Cardio EEG Classification in Python Projects

Cardio EEG Classification in Python Projects
Abstract
Cardiac and neurological disorders can be detected early by analyzing physiological signals such as Electroencephalogram (EEG) and Electrocardiogram (ECG). Traditional clinical diagnosis of such signals is manual and time-consuming. This project presents a Cardio EEG Classification System using Machine Learning and Deep Learning in Python. The system classifies EEG and ECG signal patterns to detect abnormalities related to heart and brain activities. Using preprocessing techniques like noise removal and feature extraction, the model learns complex signal characteristics. Algorithms such as Convolutional Neural Networks (CNN), LSTM, and Random Forest are used for classification, and Python libraries like NumPy, SciPy, TensorFlow, MNE, and Matplotlib are utilized. The system helps in identifying neurocardiac disorders like arrhythmia-related brain patterns and improves diagnostic support using automated classification.

Existing System
In the existing system, EEG and ECG signals are analyzed manually by medical specialists, which is a slow and error-prone process. Traditional systems use basic digital filters and statistical feature extraction, but they fail to provide accurate classification for complex biomedical signals due to noise, artifacts, and non-linear characteristics. Existing classification methods are not efficient for large-scale data and often struggle with real-time performance. Additionally, current clinical software tools lack automation and advanced classification capabilities, leading to delayed diagnosis for patients with neurological and cardiac disorders.

Proposed System

The proposed system introduces an automated Cardio EEG Classification framework using advanced signal processing and deep learning techniques. The system preprocesses raw signals using Butterworth filtering and wavelet transform to eliminate motion and muscle artifacts. Feature extraction methods like Power Spectral Density (PSD), Hjorth parameters, and statistical measures are applied. The classification is performed using a hybrid model combining CNN and LSTM to capture both spatial and temporal dependencies in EEG and ECG signals. The system is trained on benchmark biomedical datasets such as PhysioNet and offers a user interface using Flask or Streamlit for easy interaction. This system improves classification accuracy, reduces human involvement, and provides faster disease detection support for healthcare applications.

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