Neural Networks for Heart Disease Detection via ECG
Nidhi Inamdar

Neural Networks for Heart Disease Detection via ECG

Challenges

Developing an AI-driven ECG analysis system came with multiple challenges, primarily related to data processing, model accuracy, and real-world application. Handling large volumes of ECG recordings while ensuring reliable predictions required overcoming several key obstacles:

  • Massive Data Processing – The dataset consisted of 827 ECG recordings, each with 4096 samples across 12 leads. Efficiently managing, storing, and analyzing such high-dimensional data was crucial.
  • Feature Extraction Complexity – Identifying key ECG characteristics like heart rate, PR interval, QRS complex, and ST interval required advanced signal processing techniques to ensure meaningful data input for the AI model.
  • Neural Network Optimization – Designing a deep learning architecture that could accurately detect complex cardiac patterns while avoiding overfitting was a major challenge.
  • Model Generalization – Ensuring that the AI model worked across different patient demographics and medical conditions was critical to prevent biases in detection.
  • Minimizing False Positives & False Negatives – The model needed to reduce errors in classification, as incorrect diagnoses could lead to unnecessary panic or missed health risks.
  • Real-World Integration – Making the AI system compatible with clinical workflows and Electronic Health Record (EHR) systems was essential for practical medical use.

By tackling these challenges, the project aimed to create a highly accurate, scalable, and clinically useful AI-driven ECG analysis system that could aid in the early detection and intervention of cardiac diseases.

Solutions

To overcome these challenges, we developed a robust AI-powered ECG analysis system that efficiently processes ECG data, identifies heart anomalies, and enhances early detection accuracy. Our solutions included:

1. Efficient Data Processing and Feature Extraction

We implemented advanced signal processing techniques to extract key ECG features such as heart rate, PR interval, QRS complex, and ST interval. By leveraging NumPy and SciPy, we ensured smooth data handling and preprocessing, making the dataset more structured for model training.

2. Custom Neural Network Architecture

A deep learning model was designed using TensorFlow and PyTorch to analyze ECG waveforms and detect abnormalities. The architecture was optimized with hyperparameter tuning, ensuring accurate and reliable predictions while reducing overfitting.

3. Data Augmentation & Model Generalization

We applied data augmentation techniques such as synthetic ECG generation, noise addition, and signal transformation to improve the model's robustness across different patient demographics. This helped the model adapt to real-world variations in ECG data.

4. Reduction of False Positives & False Negatives

By fine-tuning the model with confusion matrix analysis, we minimized errors in classification, ensuring high sensitivity and specificity in detecting cardiac diseases. This resulted in fewer misdiagnoses and improved reliability in medical decision-making.

5. Seamless Integration with Healthcare Systems

The system was designed to integrate with Electronic Health Records (EHRs) and clinical workflows to ensure real-world usability. A user-friendly dashboard with real-time monitoring was also developed, allowing doctors to access AI-powered ECG reports instantly.

By implementing these solutions, we built an intelligent and scalable AI-driven ECG analysis platform that enhances early cardiac disease detection, improves diagnosis accuracy, and reduces healthcare professionals' workload.