Watch one of the following videos on EKG rhythms or Chest X-ray interpretation: Skillstat https://www.skillstat.com/tools/ecg-simulator/ Here is another EKG interpretation website – it’s free, but you need to register.

Common Techniques and Testing that can be prescribed by the Nurse Practitioner

For this discussion, I clicked on the link https://www.skillstat.com/tools/ecg-simulator/. The link directed me to a video of a Six Second ECG Simulator, which generates 27 of the most common cardiac rhythms for one to explore and identify. There were names of different rhythms that I could click on, and the rhythms were displayed. By clicking on different rhythms, I learned how to identify most of them. First, I realized that the heart rates varied based on the rhythm I clicked. For instance, the heart rate for sinus rhythm was 72. Sinus bradycardia was associated with a low heart rate of 54, while sinus tachycardia was associated with a high heart rate of 138. However, the highest heart rate was witnessed in supraventricular tachycardia at 180. From this, I learned that tachycardia is associated with a fast heart rate, while bradycardia is associated with a slow heart rate.

Moreover, the ECG waves varied for different rhythms. I learned that a regular sinus rhythm is associated with a narrow QRS and upright P waves in Lead II. Even though other rhythms like sinus bradycardia and sinus tachycardia have normal shapes of ECG waves, the rates at which the waves appear are less than 60 beats/minute and higher than 100 beats/minute, respectively. This is expected as the normal heart rate is between 60 and 100 beats/minute (Rajalakshmi & Madhav, 2019). When I tried to check for waves in atrial fibrillation, I learned that it is associated with irregular rhythms with the absence of P waves. A rhythm such as atrial flutter can be identified by a sawtooth baseline of waves. Normal sinus rhythm with premature atrial complexes can be identified by narrow QRS and flattened, peaked, or biphasic P waves. Therefore, I can say that identifying rhythms can be done by checking the rates at which waves appear and the shapes of the waves. Every rhythm is associated with a certain pattern of waves that can be used for identification. With experience, I believe it will be easier to identify the rhythms.

Reference

Rajalakshmi, S., & Madhav, K. V. (2019). A collaborative prediction of the presence of Arrhythmia in the human heart with electrocardiogram data using machine learning algorithms with analytics. Journal of Computer Science, 15(2), 278–287. https://doi.org/10.3844/jcssp.2019.278.287