A multi parameter index for the presence and severity of pain

Medasense has developed a patented technology platform to objectively assess the physiological response to pain (nociception), by leveraging composite artificial intelligence algorithms to process and analyzing dozens of pain-related physiological parameters.
The technology combines a non-invasive finger probe incorporating 4 sensors (PPG- photoplethysmograph, GSR- Galvanic Skin Response, Temperature, and Accelerometer).




The finger probe continuously acquires physiological signals through 4 sensors.

Dozens of pain-related physiological parameters and derivatives are extracted and computed (Heart rate, Heart rate variability, Pulse wave amplitude, Skin conductance level, Number of skin conductance fluctuations, temperature and more).

Advance machine learning algorithms identify the pain-related pattern and reflect the information on a scale where 0 represents no pain and 100 represents extreme pain – the NOL™ (Nociception Level) Index.

NOL™ provides superior indication of the presence and severity of pain vs. individual parameters (such as changes in Heart rate and Blood pressure).

The easy-to-interpret NOL™ index monitors and grades pain levels objectively, enabling optimal personalized pain treatment.

The technology was validated in trials with hundreds of patients (in Europe, Canada, and Israel) and was identified as superior to existing pain indicators currently used to assess pain.

  • Clinically proven as superior to other pain indicators 1,2
  • NOL™ is a multi-parameter index- delivers more reliable and robust measurements
  • Safe, noninvasive, continuous, and simple to use

"Multi-variable approaches appear to be superior predictors of pain intensity and intra-operative nociception to any individual parameter alone."

“Assessing pain objectively: the use of physiological markers”, Rachel Cowen, Maria Stasiowska, Helen Laycock, Carsten Bantel. Anaesthesia. 2015 July;70(7):828-47

Process diagram


The non-invasive finger probe continuously acquires physiological signals through 4 sensors.

Dozens of measurable  changes in the pain-related physiological parameters and derivatives are extracted.

Advanced machine learning algorithms identify the pain-related pattern.

The information is quantified and visualized on a scale where 0 represents no pain and 100  represents extreme pain – the NOL™ (Nociception Level) index.

"Science appreciates that it is the interactions and relationships between variables that predict the responses of complex systems, rather than the absolute values of one parameter. This may be relevant to assessing pain, as it is highly unlikely such a complex experience is truly reflected by evaluating one autonomic variable or derived measure alone… Combinations that best predict the presence and severity of pain are then used to create the algorithms… exemplified for instance by the nociceptive level index."

“Objective Assessment of Acute Pain”, Helen Laycock and Carsten Bantel, Journal of Anesthesia & Clinical Research, June 02, 2016



  1. Intraoperative validation of the NOL Index, a non-invasive nociception monitor. Ruth Edry, Vasile Recea, Yuri Dikust, Daniel I. Sessler. Anesthesiology July 2016, Vol.125, 193-203
  2. Ability of the Nociception Level (NOL), a multiparameter composite of autonomic signals, to detect noxious stimuli during propofol-remifentanil anesthesia. Chris H. Martini, Martijn Boon, Suzanne J. L. Broens, Evelien F. Hekkelman, Lisanne A. Oudhoff, Anna Willemijn Buddeke, Albert Dahan. Anesthesiology Sept. 2015, Vol.123, 524-534