Update README.md

Author: gaurav12301010

README.md

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-# template
-A Template Repository for OpenSpringFest (OSF)
+# ECG Arrhythmia Classification using DL Models
+
+This project implements a **deep learning model using LSTM networks** to classify ECG heartbeats from the **MIT-BIH Arrhythmia Database** into different arrhythmia categories.
+
+---
+
+## Overview
+
+- **Goal:** Automatic classification of ECG heartbeats into arrhythmia types.  
+- **Model:** Stacked LSTM architecture for multi-class classification.  
+- **Processing:** Automated ECG preprocessing — filtering, R-peak detection, segmentation, normalization.  
+- **Evaluation:** Includes metrics and visualizations.  
+- **Accuracy:** Achieves around **98% test accuracy** on MIT-BIH dataset.
+
+---
+
+## Dataset
+
+**MIT-BIH Arrhythmia Database (PhysioNet)**  
+- 48 half-hour two-channel ECG recordings  
+- Sampling Rate: 360 Hz  
+- Expert-annotated beats  
+
+**Classes:**
+- N — Normal beats  
+- S — Supraventricular ectopic beats  
+- V — Ventricular ectopic beats  
+- F — Fusion beats  
+- Q — Unknown/Other beats  
+
+---
+
+## Model Architecture
+```
+Input (250, 1)
+↓
+LSTM(128, return_sequences=True)
+↓
+Dropout + BatchNormalization
+↓
+LSTM(64, return_sequences=True)
+↓
+Dropout + BatchNormalization
+↓
+LSTM(32)
+↓
+Dropout + BatchNormalization
+↓
+Dense(64, ReLU) → Dense(32, ReLU)
+↓
+Dense(num_classes, Softmax)
+```
+
+**Training Details:**
+- Optimizer: Adam (lr = 0.001)  
+- Loss: Categorical Crossentropy  
+- Batch Size: 128  
+- Max Epochs: 100  
+- Early Stopping and Learning Rate Scheduling enabled  
+
+---
+
+## Performance
+
+| Metric | Value |
+|--------|--------|
+| Accuracy | ~98% |
+| Precision | ~97% |
+| Recall | ~96% |
+| F1-Score | ~97% |
+
+**Generated Visualizations:**
+- Training history (accuracy/loss)
+- Confusion matrices
+- Sample predictions with confidence scores
+- Raw vs filtered signal comparisons
+
+---
+
+## Workflow
+
+1. **Preprocessing** — Run:
+   ```bash
+   python ecg_preprocessing.ipynb
+
+   ```
+   - Downloads MIT-BIH data
+   - Filters noise
+   - Segments beats and normalizes them
+  
+2. **Training** — Run:
+
+   ```python
+    lstm_model.ipynb
+
+   ```
+  - Trains the LSTM model
+  - Saves best and final model checkpoints
+    
+3. **Prediction Example:**
+   ```python
+   from tensorflow import keras
+   import numpy as np
+    
+   model = keras.models.load_model('best_lstm_model.keras')
+   data = np.load('ecg_mitdb_processed.npz')
+   preds = model.predict(data['X'][:10])
+   classes = np.argmax(preds, axis=1)
+   ```
+
+   ## Project Structure
+    ``` ecg-arrhythmia-classification/
+    ├── ecg_preprocessing.ipynb
+    ├── lstm_model.py
+    ├── requirements.txt
+    ├── README.md
+    ├── data
+       ├── ecg_mitdb_processed.npz
+       ├── mitdb
+    ├── final_lstm_model.keras
+    ├── best_lstm_model.keras
+    ├── evaluation_results.json
+    └── *.png (visualizations)
+    ```
+    ## Requirements
+   ```
+   numpy>=1.21.0
+   matplotlib>=3.4.0
+   scipy>=1.7.0
+   scikit-learn>=0.24.0
+   tensorflow>=2.8.0
+   wfdb>=4.0.0
+   seaborn>=0.11.0
+   tqdm>=4.62.0
+   ```
+   
+
+
+   
+
+
+
+