The inter-beat interval (abbreviated as IBI) is the time interval between individual beats of the heart. It's used to estimate the instantaneous heart rate. The IBI sequence provided by Empatica is obtained from the processing of the PPG/BVP signal, with an algorithm that already removes incorrect peaks due to noise in the BVP signal. Both BVP and IBI are provided in the format of .CSV files in the E4 connect.
Computation of IBI
The sequence below describes the processing chain that Empatica employs to compute the IBI from the BVP.
The picture below depicts a typical situation where the PPG data is corrupted by motion artefacts (graph a). The green points represent the good heartbeats, while the red crosses represent the wrong heartbeats, correspondent to a period of intense movement.
Graph b of the picture below indicates how each row is generated.
At UNIX start + t0, there's a diastolic point; d0 is its distance from the previous peak.
The following peak is at t1, and at a distance d1 from t0.
The timings of the wrong beats are not included in the IBI.csv, and therefore it may happen that two consecutive rows in IBI.csv are not consistent with a standard tacogram.
In t2, there are 2 other consecutive beats detected and so we add a new IBI <t2, d2>.
See the representation below of what you'll find in the .csv file, with respect to the situation depicted in the figure above.
If you need to create a standard tacogram you could use the time in the .csv file, and add new rows in the case t2> t1+d2.
Additional considerations
Depending on your experimental environment you should consider the following:
- Studies in a static condition (i.e. pictures, movies, lessons): use the IBI as provided.
- Studies where movements hold for less than 30% of the time: use the IBI as provided but be prepared to visually inspect the IBI, and use only segmented portions that are good. (There are also now starting to be algorithms to automate the detection of good regions).
- Studies with strong movement (more than 30% of the time): for these, you will probably not be able to get enough reliable IBI to compute heart rate variability continuously. However, you can still compute the average heart rate like consumer devices have done for over a decade. For example, you can use the motion information that our sensor gives, and when it is large, discard the IBI's with huge motion. Then take the average of the remaining IBI and check that they are in a reasonable range that has not changed too abruptly from the last estimate.
What works best will depend on what your specific goals are. Empatica research sensors give you access to the raw data so that you can make the best decisions for your application.
IBI file
See an extraction of the IBI.csv file below: it represents the header and the first rows of the file. No sample rate is needed for this file. The first column is the time (with respect to the initial time, reported in the first raw in UNIX) of the detected inter-beat interval expressed in seconds (s). The second column is the duration in seconds (s) of the detected inter-beat interval (i.e., the distance in seconds from the previous beat).
Visualization of the IBI on the Connect
The IBI values are not directly displayed in the E4 Connect portal, but instead a derived instantaneous heart rate is displayed: the format of the signal is number of beats per minute [bpm] which is then an heart rate measure, obtained using the formula below:
HR from IBI [bpm] = 60/IBI;
The visualization of the HR from the IBI is obtained by connecting all the consecutive points in time with a line; when values are not available in the short period, then an interpolated line connects the latest available point with the first one after the lapse of data.
An example of an interpolated HR from IBI is shown below: the values between 14:41 and 14:47 are interpolated.
What can be derived from the IBI?
In normally functioning heart, each IBI value varies from beat to beat. This natural variation is known as heart rate variability (HRV). Heart rate variability is of huge interest in studies of stress and its impact on medical conditions. This metric is not inside the set of data that Empatica offers at this time, but we can suggest different ways to extract this information from the IBI.csv file included in the archive that can be downloaded from the E4 Connect portal.
Here you can find an article about suggested tools for the data provided: Recommended tools for signal processing and data analysis
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