ECG Arrhythmia Detection using Deep Learning

Project Overview

This project implements advanced deep learning techniques (LSTM, CNN and AlexNet) for detecting heart arrhythmias using the St. Petersburg INCART 12-lead Arrhythmia Database. The goal is to develop robust machine learning models that can accurately identify abnormal heart rhythms from ECG signals.

Dataset

• Source: St. Petersburg INCART 12-lead Arrhythmia Database
• Characteristics:
  • 75 subjects with 30-minute ECG recordings
  • Sampled at 257 Hz
  • Annotated by medical experts for arrhythmia detection

Key Features

Data Preprocessing

• Custom data extraction function to segment ECG signals
• Feature extraction using Short-Time Fourier Transform (STFT)
• Handling class imbalance with custom loss function

Model Architectures

The project implements three different neural network architectures:

1. Bidirectional LSTM

   • Captures temporal dependencies in ECG signals
   • Processes sequential data with bidirectional context

2. 1D Convolutional Neural Network (CNN)

   • Extracts spatial patterns from ECG signals
   • Uses convolutional and pooling layers for feature detection

3. AlexNet-Inspired Architecture

   • Adapted from the original AlexNet image classification model
   • Customized for 1D ECG signal processing

Key Techniques

• Custom Binary Cross-Entropy (BCE) loss function
• Handling class imbalance
• Data visualization of ECG signals
• Performance evaluation using accuracy and F1 score

Dependencies

• Python 3.x
• PyTorch
• NumPy
• Pandas
• Matplotlib
• SciPy
• Scikit-learn
• WFDB (Waveform Database Software Package)

Results

The project compares the performance of different neural network architectures:

• Bidirectional LSTM
• 1D Convolutional Neural Network
• AlexNet-inspired Architecture

Each model is evaluated using:

• Training accuracy
• Test accuracy
• F1 score

Key Insights

• The dataset shows a significant class imbalance (only 13.3% abnormal samples)
• Custom loss function helps address class imbalance
• CNN outperformed LSTM in this specific classification task

Future Work

• Experiment with more advanced architectures
• Incorporate additional feature extraction techniques
• Collect more diverse ECG data

Acknowledgments

• St. Petersburg INCART for providing the ECG database
• PyTorch and scientific computing community