Five different kinds of biofeedback are used to monitor client breathing. For clinicians who treat patients for breathing disorders like asthma and chronic obstructive pulmonary disease (COPD), a capnometer can provide helpful information about end-tidal CO2 levels that supplements amplitude, pattern, and respiration rate measurements supplied by a strain gauge. Heart rate variability (HRV) biofeedback is an exciting modality that shows promise in the treatment of both cardiovascular and respiratory disorders.

This unit covers Descriptions of most commonly employed biofeedback modalities: Respiration, EKG and heart rate (III-A) and Structure and function of the autonomic nervous system (V-A).
Students completing this unit will be able to discuss:

  1. Descriptions of most commonly employed biofeedback modalities: Respiration, EKG and heart rate
    A. Sensors and sensor placements
    B. Characteristic signals
    C. Signal processing and feedback displays
  2. Structure and function of the autonomic nervous system
    A. The effects of commonly employed medications on autonomic activity

An incentive inspirometer consists of a calibrated cylinder with a piston that moves upward as the patient inhales and indicates the volume of air inhaled in a single breath (the incentive). Peper and colleagues pioneered the use of incentive inspirometer feedback to train asthma patients to effortlessly increase inhalation volume. This strategy can also be used with hyperventilation syndrome and chronic obstructive pulmonary disease patients.

Strain gauge biofeedback uses a flexible sensor band that is placed around the chest, abdomen, or both. The strain gauge method can provide feedback about the relative expansion/contraction of the chest and abdomen, detect reverse breathing, and measure respiration rate.
Below is a BioGraph ® Infiniti display of chest and abdominal strain gauge movement. Note the slow rhythmic abdominal breathing pattern.

Timmons (1994) showed that strain gauge biofeedback is superior to visual observation.


Strain gauge biofeedback has two limitations: measurements are in relative units and breathing mechanics can look correct while end-tidal CO2 and RSA are reduced due to excessive effort, and while heart rate changes are out of phase from breathing (Shaffer, Bergman, & Dougherty, 1998).

Surface EMG (SEMG) biofeedback for accessory breathing muscles and facial muscles has been used to treat asthma patients. Peper and Tibbetts (1992) demonstrated that SEMG accessory muscle/incentive inspirometer biofeedback for asthma patients increased inhalation volumes and reduced asthma symptoms, medication use, emergency room visits, and breathless episodes.

An SEMG placement to monitor accessory muscles (trapezius and scalene) is shown below.


The BioGraph ® Infiniti screen below provides strain gauge and SEMG biofeedback to teach rhythmic breathing while maintaining relaxed accessory muscles.

Capnometric biofeedback provides the patient with continuous information about end-tidal CO2. The goal of training is to normalize end-tidal CO2 to about 5% (36 torr).

Fried (1990)
reported successful treatment of patients diagnosed with hyperventilation syndrome with capnometric biofeedback.

Heart rate variability (HRV) is measured by detecting the interbeat interval between successive R-waves using either EKG sensors on the wrists or torso, or a PPG sensor on a finger or earlobe.

The SDNN is the standard deviation of the N-to-N interval. The N-to-N interval is the normalized interbeat interval. The SDNN measures how these intervals vary over time and is expressed in milliseconds (ms).
The SDNN is more accurate when calculated over 24 hours than during the shorter periods monitored during biofeedback sessions. This measure is a frequently-used medical index of heart rate variability that is used to estimate cardiac risk (Shaffer & Moss, 2006). Below is a BioGraph ® Infiniti SDNN display.

The pNN50 is the percentage of adjacent N-to-N intervals that differ from each other by more than 50 milliseconds. This may be a more reliable index than the SDNN for the brief samples used in biofeedback. At least a 2-min sample is required to calculate an accurate pNN50.

HR Max – HR Min is the difference between the highest heart rate and the lowest heart rate within each cardiac cycle, measured in beats per minute.

Following normal skin preparation for SEMG, active EKG electrodes (which can be identical to SEMG electrodes) should be placed on the wrist over the radial artery on the left and right hands. The reference electrode can be placed over the dorsal aspect of the wrist on the right hand. This placement, described by Papillo and Shapiro (1990), is an excellent way to detect the EKG signal since this wrist-to-wrist configuration creates a large-surface area antenna.

A photoplethysmographic (PPG) sensor provides an alternative method of calculating heart rate (beats per minute) by measuring the interbeat interval (time period between successive heartbeats). Divide the time interval between peaks by 60 s to calculate heart rate (Peper, Harvey, Lin, Tylova, & Moss, 2007).

Caption: Heart rate is derived from measures of blood volume pulse by measuring the interbeat interval and then transforming this information into beats per minute. For example, the interbeat interval of 0.80 seconds is equal to a heart rate of 75 beats per minute, whereas the interbeat interval of 0.93 seconds is equal to a heart rate of 64.5.

Portable HRV systems utilizing a PPG sensor, like the emWave by the Institute of HeartMath and the StressEraser by Helicor Inc. have been developed.

A breathing harness can be placed snuggly over the chest and abdomen (navel).

Shaffer and Moss (2006) describe HRV training: "Therapists use either the electrocardiogram (EKG) or photoplethysmograph (PPG) to monitor the frequency bands that comprise heart rate variability. They also use a respiratory strain gauge to measure abdominal or chest expansion and contraction during each respiratory cycle and respiration rate. Relaxed breathing produces a biofeedback display showing a smooth sinusoidal line, depicting exhalation and inhalation, and a parallel smooth sinusoidal line showing heart rate variation. Anxious thoughts, on the other hand, produce a jagged irregular respiration signal and a jagged irregular variation in heart rate. Producing this coherence––or smoothly organized and regular variation––of the respiratory and heart rate displays is also a training goal for biofeedback."



The BioTrace+ / NeXus-10 display below shows patients how heart rate changes during inspiration and expiration. In healthy patients, it should increase during inspiration and decrease during expiration. In patients who hyperventilate or show a thoracic breathing pattern, there may be minimal heart rate variability and the heart rate trace may be out-of-phase with the abdominal strain gauge trace.

Heart rate variability is produced by the interaction of multiple regulatory mechanisms that operate on different time scales (Moss, 2004). Spectral analysis separates heart rate variability into its component rhythms that operate within different frequency bands. The Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996) has divided heart rate oscillations into the following bands:

The ultra low frequency (ULF) band (below 0.0033 Hz) represents very slow-acting biological processes and is too gradual to train using conventional biofeedback.

The very low frequency (VLF) band (0.005-0.05 Hz) may represent sympathetic activation. Worry and rumination increase the power of this waveform.

The low frequency (LF) band (0.05-0.15 Hz) may represent the influence of both the parasympathetic and sympathetic branches and blood pressure regulation via baroreceptors or blood pressure receptors (Lehrer, 2007). Meditation and slow breathing, like the “tanden breathing” practiced by Rinzai Zen monks, increase the power of this waveform.

The high frequency (HF) or respiratory band (0.15-.40 Hz) represents the inhibition and activation of the vagus nerves by breathing at normal rates (Moss, 2004).

In HRV training, the clinician teaches clients to gradually slow respiration to their personal resonant frequency, from 4.5-7.5 breaths per minute, to increase the amplitude around .1 Hz. This maximizes HRV because it combines the influence of baroreceptors and the parasympathetic system.

The BioGraph Infiniti display below is designed to increase the percentage of power in the low frequency band.

Now that you have completed this module, describe the method you use to monitor client breathing and the types of measurements you record. If you use a strain gauge to measure breathing, where do you place the strain gauge and why? What precautions do you take when attaching the strain gauge to female clients?

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Fried, R. (1987). The hyperventilation syndrome: Research and clinical treatment. Baltimore: John Hopkins University Press.

Lehrer, P. M. (2007). Biofeedback training to increase heart rate variability. In P. M. Lehrer, R. M. Woolfolk, & W. E. Sime (Eds.). Principles and practice of stress management (3rd ed.). New York: The Guilford Press.

Lehrer, P. M., Vaschillo, E., & Vaschillo, B. (2000). Resonant frequency biofeedback training to increase cardiac variability: Rationale and manual for training. Applied Psychophysiology and Biofeedback, 25(3), 177-191.

Moss, D. (2004). Heart rate variability (HRV) biofeedback. Psychophysiology Today, 1, 4-11.

Peavey, B. (2003). Effects of drugs on biofeedback. Short course presented at the 34th annual Association for Applied Psychophysiology and Biofeedback convention, Jacksonville, Florida.

M. S. Schwartz, & F. Andrasik (Eds.). (2003). Biofeedback: A practitioner's guide (3rd ed.). New York: The Guilford Press.

Peper, E., Harvey, R., Lin, I., Tylova, H., & Moss, D. (2007). Is there more to blood volume pulse than heart rate variability, respiratory sinus arrhythmia, and cardio-respiratory synchrony? Biofeedback, 35(2), 54-61.

Shaffer, F., Bergman, S., & Dougherty, J. (1998). End-tidal CO2 Is the best indicator of breathing effort [Abstract]. Applied Psychophysiology and Biofeedback, 23(2).

Shaffer, F., & Moss, D. (2006). Biofeedback. In Y. Chun-Su, E. J. Bieber, & B. Bauer (Eds.). Textbook of complementary and alternative medicine (2nd ed.). Abingdon, Oxfordshire, UK: Informa Healthcare.

Stern, R. M., Ray, W. J., & Quigley, K. S. (2001). Psychophysiological recording (2nd ed.). New York: Oxford University Press.

Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology (1996). Heart rate variability: Standards of measurement, physiological interpretation, and clinical use. Circulation, 93, 1043-1065.