Heart Rate Variability Analysis
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Short-term HRV analysis and assessment of the autonomic regulation
It is believed that Heart Rate
Variability (HRV) will become as common as pulse, blood pressure or
temperature in patient charts in the near future. In the last ten years
more than 2000 published articles have been written about HRV. HRV has
been used as a screening tool in many disease processes. Various
medical disciplines are looking at HRV. In diabetes and heart disease
it has been proven to be predictive of the likelihood of future events.
In 1996, a special task force was formed between the US and European
Physiological associations to outline current finds on HRV and set
specific standards on using HRV in medical science and future practice.
Since then a steady stream of new information and value continues to
come out of HRV research.
It all started in 1966 when a variation
in the beat-to-beat intervals between heartbeats was noticed. Initially
all recording devices were averaging heart rate data stream trying to
get rid of any rapid HR fluctuations. Then there were very specific
patterns in such fluctuations were noticed that had links to certain
conditions way before any clinical symptoms appeared.
Physiological Phenomenon of HRV
The origin of heartbeat is located in a
sino-atrial (SA) node of the heart, where a group of specialized cells
continuously generates an electrical impulse spreading all over the
heart muscle through specialized pathways and creating process of heart
muscle contraction well synchronized between both atriums and
ventricles. The SA node generates such impulses about 100-120 times per
minute at rest. However in healthy individual resting heart rate (HR)
would never be that high. This is due to continuous control of the
autonomic nervous system (ANS) over the output of SA node activity,
which net regulatory effect gives real HR. In healthy subject at rest
it is ranging between 50 and 70 beats per minute.
The autonomic nervous system is a part
of the nervous system that non-voluntarily controls all organs and
systems of the body. As the other part of nervous system ANS has its
central (nuclei located in brain stem) and peripheral components
(afferent and efferent fibers and peripheral ganglia) accessing all
internal organs. There are two branches of the autonomic nervous system
- sympathetic and parasympathetic (vagal) nervous systems that always
work as antagonists in their effect on target organs.
For most organs
including heart the sympathetic nervous system stimulates organ's
functioning. An increase in sympathetic stimulation causes increase in
HR, stroke volume, systemic vasoconstriction, etc. The heart response
time to sympathetic stimulation is
relatively slow. It takes about 5 seconds to increase HR after actual
onset of sympathetic stimulation and almost 30 seconds to reach its
peak steady level.
In contrast, the parasympathetic
nervous system inhibits functioning of those organs. An increase in
parasympathetic stimulation causes decrease in HR, stroke volume,
systemic vasodilatation, etc. The heart response time to
parasympathetic stimulation is almost instantaneous. Depending on
actual phase of heart cycle it takes just 1 or 2 heartbeats before
heart slows down to its minimum proportional to the level of
stimulation.
At rest both sympathetic and
parasympathetic systems are active with parasympathetic dominance. The
actual balance between them is constantly changing in attempt to
achieve optimum considering all internal and external stimuli.
There are various factors affecting
autonomic regulation of the heart, including but not limited to
respiration, thermoregulation, humoral regulation (rennin-angiotensin
system), blood pressure, cardiac output, etc. One of the most important
factors is blood pressure. There are special baroreceptive cells in the
hear and large blood vessels that sense blood pressure level and send
afferent stimulation to central structures of the ANS that control HR
and blood vessel tonus primarily through sympathetic and somewhat
parasympathetic systems forming continuous feedback dedicated to
maintain systemic blood pressure. This mechanism is also called
baroreflex, which increases HR when blood pressure decreases and vice
versa. This mechanism is also targeted to maintain optimal cardiac
output.
The heart rate variability analysis is
a powerful tool in assessment of the autonomic function. It is
accurate, reliable, reproducible, yet simple to measure and process.
The source information for HRV is a continuous beat-by-beat measurement
of interbeat intervals. The electrocardiograph (ECG or EKG) is
considered as the best way to measure interbeat intervals. ECG is an
electrical signal measured with special conductive electrodes placed on
chest around heart area or limbs. It reflects minute changes in
electrical field generated by heart muscle cells originating from its
SA node. ECG signal has a very specific and robust waveform simple to
detect and analyze. Because of that cardiac rhythm derived from ECG is
the best way to detect not only true sinus rhythm but all types of
ectopic heartbeats, which must be excluded from consideration of HRV
analysis.
The other approach
to measure cardiac intervals is a measurement of pulse wave. It is less
invasive and simple method of measurement based on photoplethysmograph.
PPG is a signal reflecting changes in a blood flow detected when
infrared light is emitted towards microcirculatory blood vessels.
Depending on blood flow volume certain portion of that light is
absorbed letting other part to pass or b reflected. An optical sensor
detects a quantity of light passed (or reflected from) the blood flow
producing a waveform identifying pulse wave. Such waveform can also be
processed to derive beat-by-beat interbeat intervals. Although PPG
gives the summary information reflecting both cardiac and
blood vessel components of HRV, some research studies showed a
significantly high correlation between interbeat interval data measured
by both ECG and PPG in short-term steady-state recordings.
One of the important issues when
measuring either ECG or PPG is the absence of abnormal heartbeat used
in interval detection. Only heartbeats originated in SA node can be
processed to obtain HRV data. Whether there are ectopic heartbeats
(PVCs or other types of extrasystolic heartbeats) or various movement
artifacts on ECG (or PPG) considered as heartbeats, they must be
excluded from consideration. There are various statistically-based
algorithms of detection of such abnormal heartbeats that minimize
chances to get contaminated HR recordings. Nevertheless, for the sake
of accuracy in HRV analysis it is important to be able to visually
verify all heartbeats automatically found, remove abnormal ones and
include missing.
Short-term HRV analysis requires much
shorter recordings - typically 5-min long. However such recordings are
assumed to be done at steady-state physiological condition and should
be properly standardized to produce comparable data. Typically such
measurements should be done in either supine or comfortably sitting
relaxed position, limiting body movements, conversations, any mental
activities.
According to the standards set forth by
the Task Force of the European Society of Cardiology and North American
Society of Pacing and Electrophysiology in 1996, there are two methods
of analysis of HRV data: time- and frequency-domain analysis. In either
method, the interbeat intervals should be properly calculated and any
abnormal heartbeats found.
Time-domain measures are the simplest
parameters to be calculated. Before such calculation all abnormal
heartbeats and artifacts must be removed from consideration. The
following time-domain parameters can be calculated for both long-term
and short-term recordings: Mean HR, SDNN and RMS-SD. Some extra
parameters can be calculated specifically for long-term recordings. The
time-domain parameters are associated mostly with overall variability
of HR over the time of recording, except RMS-SD, which is associated
with fast (parasympathetic) variability.
Frequency-domain measures pertain to HR
variability at certain frequency ranges associated with specific
physiological processes. Before frequency-domain analysis is performed,
all abnormal heartbeats and artifacts must be detected and removed,
then cardiotachogram (sequence of RR intervals) must be resampled to
make it as if it is a regularly sampled signal. A standard spectral
analysis routine is applied to such modified recording and the
following parameters evaluated on 5-min time interval: Total Power
(TP), High Frequency (HF), Low Frequency (LF) and Very Low Frequency
(VLF). When long-term data is evaluated an additional frequency band is
derived - Ultra Low Frequency.
The HF power spectrum is evaluated in
the range from 0.15 to 0.4 Hz. This band reflects parasympathetic
(vagal) tone and fluctuations caused by spontaneous respiration known
as respiratory sinus arrhythmia.
The LF power spectrum is evaluated in
the range from 0.04 to 0.15 Hz. This band can reflect both sympathetic
and parasympathetic tone.
The VLF power spectrum is evaluated in
the range from 0.0033 to 0.04 Hz. The physiological meaning of this
band is most disputable. With longer recordings it is considered
representing sympathetic tone as well as slower humoral and
thermoregulatory effects. There are some findings that in shorter
recordings VLF has fair representation of various negative emotions,
worries, rumination etc.
The TP is a net effect of all possible
physiological mechanisms contributing in HR variability that can be
detected in 5-min recordings, however sympathetic tone is considered as
a primary contributor.
The LF/HF Ratio is used to indicate
balance between sympathetic and parasympathetic tone. A decrease in
this score might indicate either increase in parasympathetic or
decrease in sympathetic tone. It must be considered together with
absolute values of both LF and HF to determine what factor contributes
in autonomic disbalance.
The frequency domain analysis is
traditionally performed by means of Fast Fourier Transformation (FFT).
This method is simple in calculation but for fair representation of all
frequency-domain HRV scores at least 5-min data should be collected.
FFT assumes that time series represents a steady-state process. Because
of that all data recordings should be conducted at highly stable
standardized conditions, when no other factors other than current
autonomic tone contributes in HRV. One of the most serious
disadvantages of that is its insensitivity to rapid transitory
processes, which often possess very valuable information about how
physiology or certain pathological processes behave dynamically.
Some most recent studies implemented an
alternative way to estimate power spectrum of HRV. It is based on
autoregression methods. One of its major advantages is that it doesn't
require to have analyzed data series to be in steady state. Thus any
HRV data can be analyzed and fair HRV information still derived. Such
analysis can be also performed on relatively shorter time intervals
(less than 5 minutes) without missing meaningful HRV information.
Finally this method is sensitive to rapid changes in HR properly
showing tiny changes in autonomic balance. The drawback of this
approach is a necessity to perform massive calculations to find best
order of autoregression model.
Normative Data Sets
From clinical
perspective it is important not only to evaluate all HRV scores but be
able to assess such HRV data, whether they are normal or not and how to
interpret such data. It is known that HRV scores are age-dependent.
Most of scores decrease with age. For better HRV data assessment
special sets of reference ranges for each HRV parameter were
created. Such ranges are based on statistics derived from HR data
measured in a number of healthy individuals of different ages under
standardized conditions. Such norms are considered as a reference point
and cannot be used for any diagnostic purpose.
Clinical Significance of HRV
It is found that lowered HRV is
associated with aging, decreased autonomic activity, hormonal tonus,
specific types of autonomic neuropathies (e.g. diabetic neuropathy) and
increased risk of sudden cardiac death after acute MI.
Other research indicated that
depression, panic disorders, anxiety have negative impact on autonomic
function, typically causing depletion of parasympathetic tonus. On the
other hand an increased sympathetic tonus is associated with lowered
threshold of ventricular fibrillation. These two factors could explain
why such autonomic imbalance caused by significant mental and emotional
stress increases risk of acute MI followed by sudden cardiac death.
Besides that there are multiple studies
indicating that HRV is quite useful as a way to quantitatively measure
physiological changes caused by various interventions both
pharmacological and non-pharmacological during treatment of many
pathological conditions having significant manifestation of lowered HRV.
Continuous HRV monitoring
Most known existing HRV measurement
tools available at this time have capability to measure short-term HR
data and provide their further static assessment. Each assessment
provides information on what happened at certain point of time without
any consideration of the dynamics of physiological changes reflected in
HRV.
We have recently developed a new
approach of HRV evaluation that is performed repetitively and
continuously over longer periods of time. A standard 5-min time window
is used for such HRV evaluation which is continuously updated every
half a second. A robust method of real-time HR data evaluation and
artifact detection is implemented to ensure reliability of HRV data.
Use of new autoregression method of power spectrum calculation allows
for fast and reliable early detection of transitory processes caused by
various interventions. This tool is especially effective when various
effects are examined. As an example, when performing tests for specific
allergies or toxins various substances have to be tested with regard to
what kind of autonomic response they create. Having ability to detect
sudden changes in autonomic balance gives an opportunity to reliably
detect such agents that cause such allergic reactions by means of
affecting autonomic balance. When monitoring HRV changes multiple time
markers can be posted along with data recorded. When HRV data is
reviewed multiple comparisons of the data associated with those time
markers can be performed.
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