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.

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.

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.

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.

A smart modules network for real time data acquisition: application to biomedical research.

Healthcare monitoring applications require 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. In this paper, we describe a smart hardware modules network for biomedical data real time acquisition. This toolkit, composed of multiple electronic modules, allows users to acquire and transmit all kind of biomedical signals and parameters. These highly efficient hardware modules have been developed and tested especially for biomedical studies and used in a large number of clinical investigations.

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.

An efficient algorithm for R-R intervals series filtering.

Spectral analysis of heart rate variability (HRV) constitute a simple and non invasive way to study the autonomic nervous system (ANS) activity. On-line implementation of this technique would allow to follow the evolution of the ANS activity and to track transient events during medical procedures. However, continuous spectral analysis of HRV is not reliable enough due to the difficulty to obtain a noiseless ECG signal during a long period. Indeed, the consequential effects of each ECG signal perturbation on the R-R intervals gives an erroneous evaluation of HRV spectral analysis. In this article, we describe a real time filtering algorithm for R-R intervals series. This filter is able to detect each disturbed area and to replace the erroneous samples with the most probable ones. Therefore, this method allows detecting and replacing up to 90 % of R-R series erroneous samples while keeping the real recording time and without having any effect, beyond measure, on the frequency analysis result.