July 22, 2025
Day 41 - Simulating Real-World ECG Variability Through Noise Augmentation
What I Learned
Today marked a significant step forward in the robustness of our ECG classification project. I dedicated the majority of my time to implementing and testing various forms of synthetic noise to simulate real-world distortions in ECG signals. The goal was to enhance the generalization capabilities of our deep learning models by exposing them to more realistic, imperfect data.
I began by introducing Gaussian noise into the dataset, which mimics the random fluctuations often encountered in sensor readings due to electronic interference or environmental conditions. This type of noise helps the model learn to distinguish between signal and background fluctuations, making it more reliable when deployed in practical settings.
Next, I implemented baseline wander, a common low-frequency distortion typically caused by factors like patient movement or breathing. This artifact can shift the entire signal baseline up or down over time, posing a challenge for accurate detection of cardiac events. By simulating this condition, the model becomes better equipped to ignore irrelevant baseline shifts and focus on the important morphological features of the ECG.
Later in the day, I met with my professor to review the progress and discuss the implications of these noise additions. We examined how noise augmentation can serve as a form of regularization and potentially improve model resilience against unseen data. The meeting also helped clarify our next steps, which include evaluating model performance on noisy versus clean data and determining the best noise combinations for training.
Overall, today’s work was both technically challenging and intellectually rewarding. It brought us closer to building a more reliable and clinically applicable ECG diagnostic tool by replicating the complexities of real-world data.
Blockers
No blockers.
Reflection
Today’s internship experience was focused on an important aspect of our ECGNet project: preparing our dataset to better reflect real-world conditions. A key insight in deep learning for biomedical data is that models trained only on clean, ideal signals often fail when exposed to noisy, real-life data.
To address this, I spent the day implementing various forms of noise augmentation into our ECG dataset, including Gaussian noise, baseline wander, and muscle noise.
The first type of noise I introduced was Gaussian noise, which represents random fluctuations commonly seen in ECG signals due to sensor imperfections or background interference. Adding this noise helps our model learn how to filter out irrelevant spikes and better detect consistent patterns in the signal.
Next, I worked on simulating baseline wander, a slow drift in the ECG signal caused by factors like patient movement or breathing. This distortion can interfere with key features like the ST segment, so making our model robust to such artifacts is critical for accurate clinical interpretation.
Lastly, I added muscle noise, or electromyographic interference, which introduces high-frequency variations typically caused by voluntary or involuntary muscle contractions. This type of noise is especially common during ambulatory ECG recordings and can easily confuse a model if it hasn’t seen similar distortions during training.
Later in the day, I had a valuable meeting with my professor. We reviewed the goals of noise augmentation and discussed how these changes could lead to improved model generalization and robustness. The discussion helped me better understand not just the how, but the why behind these techniques — and how they align with our broader goal of building a clinically usable diagnostic model.
Reflecting on today, I realized how essential it is to simulate real-world variability when working with medical data. Clean data may perform well in lab settings, but our goal is to develop solutions that function reliably in unpredictable environments. Today’s work was a meaningful step toward that vision.