data set (optROCval, indicated in figure 4 by a red circle). This way, the pain detection algorithm achieved a sensitivity of 71%, a specificity of 70%, and an accuracy of 70%. When the operating point was chosen in a way to maximize the harmonic mean between precision and sensitivity, the F1 score was 0.403. Table 2 presents the

performance measures for each of the test patients separately.There were three patients for which the pain detection worked worse than for the others. Those patients

(28, 37, 44) had provided only one or two pain sensations and, therefore, only a few pain samples (see figure 3). Especially, for patient 37 the pain detection performed worse than chance (AUC of 0.489). Patient 18 also only provided a few pain samples, but these were better covered by the algorithm.

As described in section 2.2, the pain samples were shifted with different onsets around the time point at which the pain sensation was indicated by the patient. This means there are five pain samples per pain event. To deter-mine if this time-shifting procedure was necessary and also if there is one onset that yields better classification than the other onsets, the results that are achieved when only one time interval per pain event is labeled as pain sample (either only for the test data or for the test and training data) were computed (see table B1 in appendix B). The results of this analysis are worse than the preceding ones with multiple pain samples per pain event. This deterioration is most likely due to further reducing the already limited data set (number of pain samples is reduced to one fifth).

Table 1. AUC values of each classifier for the different steps of the feature forward selection process (average AUC values of LOOCV with 5-fold boot strapping). Enumeration of feature sets as follows: HBI(1), PPGA(2), SPI(3), ANSSi(4), FSBPPG(5), FSBECG(6), LDWTPPG(7), LDWTECG(8).

Classifier

StepBest

resultI II III IV V VI VII VIII

ANN AUC 0.591 0.596 0.621 0.592 0.611 0.596 0.601 0.563 0.621

feature

sets

6 6 , 8 6 , 8 , 2 6 , 8 , 2 , 4 6 , 8 , 2 , 4 , 7 6 , 8 , 2 , 4 , 7 , 1 6 , 8 , 2 , 4 , 7 , 1 , 3 6 , 8 , 2 , 4 , 7 , 1 , 3 , 5 6 , 8 , 2

Random

forest

AUC 0.630 0.675 0.683 0.679 0.685 0.675 0.670 0.659 0.685

feature

sets

8 8 , 6 8 , 6 , 2 8 , 6 , 2 , 7 8 , 6 , 2 , 7 , 3 8 , 6 , 2 , 7 , 3 , 5 8 , 6 , 2 , 7 , 3 , 5 , 4 8 , 6 , 2 , 7 , 3 , 5 , 4 , 1 8 , 6 , 2 , 7 , 3

SVM AUC 0.596 0.612 0.625 0.633 0.634 0.632 0.634 0.633 0.634

feature

sets

5 5 , 6 5 , 6 , 2 5 , 6 , 2 , 8 5 , 6 , 2 , 8 , 1 5 , 6 , 2 , 8 , 1 , 3 5 , 6 , 2 , 8 , 1 , 3 , 7 5 , 6 , 2 , 8 , 1 , 3 , 7 , 4 5 , 6 , 2 , 8 , 1

KNN AUC 0.593 0.576 0.576 0.574 0.569 0.575 0.579 0.577 0.593

feature

sets

1 1 , 8 1 , 8 , 2 1 , 8 , 2 , 6 1 , 8 , 2 , 6 , 3 1 , 8 , 2 , 6 , 3 , 7 1 , 8 , 2 , 6 , 3 , 7 , 5 1 , 8 , 2 , 6 , 3 , 7 , 5 , 4 1

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4. Discussion

In this study, the annotation of pain events is based on self-reported methods of algesimetry. The perception of pain and the related self-reported measurements are subjective and are influenced by psychological factors, e.g. attention and fear (Lepanto et al 1965, Dougher et al 1987, Arntz et al 1991, Arntz and Jong 1993). Applying the quantitative method of microneurography would have been the most direct and unbiased way of obtaining a pain-related nociceptive signal. Fors et al applied a mathematical model to relate nerve activity to the tooth pain intensity indicated by a subject (Fors et al 1984, 1986). Britton and Skevington emphasize the importance of such techniques in their review article (Britton and Skevington 1996). Serra et al report that this approach can even be used to monitor spontaneously occurring pain by tracking the activity of certain C-nociceptors (Serra et al 2012). In the present study, one rationale for the utilization of self-reported algesimetry as reference measure is the fact that, while microneurography offers some insight into the physiological source of pain, it does not elucidate what is added to the noxious signal to yield the psychological sensation of pain. Nevertheless, with reference to Chapman et al (1985), it would be interesting in the future to examine differences between the performance of the autonomic pain indicator proposed here when it is trained on data annotated either by pain reports or by nociceptive excitation.

Although Chapman mentions several disadvantages of pain measurement based on autonomic indi-ces (Chapman et al 1985), the stress response to pain is governed by various changes in metabolism, hormone balance (Desborough 2000), and activation shifts within the autonomous nervous system (Neukirchen and Kienbaum 2008), which may be mapped to pain indicators. Furthermore, the use of autonomic pain indica-tors yields economic advantages as they are relatively easy to measure, especially compared with techniques that extract signals from the brain; they do not require a conscious answer they are less easily manipulated by biases and can be used with anesthetized patients. In future, it might be possible to record such autonomic indices by

Figure 4. ROC of the final algorithm applied to the separate test data set. To avoid biasing, the operating point (OptROCval) was determined beforehand based on the validation data set.

Table 2. Performance of the final pain detection algorithm applied to each test patient separately and all test patients’ data as a whole (overall).

Patient

5 12 18 28 32 37 44 Overall

AUC 0.842 0.783 0.843 0.560 0.869 0.489 0.638 0.811

Sensitivity 69% 69% 100% 20% 100% 0% 0% 71%

Specificity 76% 67% 59% 69% 48% 77% 89% 70%

Accuracy 76% 68% 60% 68% 69% 76% 86% 70%

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sensor systems which work in an unobtrusive and noncontact way and which are seamlessly integrated into the dental treatment unit (Teichmann et al 2017).

Additionally, there exists a variety of other additional biomedical signals which are easy to record and have been demonstrated to be linked to pain sensation, e.g. electrodermal activity, skin temperature, and electromyo-graphy of the face or the trapezius muscle (Gruss et al 2015). Although such measures are relatively easy to meas-ure, they were considered less common and the instrumentation necessary for their recording is not as widely available. Furthermore, with the exception of temperature, it seems difficult to measure them in a noncontact way, which is a long term goal of the authors’ research group. Nevertheless, based on the results of the present work detection of acute periodontal pain based on ECG and PPG seems feasible but also challenging and it would be interesting to investigate the benefit of the addition of further physiological signals in future studies.

With the maximal harmonic mean between sensitivity and specificity chosen as the operation point, the final algorithm yielded a sensitivity and specificity of approximately 70%. Although this result demonstrates the fea-sibility of the detection of acute, very short pain sensation solely based on PPG and ECG, the performance might be too low for clinical use. From a clinical point of view, depending on the patient’s status and the course and/or the type of treatment, either a high sensitivity or a high specificity might be preferable. In such a case, the work-ing point of the algorithm could be adjusted accordingly, yielding higher values for one measure by lowering another measure. In general, a higher accuracy would be ideal as this would allow high sensitivity and specificity at the same time. Depending on the treatment needs, a more general information about the patient’s level of pain sensation along the treatment procedure and during the last couple of minutes might be of more interest than the information about a short single pain sensation at a specific point in time. Such a time-averaged pain intensity level could be calculated by accumulation of the detected short pain sensations and would possibly be less sensi-tive to false negatives.

When used in an isolated way in step 1 of the feature forward selection procedure, both established pain indices (SPI and ANSSi) were outperformed by other features with all classifiers (see table 1). The ANSSi was not part of any of the selected feature combinations. The SPI was only part of the selected feature combination of the random forest classifier.

There are two recent publications that describe studies which are comparable to the present study, as they are also applying machine learning techniques to physiological signals with the aim to detect short pain sensations of non-anaesthetized subjects. But for comparison it should be noted that in these studies more physiological signals were utilized and longer lasting pain sensations were induced. Jiang et al trained an ANN classifier for pain detection based on heart rate, breathing rate, galvanic skin response (GSR) and facial electromyography features (Jiang et al 2018). Their best result for detection of moderate/severe pain was an average accuracy of 76.3 Hz, which lies in a similar range as the results achieved in the present study. Chu et al were able to demon-strate an average accuracy of inter-person pain intensity classification of 91.18% (Chu et al 2017). They used SVM classifier and PPG, ECG, and GSR as physiological signals. However, this relatively good result has been achieved with only six subjects and for 30 s pain intervals during which pain was constantly induced.

Directly after pain sensation, as part of the first stage of the general adaption syndrome, the sympathetic nervous system will trigger catecholamine production (acute stress response) resulting among others in increased blood pressure and tachycardia (Selye 1950). As demonstrated by Huiku et al (2007) and Paloheimo et al (2010), this stress response to pain via the autonomous nervous system affects the frequency as well as the strength of the heart beat and can be utilized for pain assessment. The indices in those studies (SPI and ANSSi) implement a quantification of this effect by using PPGA and HBI. Therefore, we decided to use SPI and ANSSi as well as the two underlying measures PPGA and HBI itself as features. Since the acute stress response at least also affects the muscle tonus, blood glucose level, and hypothalamic-pituitary-adrenal axis, there might exist further pain-related effects onto the cardiorespiratory activity contained in the recorded physiological signals. The fre-quency and wavelet components of the PPG and ECG have been chosen as features with the idea in mind that those measures might enable the classification algorithm to discover such underlying effects. There are also pain indicators known from the literature which are based on the fact that the modulation of the heart rate variability by the vagus nerve gets disturbed by induced pain (Logier et al 2004). But this effect was considered to be too slow to take place within the 5 s interval aimed at in this study. In the context of pain, it is usually observed within a time window of 64 s and analyzed in a frequency range of 0.15 Hz–0.5 Hz.

In this study, the length of the input samples was chosen to be 5 s. On the one hand the indication of a pain sensation should be provided as soon as possible, ideally immediately. On the other hand, for a precise pain indi-cation a sufficient amount of information is necessary. After discussion with clinicians and dentists, a 5 s interval seemed to be in a range of acceptable deviation for pain related dentist-patient interaction. However, the perfor-mance of the algorithm in dependence on the window length has not been investigated and should be considered in future works.

The type of available ECG leads and, therewith, the signal form of the typical ECG waves differed among the patients in this study. Although this means a limitation of the benefit of some ECG-based features, the perfor-

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mance of the classification process was still good. In future studies, the consistency of ECG lead types has to be ensured.

In the comparison of all applied classifiers for pain detection based on autonomic feature fusion, the random forest algorithm performed the best. The ANN had worse results than Random forest and SVM; however, this classifier was based on a network with two hidden layers and it must be noted that other architectures may per-form differently. In general, the classifiers could have been more optimized in terms of their parameters.

In the present study, the occurrence of pain events is very unequally distributed between patients. The AUC achieved in the test phase was much better than the one achieved in the training phase. This might be a coinci-dence and due to the heterogenous and limited data set. The only (but unlikely) other explanation is that the data for training of the final algorithm was slightly bigger (one patient) as during the validation phase. A data pool with the same amount of pain events per subject that could be used for the training phase would possibly allow generation of better generalizing classifiers. Therefore, replacing periodontal probing by another dental treat-ment which ensures pain perception during each application would be helpful as this would yield an equally distributed pain sensation among all participants. For example, cold thermal testing of tooth pulp vitality might be suitable (Fors et al 1984, 1986), if applied to the same number of vital teeth per patient.

5. Conclusion

Utilizing autonomic features in an isolated way for the detection of pain events yields low performance. Here, FSBPPG and LDWTECG show the best results, while the established autonomic pain indicators (the SPI and ANSSi) provide lower performance level.

Fusion of the autonomic features by the application of machine learning techniques allows more accurate automatic detection of brief periodontal pain events. Based on our validation data, the random forest classifier using frequency spectral bins of the ECG, wavelet level energies of the ECG and PPG, PPGA, and SPI as features was the best pain detection algorithm.

The final test on an independent data set yielded sensitivity and specificity of 71% and 70%, respectively, and

proves the feasibility of detecting brief pain sensations by means of ECG and PPG signals.

Acknowledgment

The clinical study and data acquisition was funded by the German Federal Ministry of Economics and Technology (Central Innovation Program SME, ZIM). The dental treatment unit was provided by Ultradent Dental-Medizinische Geräte GmbH & CO. KG, Brunnthal, Germany. Data analysis was partially funded by the European Unions Horizon 2020 research and innovation programme (ADAS&ME, no. 688900) and the German Research Foundation (DFG, no. TE1174/2-1).

Appendix A. Parameter sets used in section 3.1

A.1. Random forestsNumber of trees NTree = 100.Minimum number of instances per leaf NLeaf,min = 2.Number of features to form the search space for a split decision NDecisionFeatures = 5.

A.2. KNNNumber of neighbours kTree = 100.

A.3. SVMPenalty on points that are on the wrong side of the decision boundary at training time CSlack = 1.Misclassification cost for class ‘no pain’ CMisClass,0 = 1.Misclassification cost for class ‘pain’ CMisClass,1 = 10.The kernel was the radial basis function as it performed better than linear and polynomial kernels in preliminary tests.

A.4. ANNNumber of elements in first hidden layer NHiddenLayer1 = 30.Number of elements in second hidden layer NHiddenLayer2 = 15.The error function to be optimized was mean squared error as it performed better than cross entropy in prelimi-nary tests.

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Appendix B. Result with only one pain sample per pain event

ORCID iDs

Daniel Teichmann https://orcid.org/0000-0003-3716-3201

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Table B1. Performance of the final pain detection when only one pain sample is generated per pain event. Columns give the results achieved with different onsets (see section 2.2 and figure 2). (a) Multiple pain samples per pain event in the training data and one pain sample per pain event in the test data. (b) One pain sample per pain event in the training and test data.

Onset

−2.25 s −1.625 s −1 s −0.375 s 0.25 s

(a) (b) (a) (b) (a) (b) (a) (b) (a) (b)

AUC 0.803 0.717 0.811 0.739 0.804 0.772 0.801 0.822 0.826 0.784

Sensitivity 77% 69% 74% 69% 74% 77% 80% 80% 80% 82%

Specificity 70% 56% 73% 62% 69% 61% 71% 66% 73% 58%

Accuracy 70% 56% 73% 62% 69% 61% 71% 66% 73% 59%

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