Abstract

The neural network projects focuses on enhancing electrocardiogram (ECG) classification using a convolutional neural network (CNN) optimized by a differential evolution algorithm. The objective is to improve the accuracy and efficiency of detecting and classifying various heart conditions from ECG signals. Differential evolution will optimize CNN hyperparameters, such as the number of layers, learning rate, and filter sizes, to achieve superior performance in terms of classification accuracy and computational efficiency.

Existing System

Traditional ECG classification models rely on handcrafted features and static classifiers, which may not generalize well to diverse datasets or adapt efficiently to changing data distributions. They often lack the optimization techniques required to extract the most relevant features for accurate classification.

Proposed System

The proposed system integrates CNNs for feature extraction and classification with a differential evolution algorithm to optimize the model’s hyperparameters. This hybrid approach combines the powerful feature extraction capabilities of CNNs with the global optimization potential of differential evolution to enhance classification performance.

Software and Hardware Requirements

Software:

• Programming Languages: Python
• Libraries/Frameworks: TensorFlow or PyTorch (for CNN implementation), SciPy (for differential evolution), NumPy, and Matplotlib for data processing and visualization.
• Development Environment: Jupyter Notebook or PyCharm
• Data Handling: Use of MIT-BIH Arrhythmia Dataset or similar ECG datasets.

Hardware:

• GPU: NVIDIA GPUs (e.g., RTX 3060 or higher) for training CNN models.
• CPU: Multi-core processors for differential evolution optimization tasks.
• RAM: At least 16 GB for handling large datasets and model computations.
• Storage: Sufficient storage for dataset and model checkpoints (SSD recommended).

Module Description

1. Data Preprocessing Module: Filters and normalizes ECG signals, segment the data, and prepares it for input into the CNN.
2. CNN Module: Extracts features from ECG signals and classifies them into predefined categories.
3. Optimization Module: Utilizes differential evolution to optimize CNN hyperparameters, such as the number of convolutional layers, filter sizes, and learning rate.
4. Evaluation Module: Validates the model using metrics such as accuracy, precision, recall, and F1-score.
5. User Interface Module: Provides visualizations of ECG signal classifications and performance metrics.

Functional Requirements

• Accurate Classification: High precision in identifying and classifying heart conditions from ECG data.
• Dynamic Optimization: Real-time optimization of CNN parameters to adapt to dataset variations.
• Data Compatibility: Support for standard ECG datasets and customizable inputs.

Non-Functional Requirements

• Efficiency: Minimized computational overhead during training and inference.
• Scalability: Capability to handle larger datasets and more complex CNN architectures.
• Reliability: Consistent classification results across varying input data.
• Maintainability: Easy integration with new datasets or updated optimization algorithms.