We present an interactive visualization tool for exploring RR interval data (the time between consecutive heart beats) to support diagnostics. An RR interval sequence diagram allows us to reduce hours of data into a general overview opposed to using short term ECG strips. A simple moving average is applied to the sequence diagram to smooth out short-term variance and highlight long-term trends. The moving average is surrounded by standard deviation bands which allow us to see the lfuctuations in variance. After a brief training period using these tools coupled with RR interval and RR interval diference histograms, nonexpert participants (undergraduate students) were able to diferentiate between normal, atrial fibrillation, and congestive heart failure.
Our visualization is comprised of two diferent sections, the main RR interval sequence diagram, and the RR interval histograms. Figure 1 shows the RR sequence diagram and a seconds by hours scatter plot of each RR interval in the signal. With 24 hours of data, this sequence diagram could contain over 200,000 RR intervals causing them to be clustered together making it virtually impossible to trace between consecutive RR intervals. To address this, each RR interval point has an opacity of 0.1 allowing us to focus on the dense regions of RR intervals by reducing the distraction caused by outliers within the signals. We also provide the ability to apply a simple central moving average (mAvg) across the sequence diagram allowing us to smooth out short-term fluctuations and highlight long term-trends. The window size of the mAvg can be defined to suit the needs of the user. A larger window size will reduce the efect of short term variance highlighting the long term trend, while a smaller window size will highlight the short term variance.
The mAvg is accompanied by upper and lower standard deviation bands calculated by finding the standard deviation of the subset of values above the mean and below the mean for each average within the mAvg. The upper standard deviation is then added to the respective mean and plotted on the sequence diagram while the lower one is subtracted from the respective mean. By plotting these upper and lower deviations for each average within the mAvg, we obtain two bands that surround the mAvg. The amount of variation that exists throughout the sequence can be inferred from a quick glance at the width of the bands. A larger band represents a greater amount of variance than a thinner band.
Two histograms are included, an RR interval histogram (IH) and an RR
interval diferences histogram (IDH) (see Fig. 1). Significant amounts of data
from a signal can be encoded into these normalized histograms, allowing easy
comparison across time scales. For example a large amount of data (24 hour
signal) can be compared to a small amount (4 hours) because we care more
about the shape (distribution) of the histograms than we do the total values.
It has been shown that when comparing or classifying signals, using both the
IH and IDH combined provides more reliable qualitative information than the
IH alone [
A decreased amount of short term variability has been found to be one of the
indicators of congestive heart failure [
By adding a moving average and standard deviation bands to the CHF signal (Fig. 3) we obtain a figure that we would expect to see from a signal with low short term variation. The short term variation causes the bands to be incredibly tight around the moving average. Considering the RR interval points are semitransparent, these tight bands allow us to confirm that there are no clusters of RR intervals surrounding the moving average as they would increase the width of the bands. Such clusters would increase the amount of short term variance lowering the probability of CHF. It should also be noted that when adding a moving average the IDH is calculated as diferences from the moving average, which is why the IDH in Fig. 3 is wider than the IDH in Fig. 2.
Therefore by using both techniques together we can infer a low short term variability from the IDH when no moving average has been applied and that no hidden clusters surround the moving average once the bands have been applied. Both of these are evidence that CHF could be the cause of such cardiac patterns.
Atrial fibrillation characteristically appears in the RR interval sequence as
a curtain of points (essentially a large amount of short term variation). Atrial
ifbrillation can appear for short or long periods of time and it can be almost
impossible to determine a baseline average at a glance during a period of atrial
ifbrillation. Without the added moving average it has been shown that atrial
ifbrillation produces wide triangular histogram patterns within the IDH [
The moving average after being applied to the AF signal allows us to see
the baseline throughout the period of AF as shown in Fig. 5. Notice how the
standard deviation bands around the moving average during the period of atrial
ifbrillation are wide, showing that there is a high amount of short term variation.
The IDH also produces a poly-modal distribution during the period of AF. Not
all instances of AF have been shown to produce these poly-modal distributions
but (after analyzing all the signals in the nsrdb [
The main factor varied in the experiment was interface style (2 levels): Basic style, and Statistical style. Participants were trained on how to use each style and how to find characteristics of atrial fibrillation (AF), congestive heart failure (CHF), and normal sinus rhythm using that style. After hands-on training, participants were able to explore 6 training datasets provided for each style to practice making a diagnosis. During this stage participants were aware of the proper diagnosis for each signal provided and the exploration was intended for them to identify patterns within the signals they could recognize in the trial datasets.
After training they were tasked with diagnosing datasets as normal or
abnormal. If they believed it to be abnormal, they also stated whether any instances
of AF or CHF exist. After selecting their diagnosis they were asked to rate the
confidence of their diagnosis on a scale of 1 to 7, 1 being not confident at all and
7 being completely confident. The Basic style was created to replicate the time
domain analysis tools provided by a state-of-the-art HRV analysis toolkit [
Given the success of the participants in diferentiating between normal and
abnormal signals after a brief training period, it can be said that patterns can be
easily recognized when searching for AF or CHF. Assuming these conditions are
not the only ones with easily recognizable characteristics, our visualization will
be able to support clinical decisions as patterns of other conditions are discovered.
A promising condition that could be investigated would be diabetic autonomic
neuropathy due to the low variation of heart rate caused by this condition [