Welcome to the ECG Waveform Analysis Challenge! This interactive game offers a unique opportunity to test and enhance your skills in identifying abnormal electrocardiogram (ECG) patterns. In this challenge, you're not just playing alone; you're competing against our advanced robot algorithm designed to analyze ECG waveforms.
As the waveforms scroll across your screen, your task is to identify and tag abnormal segments, such as irregular QRS complexes, missing P waves, or absent T waves. Be cautious, though – tagging normal segments will affect your score negatively. As the game progresses, the waveforms will move faster, increasing the difficulty. Keep track of your score and see how well you can do against our robot!
But that's not all – this challenge also provides a unique learning opportunity. By comparing your scores with the robot's analysis, you gain insights into the complexities of ECG interpretation. The robot uses a sophisticated fuzzy logic algorithm to evaluate each waveform, offering a high-tech benchmark for your own assessments.
Whether you're a medical professional, a student, or simply someone interested in biomedical engineering, this game is designed to test your skills and expand your understanding of ECG analysis. So, are you ready to take on the challenge and see how you stack up against our robot? Let the game begin!
This interactive game is designed to test your skills in identifying abnormal ECG patterns. In this challenge, you will be competing against a robot algorithm. This robot, programmed in Python, analyzes ECG waveforms using fuzzy logic but does not have prior knowledge of waveform labels. This means it operates solely based on the data it analyzes in real-time, just like a human player.
Your task is to observe the scrolling ECG waveforms, generated in JavaScript, and identify abnormal segments. The complexity lies in distinguishing between normal and abnormal patterns, such as variations in the QRS complex or the absence of specific waves. Clicking correctly on an abnormal segment increases your score, but incorrect clicks on normal segments will reduce it. The game's pace accelerates as you score more points, adding to the challenge.
The ECG waveforms you see on your screen are dynamically generated using JavaScript. This part of the code is responsible for creating both normal and abnormal ECG patterns, but it does not label them. Hence, when you and the robot algorithm analyze these waveforms, you're both starting from the same base of knowledge.
The robot's decision-making process is handled by a Python-based fuzzy logic system. This system evaluates the waveforms based on several criteria, such as frequency, amplitude, and peak patterns, to determine whether a segment is normal or abnormal. However, it doesn't receive any direct information or labels about the waveforms; its judgments are made solely based on its programmed analysis parameters.
This setup ensures a fair comparison between your human analytical skills and the robot's programmed logic. It offers an engaging way to understand ECG interpretation and appreciate the application of fuzzy logic in biomedical analysis.