Changes in Heart Rate in Response to Cold Pressor Test
Condition: Pain
Study type: Observational

Summary: 
      Abstract:

      Pain, a subjective sensation, has been increasingly studied, as it has been recognized as an
      important factor in patients' recovery and quality of life. Pain is charted today as one of
      the vital signs. For standardization, pain is charted by a number from 0 to 10 indicating its
      level. The most common practiced pain assessment tool today is the VAS- Visual Analog Score
      (facial or numerical), by which the patient himself indicates the level of the pain he or she
      endures. It has been found that the correlation between the reported pain by the patient and
      the assessed pain by the caregivers or the medical personnel becomes poor as pain
      intensifies.

      Objective assessment of anesthesia using the heart rate and its spectral analyses was done in
      the past. By using this modality, works on neonatal pain were conducted. In adults, works
      have shown that there is possibility to assess pain using this modality, though no repeated
      proof for its ability to detect pain was published.

      We know that physiological signals such as ECG consist of mixtures of variety of patterns and
      phenomena accruing at different patterns and time points. Traditional analysis methods are
      designed and optimized to handle signals that include a single class of patterns such as pure
      harmonics or piece-wise constant functions. However, such basic operations that use a single
      representation method usually yield mediocre results when applied to real complex biological
      signals as ECG and EEG especially in the case where the Signal to Noise Ratio (SNR) is very
      low. Recent trends in digital signal processing (DSP) use the novel idea of merging several
      different representation methods to create a so called over-complete dictionary, examples of
      this approach include the Matching Pursuit algorithm and the Basis Pursuit algorithm. We
      intend to develop and apply the novel signal processing tools to the ECG signals during
      painful experience for the first time. We believe that such tools have the potential to
      provide much better insight of the signal basic components and their relation to pain.
    

Detailed description: 
      Background:

      Pain Assessment:

      Whereas temperature, pulse, respirations, and blood pressure are all objectively measured,
      pain is inherently subjective. Given this fundamental difficulty, it is no wonder that
      failure to properly assess pain is a common cause of its poor control and lack of treatment
      in patients in different settings (1). Moreover, pain is multidimensional, with
      "sensory-discriminatory, cognitive-evaluative, and affective-motivational" components (2), or
      in other words, it affects body, mind, and spirit, and its complexity makes it hard to
      measure (1). Since pain has been recognized as an important modality, influencing recovery
      and quality of life in patients, it has been termed "the fifth vital sign" and is now being
      evaluated and registered in the patients' charts during routine checkups. Pain itself is
      repeatedly rated by the patients and the caregivers or the medical personnel, it is followed
      and treated.

      For routine standardization, pain is charted on a scale of 0(no pain) to 10 (worst pain
      possible). At pain intensities of 0 to 4 patients describe the interference with function as
      mild (as reflected in daily activities and mood), at 5 to 6 it is described as moderate, and
      at 7 to 10 as severe (2). For standard scaling, different assessment tools were developed.
      One-dimensional pain scales, in which the patient is asked to describe the intensity of pain,
      are the most used tool today (1). These are the VAS- Visual analog Score (e.g., the patient
      places a mark on a 10-cm line to indicate the intensity of pain; one end of the line is
      labeled "no pain" and the other "the worst possible pain"), the Numeric ("please rate the
      intensity of pain on a scale of 1 to 10") and the Categorical ("please rate the pain as none,
      mild, moderate, or severe"). These scales are reliable and valid and can be used in
      conjunction with the World Health Organization (WHO) analgesic ladder guideline. (3)

      The one-dimensional intensity scales can be modified to produce a pain relief scale, a
      patient pain satisfaction scale, or a pain management index (4). Moreover, comprehensive
      multidimensional pain assessment tools such as the Brief Pain Inventory were developed to
      help pain management specialist's measure and assess the effect of pain on mood, activities,
      and quality of life-which one-dimensional tools cannot do. (2) These tools are more difficult
      for the patients and the medical personnel to complete and are usually not used in daily
      practice (1).

      In recent years the growing interest in pain and its treatment has generated a greater number
      of studies for the assessment of these tools. The tools are basically regarded reliable but
      Grossman SA et al (5) have found that the intensity of pain expressed by patients on
      self-assessment scales correlates poorly with caregivers' assessments of pain, and the
      greater the intensity of the pain, the poorer the correlation between patient and caregiver.
      Moreover, patients, who can not communicate with the medical staff such as unconscious, or
      sedated patients, young pediatric patients or psychiatric or mentally retarded patients, are
      evaluated subjectively by the caregivers or the medical staff, this with wide variations
      which do not always assess correctly the pain status of the suffering patient. For this
      reason objective pain assessment tools have been suggested in recent past, for more accurate
      and comprehensive indication of this modality in patients in different settings including
      sedation and anesthesia.

      Objective pain assessment:

      Different mathematical tools, mostly the heart rate variability, the heart rate spectral
      analysis were studied to assess the depth of anesthesia and pain (6-7). Most of the works
      done on human patients were done on pain recognition in neonates ( ). Some works on adult
      patients have been done too, trying to define pain using the heart rate and its analysis.
      Since the acute effect of pain to increase heart rate is well known, it was hypothesized by
      Storella et al. (10), in their work that there may be adaptive effects of chronic pain on the
      autonomic regulation of the cardiovascular system that could be reversed by analgesia. They
      examined whether the acute relief of chronic pain affects heart rate variability and
      concluded their study, based on experimental data that acute relief of chronic pain is
      accompanied by an analgesia-specific increase in heart rate variability in many patients. Ray
      et al. in their work used this method for ECG signal processing and developed a new monitor
      for pain assessment during anesthesia (11). Using the heart rate signal, the R-R intervals
      and the spectral analysis were calculated. The output containing the information about the
      respiratory cycles from which the RSA (respiratory sinus arrhythmia) was drawn to which they
      assumed the level of consciousness was proportional. That study has demonstrated the
      feasibility of this method, but their article states many drawbacks and restriction for this
      system and clinically this method is not used today.

      Methods

      Signal processing:

      We know that physiological signals such as the ECG and the EEG consist of mixtures of variety
      of patterns and phenomena accruing at different patterns and different time points during
      each recording. Some occur over very short time intervals, others last longer and some repeat
      periodically. Another characteristic of these signals is the presence of a high leveled
      noise, both systematic and unsystematic.

      Traditional analysis methods are designed and optimized to handle signals that include a
      single class of patterns such as pure harmonics (Fourier Representation/ dictionary) or
      piece-wise constant functions (Wavelets Representation/ dictionary), in simplified and unreal
      case, simple operations such as threshold calculations or filtering in the appropriate
      representation space, can be very effective for separation of signal and noise (denoising),
      decomposition into basic components, pattern detection and more. However, such basic
      operations that use a single representation method usually yield mediocre results when
      applied to real complex biological signals as mentioned above especially in the case where
      the Signal to Noise Ratio (SNR) is very low. Recent trends in digital signal processing (DSP)
      use the novel idea of merging several different representation methods to create a so called
      over-complete dictionary, examples of this approach include the Matching Pursuit algorithm,
      described by Mallat et al (12) and the Basis Pursuit algorithm, described by Donoho et. al
      (13).

      The matching pursuit and the basic pursuit can achieve near-optimal solutions for different
      kinds of analyses of complex signals provided that the appropriate representation methods are
      used. We intend to develop and apply this novel signal processing tools to the ECG signals
      for the first time. We believe that such tools have the potential to provide much better
      insight of the signal basic components and their relation to different physiological states
      than the traditional analysis methods that are practiced today which are based on a single
      dictionary.

      In this study we intend to apply the advanced supervised learning methods, developed by Elad
      (14) in order to adaptively generate optimal dictionaries and representation methods for ECG
      signals and use these dictionaries to develop highly effective over complete dictionary based
      analysis methods in order to separate the complex ECG signal into its basic components and
      noise. We then intend to apply advanced statistical and data mining techniques in order to
      relate the basic patterns of the ECG signals to the pain sampled in the designed groups.

      Test groups:

      In this study we intend to sample the ECG from two groups. The first group will comprise of
      20 healthy young adults who voluntarily will be inflicted by thermal, pressure and neural
      stimuli pain using the Cold pressor test, (this study is to be authorized by the Soroka
      University Medical Center IRB Committee); these subjects will be VAS and ECG monitored
      before, during and after the thermal pain induction. It is important to state that this
      instrument inflicts thermal pain by an authorized protocol, which does not harm or produce
      any tissue damage.

      Hypothesis:

      Our hypothesis is that pain can be detected, discriminated from noise and diagnosed by
      routine ECG samplings using these processing techniques.
    

Observational model: Case-Control
Time perspective: Prospective
Sampling method: Non-Probability Sample
Study population: 
        healthy volunteers
      
Keywords: pain; ECG; Heart rate variability; wavelets