An efficient algorithm for R-R intervals series filtering

Heart rate variability analysis for the prediction of pre-arousal during propofol-remifentanil general anaesthesia: A feasibility study

Accidental awareness during general anaesthesia is a major complication. Despite the routine use of continuous electroencephalographic monitoring, accidental awareness during general anaesthesia remains relatively frequent and constitutes a significant additional cost. The prediction of patients' arousal during general anaesthesia could help preventing accidental awareness and some researchers have suggested that heart rate variability (HRV) analysis contains valuable information about the patient arousal during general anaesthesia. We conducted pilot study to investigate HRV ability to detect patient arousal. RR series and the Bispectral IndexTM (BISTM) were recorded during general anaesthesia. The pre-arousal period T0 was defined as the time at which the BISTM exceeded 60 at the end of surgery. HRV parameters were computed over several time periods before and after T0 and classified as "BISTM<60" or "BISTM≥60". A multivariate logistic regression model and a classification and regression tree algorithm were used to evaluate the HRV variables' ability to detect "BISTM≥60". All the models gave high specificity but poor sensitivity. Excluding T0 from the classification increased the sensitivity for all the models and gave AUCROC>0.7. In conclusion, we found that HRV analysis provided encouraging results to predict arousal at the end of general anaesthesia.

End-of-life Comfort Evaluation, is Clinic Enough? A Retrospective Cohort Study of Combined Comfort Evaluation with Analgesia/Nociception Index and Clinic in non-Communicative Patients

Background: Comfort evaluation is one of the major challenges in the palliative care setting, particularly when it comes to non-communicative patients. For this specific population, validated tools for comfort evaluation are scarce and healthcare professionals have to rely on their clinical sense and experience. Objectives: To provide arguments for the use of Analgesia/Nociception Index (ANI) monitoring in order to improve clinical comfort evaluation. Methods: We conducted a retrospective cohort study of non-communicative patients at the end of their lives whose comfort was evaluated clinically and with ANI. We focused on the coherence or discordance of clinical and ANI evaluations and on pharmacological interventions driven by them. Results: 58 evaluations from 33 patients were analyzed. Clinical and demographic characteristics were highly variable. Simultaneous clinical and ANI evaluations were concordant in 45 measurements (77.58%), leading mostly to no treatment modification when indicating comfort and to increasing anxiolytic or pain-relief treatments when indicating discomfort. Thirteen (22.41%) evaluations were discordant, leading mostly to treatment incrementation. Conclusion: We suggest that the ANI monitor is a reliable tool in the palliative setting and may help provide patients with the best symptom relief and the most appropriate therapeutics.

Influence of averaged fetal heart rate in heart rate variability analysis

In high-income countries, fetal hypoxia affects 3 to 8 newborns per 1000 live births with subsequent moderate or severe Hypoxic-Ischemic Encephalopathy (HIE) in 0.5 to 1 per 1000 live births. Visual interpretation of FHR signal issued from a Doppler ultrasound cardiotocography is the gold standard to monitor fetal condition. Unfortunately, its analysis presents a high rate of inter-observer variability and a low specificity to predict poor neonatal outcomes. Under hypoxia, the fetus develops several adaptive mechanisms regulated by the autonomic nervous system inducing changes in the fetal heart rate variability. Though fetal heart rate variability methods demonstrated abilities to predict perinatal asphyxia, most of the Doppler ultrasound technologies used in clinical practice do not provide sufficiently accurate fetal heart rate signals for heart rate variability analysis. Indeed, Doppler ultrasound cardiotocography usually provides fetal heart rate values averaged over 2 or 3 beats which can constitute a limitation for spectral analysis. We developed a fetal heart rate variability analysis method: the Fetal Stress Index (FSI). The objective of this study was to investigate the influence of averaged fetal heart rate on this new index in order to check the feasibility of computing the FSI from the signal issued from Doppler ultrasound cardiotocography.

Comparison of multiple cardiac signal acquisition technologies for heart rate variability analysis

Heart rate variability analysis is a recognized non-invasive tool that is used to assess autonomic nervous system regulation in various clinical settings and medical conditions. A wide variety of HRV analysis methods have been proposed, but they all require a certain number of cardiac beats intervals. There are many ways to record cardiac activity: electrocardiography, phonocardiography, plethysmocardiography, seismocardiography. However, the feasibility of performing HRV analysis with these technologies and particularly their ability to detect autonomic nervous system changes still has to be studied. In this study, we developed a technology allowing the simultaneous monitoring of electrocardiography, phonocardiography, seismocardiography, photoplethysmocardiography and piezoplethysmocardiography and investigated whether these sensors could be used for HRV analysis. We therefore tested the evolution of several HRV parameters computed from several sensors before, during and after a postural change. The main findings of our study is that even if most sensors were suitable for mean HR computation, some of them demonstrated limited agreement for several HRV analyses methods. We also demonstrated that piezoplethysmocardiography showed better agreement with ECG than other sensors for most HRV indexes.

Analgesia Nociception Index: Heart Rate Variability Analysis of Emotional Status

Background

Analgesia nociception index (ANI) has been developed for real-time pain measurement during a surgical procedure under general anesthesia. The index is based on heart rate variability and constitutes a measure of parasympathetic tone. In this paper, we hypothesized that this index could be used as a tool to investigate the process of emotional regulation of a human subject.

Materials and methods

Twenty adult volunteers were recruited for the study, wherein ANI response to the emotional stimulus was evaluated. An emotional stimulus was obtained through a 60-second music sound record from the song “Ala Gözlerini Sevdiğim Dilber,” performed by the Turkish rock band Badem. ANI measurements were obtained before the song presentation (T_pre), at the end of the record presentation (T0), and each minute thereafter until the end of the five-minute observation (T1-T5).

Results

Twenty participants were investigated; 10 males and 10 females. The mean age of the participants was 17.0 ± 0.9 (min: 16, max: 20). ANI measurements were significantly lower in T0 and T3 compared with T_pre (P = 0.009). The differences between other values were not statistically significant.

Conclusion

ANI can be used for assessment of parasympathetic changes related to the emotional state of conscious patients.

A Deep Neural Network-Based Pain Classifier Using a Photoplethysmography Signal

Side effects occur when excessive or low doses of analgesics are administered compared to the required amount to mediate the pain induced during surgery. It is important to accurately assess the pain level of the patient during surgery. We proposed a pain classifier based on a deep belief network (DBN) using photoplethysmography (PPG). Our DBN learned about a complex nonlinear relationship between extracted PPG features and pain status based on the numeric rating scale (NRS). A bagging ensemble model was used to improve classification performance. The DBN classifier showed better classification results than multilayer perceptron neural network (MLPNN) and support vector machine (SVM) models. In addition, the classification performance was improved when the selective bagging model was applied compared with the use of each single model classifier. The pain classifier based on DBN using a selective bagging model can be helpful in developing a pain classification system.

Detection of acute periodontal pain from physiological signals

OBJECTIVE

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.

A new analysis of heart rate variability in the assessment of fetal parasympathetic activity: An experimental study in a fetal sheep model

Analysis of heart rate variability (HRV) is a recognized tool in the assessment of autonomic nervous system (ANS) activity. Indeed, both time and spectral analysis techniques enable us to obtain indexes that are related to the way the ANS regulates the heart rate. However, these techniques are limited in terms of the lack of thresholds of the numerical indexes, which is primarily due to high inter-subject variability. We proposed a new fetal HRV analysis method related to the parasympathetic activity of the ANS. The aim of this study was to evaluate the performance of our method compared to commonly used HRV analysis, with regard to i) the ability to detect changes in ANS activity and ii) inter-subject variability. This study was performed in seven sheep fetuses. In order to evaluate the sensitivity and specificity of our index in evaluating parasympathetic activity, we directly administered 2.5 mg intravenous atropine, to inhibit parasympathetic tone, and 5 mg propranolol to block sympathetic activity. Our index, as well as time analysis (root mean square of the successive differences; RMSSD) and spectral analysis (high frequency (HF) and low frequency (LF) spectral components obtained via fast Fourier transform), were measured before and after injection. Inter-subject variability was estimated by the coefficient of variance (%CV). In order to evaluate the ability of HRV parameters to detect fetal parasympathetic decrease, we also estimated the effect size for each HRV parameter before and after injections. As expected, our index, the HF spectral component, and the RMSSD were reduced after the atropine injection. Moreover, our index presented a higher effect size. The %CV was far lower for our index than for RMSSD, HF, and LF. Although LF decreased after propranolol administration, fetal stress index, RMSSD, and HF were not significantly different, confirming the fact that those indexes are specific to the parasympathetic nervous system. In conclusion, our method appeared to be effective in detecting parasympathetic inhibition. Moreover, inter-subject variability was much lower, and effect size higher, with our method compared to other HRV analysis methods.

Comparison of pulse rate variability and heart rate variability for high frequency content estimation

Heart Rate Variability (HRV) analysis can be of precious help in most of clinical situations because it is able to quantify the Autonomic Nervous System (ANS) activity. The HRV high frequency (HF) content, related to the parasympathetic tone, reflects the ANS response to an external stimulus responsible of pain, stress or various emotions. We have previously developed the Analgesia Nociception Index (ANI), based on HRV high frequency content estimation, which quantifies continuously the vagal tone in order to guide analgesic drug administration during general anesthesia. This technology has been largely validated during the peri-operative period. Currently, ANI is obtained from a specific algorithm analyzing a time series representing successive heart periods measured on the electrocardiographic (ECG) signal. In the perspective of widening the application fields of this technology, in particular for homecare monitoring, it has become necessary to simplify signal acquisition by using e.g. a pulse plethysmographic (PPG) sensor. Even if Pulse Rate Variability (PRV) analysis issued from PPG sensors has been shown to be unreliable and a bad predictor of HRV analysis results, we have compared PRV and HRV both estimated by ANI as well as HF and HF/(HF+LF) spectral analysis on both signals.

Measurement of Heart Rate Variability to Assess Pain in Sedated Critically Ill Patients: A Prospective Observational Study

Introduction

The analgesia nociception index (ANI) assesses the relative parasympathetic tone as a surrogate for antinociception/nociception balance in sedated patients. The aim of this study is to determine the effectiveness of ANI in detecting pain in deeply sedated critically ill patients.

Methods

This prospective observational study was performed in two medical ICUs. All patients receiving invasive mechanical ventilation and deep sedation were eligible. In all patients, heart rate and ANI were continuously recorded using the Physiodoloris^® device during 5 minutes at rest (T1), during a painful stimulus (T2), and during 5 minutes after the end of the painful stimulus (T3). The chosen painful stimulus was patient turning for washstand. Pain was evaluated at T2, using the behavioral pain scale (BPS). The primary objective was to determine the effectiveness of ANI in detecting pain. Secondary objectives included the impact of norepinephrine on the effectiveness of ANI in detecting pain, and the correlation between ANI and BPS.

Results

Forty-one patients were included. ANI was significantly lower at T2 (Med (IQR) 69(55–78)) compared with T1 (85(67–96), p<0.0001), or T3 (81(63–89), p<0.0001). Similar results were found in the subgroups of patients with (n = 21) or without (n = 20) norepinephrine. ANI values were significantly higher in patients with norepinephrine compared with those without norepinephrine at T1, and T2. No significant correlation was found between ANI and BPS at T2.

Conclusions

ANI is effective in detecting pain in deeply sedated critically ill patients, including those patients treated with norepinephrine. No significant correlation was found between ANI and BPS.

Ambulatory pain evaluation based on heart rate variability analysis: Application to physical therapy

Pain assessment is critical for efficient pain management. Clinicians usually use self-report or behavioral pain scales. In practice, the choice of the most adaptive scale depends on several parameters like the clinical context, the patient consciousness or its age, but all evaluation scales are known to be more or less subjective and to present high inter and intra individual variability. Recently, several innovative medical devices have been developed in order to provide to the clinicians a physiological measure of pain. These technologies are mainly used for the continuous monitoring of patients in intensive care or during surgery. As an example, we have developed a heart rate variability analysis based technology for analgesia/nociception monitoring in patients undergoing surgery under general anesthesia. Even if this technology is now used in other clinical settings, the resulting device presents some mobility constraints. In this paper, we describe the adaptation of this technology to the ambulatory pain evaluation and its clinical validation in the particular context of physical therapy. In the frame of this validation, we showed the device usability and efficiency for pain evaluation during physical therapy sessions.

Automated analgesic drugs delivery guided by vagal tone evaluation: interest of the Analgesia Nociception Index (ANI)

Analgesic drugs delivery optimization constitutes one of the main objectives of modern anesthesia. Indeed, their over or under determination constitutes a risk for anesthetized patient in terms of hemodynamic reactivity or post-operative hyperalgesia. Nowadays, new physiological indexes allow anesthesiologists to evaluate the balance between the analgesia level and the noxious stimulus importance. ANI is an index related to the autonomic nervous system activity based on heart rate variability analysis. Its ability for the analgesia / nociception balance evaluation has been established bringing evidences about its helpfulness for analgesic drug delivery. In this article, we describe a device for automatic analgesic drugs administration based on the ANI evolution during surgical procedures under general anesthesia. We hypothesized that such a device could improve the quality and safety of anesthesia by reducing adverse cardiovascular events and delivered analgesic drugs doses.

A multi sensing method for robust measurement of physiological parameters in wearable devices

The monitoring of physiological parameters such as heart rate, ventilatory rate, or oxygen saturation is a commonly used practice in the medical field. Many clinical solutions exist, based on the use of specific sensors, dedicated for bedside patient's vital functions monitoring at hospital. But the implementation of such sensors in ambulatory situations is rendered extremely difficult because of many artifacts induced by the movements of the subject that make the measures unusable. We have designed an original method for robust measurement of physiological parameters dedicated for wearable devices. The method is based on a multi sensing technique using, at least, two sensors of different nature or placed at different sites, for each parameter. In order to illustrate this method, we have developed a headset device including two heart rate (HR) sensors and two ventilatory rate (VR) sensors. This device has been evaluated on 6 healthy volunteers during exercises. This test showed the physiological values of HR and VR from the headset device stability and efficiency.

Heart rate variability analysis as an index of emotion regulation processes: interest of the Analgesia Nociception Index (ANI)

Autonomic Nervous System (ANS) variations are strongly influence by emotion regulation processes. Indeed, emotional stimuli are at the origin of an activation of the ANS and the way an individual pass from a state of alert in the case of emotional situation to a state of calm is closely coupled with the ANS flexibility. We have previously described and developed an Analgesia Nociception Index (ANI) for real time pain measurement during surgical procedure under general anesthesia. This index, based on heart rate variability analysis, constitutes a measure of parasympathetic tone and can be used in several other environments. In this paper, we hypothesized that such an index could be used as a tool to investigate the processes of emotional regulation of a human subject. To test this hypothesis, we analyzed ANI's response to a negative emotional stimulus. This analysis showed that the index decreases during the emotion induction phase and returns to its baseline after 2 minutes. This result confirms that ANI could be a good indicator of parasympathetic changes in emotional situation.

PhysioTrace: An efficient toolkit for biomedical signal processing

Healthcare monitoring applications requires the measurement and the analysis of multiple physiological data. In the field of biomedical research, these data are issued from different devices involving data centralization and synchronization difficulties. On the other hand, the analysis of the acquired data requires high level digital signal processing tools. In this paper we describe a real time toolkit for biomedical data acquisition, centralization, processing and visualization. This toolkit, composed of both hardware and software modules, allows users to model, test and perform all kind of digital signal processing algorithms for all kind of biomedical signals. These highly efficient hardware and software modules have been developed and tested especially for biomedical studies and used in a large number of clinical investigations. So, for developers, using such a toolkit will reduce the development time while increasing the application performances.

Heart rate variability analysis for arterial hypertension etiological diagnosis during surgical procedures under tourniquet

Pneumatic tourniquets are widely used to provide a bloodless operative field during upper or lower limb surgery. If tourniquet inflation during general anesthesia is initially a mild stimulus, a long duration of inflation can imply heart rate and blood pressure increasing. However, heart rate or blood pressure increasing can also be caused by other external stimuli. Indeed, in the case of an insufficient analgesia, painful surgical stimuli can also cause an increase in heart rate and blood pressure. Therefore, in the case of the use of a tourniquet during surgery, it's very difficult for the anesthesiologist to distinguish hypertension caused by pain from hypertension caused by tourniquet inflation. In such a case, an efficient and reliable hypertension diagnosis could help the anesthesiologist in the medication choice. We have previously developed and evaluated an Analgesia / Nociception Index (ANI) based on the magnitude analysis of the respiratory patterns on the RR series. We hypothesize that the use of such an index could help in the arterial hypertension etiological diagnosis during surgical procedures under tourniquet.

Heart rate variability analysis for newborn infants prolonged pain assessment

Pain management is a general concern for healthcare quality. In the particular context of neonatal care, it's well known that an efficient pain management will decrease mortality and morbidity of newborn infants. Furthermore, the plasticity of developing brain is vulnerable to pain and/or stress, that in turn may cause long term neurodevelopmental changes, including altered pain sensitivity and neuroanatomic and behavioural abnormalities. During neonatal intensive care stay, large number of painful procedures are performed, the majority of which are not accompanied by adequate analgesia. Optimal management requires competent pain assessment which can be especially difficult to perform in this non verbal population. We have developed an instantaneous heart rate variability (HRV) analysis method, non intrusive and user-friendly, based on the ECG signal acquisition. This analysis method enabled us to design parameters related to the influence of pain on the Autonomic Nervous System (ANS) activity. This paper presents the application of this method, previously validated for adults under general anesthesia, to the domain of newborn infants prolonged pain assessment.

PhysioDoloris: a monitoring device for analgesia / nociception balance evaluation using heart rate variability analysis

Continuous Analgesia / Nociception balance evaluation during general anesthesia could be of precious help for the optimization of analgesic drugs delivery, limiting the risk of toxicity due to the use of opioid drugs, limiting the risk of post operative hyper algesia, and, probably, reducing time of recovery after surgical procedure. Heart Rate Variability analysis has been shown in several studies to measure the Autonomic Nervous System tone, which is strongly influenced by anesthetic drugs. Recording RR series during general anesthesia enabled us to observe that the Respiratory Sinus Arrhythmia pattern changed when a surgical stimulation was painful, even though the patient was not conscious. We have previously developed and evaluated a pain / analgesia measurement algorithm based on the magnitude analysis of the respiratory patterns on the RR series. In this paper, we present the development of a monitoring device (PhysioDoloris), based on the previously described technology, giving in real time an Analgesia Nociception Index (ANI) which can be used during general anesthesia in order to give to the anesthetist, a complementary tool for optimized drug delivery.

From pain to stress evaluation using heart rate variability analysis: development of an evaluation platform

Heart Rate Variability analysis has been shown in several studies to measure the Autonomic Nervous System tone, which is strongly influenced by pain, stress or anxiety. We have previously described and developed an Analgesia/Nociception Index for pain measurement during surgical procedure. This index based on a heart rate variability analysis can be considered as a vagal tone index and used in several other environments. In this paper, we present an adaptation of our technology for stress / anxiety evaluation in the particular domain of incident management teams training.

Pain/analgesia evaluation using heart rate variability analysis

The optimization of analgesic drugs delivery during general anesthesia requires to evaluate the pain/analgesia balance. Heart rate variability analysis has been shown in several studies to measure the autonomic nervous system tone, which is strongly influenced by anesthetic drugs. Recording RR series during general anesthesia enabled us to observe that the respiratory sinus arrhythmia pattern changed when a surgical stimulation was painful, even though the patient was not conscious. We developed a pain/analgesia evaluation algorithm based on the magnitude analysis of the respiratory patterns on the RR series. The parameters computed from this algorithm were recorded in thirty nine patients during general anesthesia. We retrospectively compared our parameters at different levels of analgesia during surgical stimulation, and found that they were related to pain/analgesia and relatively independent from other anesthesia related events like hypnosis and haemodynamic conditions.