This simulation illustrates, in simplified form, how ECG-classification AI systems trained on large annotated datasets such as PTB-XL learn to map raw waveform features, like QRS width, PR interval and ST-segment level, onto rhythm and morphology categories. It applies real standard ECG interval thresholds rather than invented numbers.
Each slider changes one measurable ECG feature and the canvas redraws a stylized (not photorealistic) ECG waveform live: heart rate changes beat spacing, QRS duration widens or narrows the QRS complex, PR interval changes the gap between the P wave and the QRS complex, and ST elevation shifts the ST segment up or down. A rule-based readout below checks each value against real clinical thresholds and reports which, if any, are crossed.
Heart-rate slider, QRS-duration slider, PR-interval slider, and ST-segment-elevation slider.
Did you know that PTB-XL, a public dataset of over 21,000 clinical 12-lead ECGs with expert cardiologist multi-label annotations, was one of the datasets that made it practical to train deep learning models directly on raw ECG waveform signals rather than hand-engineered features? Automated ECG triage built on datasets like this is designed to augment, not replace, cardiologist over-reading.
This simulation illustrates, in simplified form, how ECG-classification AI systems trained on large annotated datasets such as PTB-XL learn to map raw waveform features, heart rate, QRS duration, PR interval and ST-segment level, onto rhythm and morphology categories, then applies real standard ECG interval thresholds to produce a rule-based classification readout.
This simulation illustrates, in simplified form, how ECG-classification AI systems trained on large annotated datasets such as PTB-XL learn to map raw waveform features, heart rate, QRS duration, PR interval and ST-segment level, onto rhythm and morphology categories, then applies real standard ECG interval thresholds to produce a rule-based classification readout.
Drag the heart-rate, QRS-duration, PR-interval and ST-elevation sliders and watch the stylized ECG waveform redraw live, the QRS complex widening, the PR gap lengthening, or the ST segment lifting off the baseline. Below the trace, a rule-based readout checks each value against real clinical thresholds and reports Normal sinus rhythm, suspected bundle branch block, suspected AV block or suspected STEMI pattern, sometimes flagging more than one at once.
Did you know that PTB-XL, a public dataset of over 21,000 clinical 12-lead ECGs with expert cardiologist multi-label annotations, was one of the datasets that made it practical to train deep learning models directly on raw ECG waveform signals rather than hand-engineered features? Automated ECG triage built on datasets like this is designed to augment, not replace, cardiologist over-reading.