Supraventricular Tachycardia to Normal Sinus Rhythm
67 year old male patient monitored during esophagogastrectomy. Patient has a history of smoking and aspiration.
Rhythm analysis indicates supraventricular tachycardia (SVT) which converts into normal sinus rhythm (NSR).
This encounter shows supraventricular tachycardia, a very fast and regular rhythm of over 100 bpm with sudden onset. One premature ventricular contraction (PVC) can be seen. The SVT then converts into normal sinus rhythm (NSR), with normal P waves and a normal heart rate below 100 bpm.
EKGmon is a telemetry monitoring and quiz platform. It simulates EKG monitors found in hospitals, by streaming EKG data to a display in real time.
Both information and quiz modes are available from the top menu. Speed and amplitude of the waveforms can be adjusted to better view telemetry data.
What is the purpose of EKGmon?
EKGmon has several distinct advantages over traditional EKG training methodologies. The data provided by EKGmon only comes from real patient encounters. No data is simulated.
The EKG data here is presented in a streaming fashion, very similar to hospital telemetry monitors. This is far different than other methodologies which only utilize static 6-second strips for training.
Dual signals are also provided, typically on Leads II and V1. This allows for an immersive experience most similar to that of a hospital EKG tech or nurse.
How can EKGmon help me?
Whether you are training to become a nurse, physician, tech, EMT, or are already in the medical field, you can likely benefit from watching episodes on EKGmon. It allows you to see some of the most common monitoring scenarios and dangerous rhythms without any risk to a real patient.
EKGmon also allows you to view dangerous rhythm and event transitions in real time. This will help prepare you for EKG monitoring at your workplace.
How many quiz questions does EKGmon have?
EKGmon currently features 70 patient encounters with 100 multiple choice questions. New samples and questions are being added on a routine basis.
How long are the patient encounter samples shown?
Most of the encounters shown are 1 minute in length. The samples then loop and replay. You should always carefully watch the patient encounters before selecting a quiz answer. Remember that these are streaming episodes, not static strips!
How similar is EKGmon to a real hospital telemetry monitor?
The experience between watching EKGmon and a hospital telemetry monitor is very similar. Telemetry monitors have a unique plotting style, "drawing" the EKG wave across the screen, then overwriting the wave on the next pass. This unique format allows for easy analysis of cardiac rhythms.
Real telemetry monitors also allow you to adjust the speed and amplitude of the EKG waves. Depending on the patient, electrode placement, and other factors, the amplitude (or height) of the waves can change dramatically. This sometimes requires "zooming in" in order to better see important details. You can see just how much amplitude changes from patient to patient simply by looking at samples here. Additionally, the "baseline" of the EKG (which should be at 0 voltage or the exact center of the chart) can also change or vary depending on conditions.
I have attempted to pick cleaner samples for this site in general, but real patients cause a lot of what we call "artifacts" on EKG monitors. Many factors such as electrode placement, body weight, movement, muscle tremors, shivering, or even pacemakers can cause the EKG signal to become noisy or difficult to read. This also wreaks havoc on the automated rhythm warnings generated by telemetry systems!
Real telemetry monitoring systems do include more advanced features not seen here, such as dynamic warning of dangerous rhythms. For example, VTach, VFib, Asystole, and AFib are commonly detected by telemetry equipment and reported to personnel in the form of audible and visual alerts. However, these alerts often have a high false-positive rate, due to the artifacts mentioned above.
What are the specs on the grid, and is it accurate?
The grid has been designed to be accurate for general measuring. Each small grid square has a standard size of .20s (horizontal) x .5mV (vertical). The entire grid scales up to 10s x 4mV. Thus the amplitude limits shown within the grid are -2mV to 2mV. On mobile devices, the horizontal length varies. While telemetry monitors can allow for various timeframes depending on the width of the grid and monitor, here we provide up to a 10-second grid.
With all of that in mind, I'm not sure if you want to put a caliper on the monitor :) But you can get a good idea of a Wide QRS, or 1st Degree Block, and amplitude of waves. Keep in mind that "zooming in" with the speed and amplitude controls will make the measurement squares useless. You can click on the "Speed" and "Amp" buttons which will reset the sliders to their default values, and thus the waveforms will again be accurate for measuring.
Which leads are being monitored and what are they useful for?
Whenever possible Lead II and Lead V1 are provided for analysis. Lead II is the most commonly monitored lead, while Lead V1 is a good complimentary lead. Multiple leads allow for far more diagnostic potential. For example, an EKG tech can identify a right or left bundle branch block utilizing Lead V1. Multiple leads also help to confirm an anomaly or rule out artifacts. Note that the leads do change depending on the source data. You can see the lead names in the upper left corner of the grid.
Where does the EKG data come from?
All the raw data for EKGmon comes from the Physionet.org website, which houses an incredible amount of telemetry data. Per their requirements, I am stating here that the data obtained from Physionet HAS been modified. I have preprocessed the data in order to normalize all sample rates. This is done through the use of a simple sampling methodology, and provides the most suitable data for EKGmon, which is 60 samples a second. Additionally, I have adjusted the baseline in some cases to better fit the grid. With this in mind, if you need the most accurate data possible, please visit Physionet.org.
Who is providing the analysis for the EKGs shown here?
Patient encounters shown here were diagnosed by the original personnel performing each EKG study. For example, the MGH/MF Waveform Database, which provides many of the encounters here, is the result of a collaboration between physicians, biomedical engineers and nurses at the Massachusetts General Hospital. I do create the multiple choice questions and explanations myself, based on the information provided by the studies. See my qualifications below, and always consult a licensed physician for professional advice and information.
What are your qualifications?
I previously worked as a Cardiac Monitor Technician at Lancaster General Hospital (LGH), and also have a strong IT and Cyber Security background. I love to solve problems, come up with creative ideas, and participate in the educational process. I do not hold any medical licenses. Any information provided through this site should be considered educational in nature! Please consult a licensed physician for professional advice and information!
Who else is involved with EKGmon?
Throughout the process of developing EKGmon, multiple individuals have contributed towards the project. I would like to thank the following people for their generous assistance and passion for medical training:
Breanna Gruver, BS, RCIS
Pennsylvania College of Health Sciences
Andrew Castagna, Student Doctor
West Virginia School of Osteopathic Medicine
Jennifer Smiley, Monitor Tech
Penn Medicine Lancaster General Health
How can I submit comments or feedback?
For all comments and feedback, please find my contact info at the bottom of the page.