ECG Monitor & Analysis

Overview

This project provides a Python-based toolkit for real-time monitoring, processing, and analysis of ECG (Electrocardiogram) signals. It supports analog-to-digital conversion, beat detection, and advanced ST segment analysis, with both interactive CLI and automated batch modes. Users can process data from the AD8232 sensor, run simulations with built-in or custom test patterns, and analyze results for research, prototyping, or educational purposes in biomedical signal processing.

Features

• Analog to Digital Signal Processing: Convert raw ECG signals to digital format.
• Beat Detection & Processing: Identify and process heartbeats from ECG data.
• ST Segment Analysis: Analyze the ST segment for abnormalities.
• Data Visualization: Results can be used for personal data visualization and utilize CWT (Continuous Wavelet Transform) with the Morlet wavelet for advanced signal analysis.
• Test Scripts: Run quick tests to validate functionality.

Input Data

• Sensor: This project is designed to work exclusively with ECG data collected from the AD8232 sensor.
• Sampling Rate: The system processes samples at a default rate of 250Hz.
• Format: Input data should be in the expected digital format as output by the AD8232 sensor.

Data & Results

• Input Data: The project expects ECG signal data from the AD8232 sensor at 250Hz.
• Processing: The scripts process the ECG data to detect beats, analyze ST segments, and may output processed signals or diagnostic information.
• Results: Outputs include processed ECG signals, beat locations, and ST segment analysis results, which can be used for further research or visualization.
• Sample Output:

  Beat 1: {
    'fiducial_points': {
      'p_peak': np.int64(27),
      'qrs_onset': 39,
      'r_peak': np.int64(51),
      'j_point': 63,
      'qrs_offset': 63,
      't_peak': np.int64(81),
      'qrs_energy': array([...]),
      'beat_number': 0,
      'start_idx': np.int64(16)
    },
    'baseline': np.float64(-5.46),
    'baseline_method': 'PR',
    'all_baselines': [('PR', np.float64(-5.46)), ...],
    'st_measurements': {'st_20ms': np.float64(24.12), ...},
    'st_indices': {'st_20ms': 66, ...},
    'morphology': {'st_slope': np.float64(282.21), ...},
    'st_segment': array([...]),
    'st_bounds': (63, np.int64(75)),
    'abnormalities': [
      {'type': 'ST_ELEVATION', 'measurement': 'st_20ms', 'value': np.float64(24.12), 'severity': 'severe'},
      ...
    ],
    'j_point_quality': 0.5
  }

Setup

1. Clone the repository

   git clone <repo-url>
   cd monitor_ecg

2. Create and activate a virtual environment (optional but recommended)

   python3 -m venv venv
   source venv/bin/activate

3. Install dependencies

   pip install -r requirements.txt

Quick Test / Usage

You can run the test script to generate test sensor results:

python test.py

Or run the main module in batch mode for a quick start (processes 10 seconds of data):

python main.py --mode batch --duration 10

Or run individual modules, for example:

python main.py

Usage Modes

• CLI Mode (Interactive):

  • Start the program in interactive mode:

    python main.py --mode cli

  • At the prompt, use commands like load, start, simulate, status, analyze, etc.
  • You can load built-in test patterns or input your own ECG data as comma-separated values.
  • Custom Data Input (CLI):
    • In CLI mode, use the load command and choose manual to paste your own ECG data as comma-separated values.

• Batch Mode (Automated):

  • Run the program for a fixed duration with automatic test pattern loading and simulation:

    python main.py --mode batch --duration 15

  • The program will process and analyze ECG data for the specified duration, then print results.

Example CLI Workflow

$ python main.py --mode cli

ECG> load
Enter 'auto' to use ecg_gen patterns or 'manual' to input custom data: manual
Paste your data: 500,502,501,499,498,500,503,504,502,500

ECG> start
ECG> simulate
ECG> status
ECG> analyze
Enter start time (seconds): 0
Enter end time (seconds): 10
...
ECG> results
ECG> stop
ECG> exit

File Descriptions

• main.py: Main entry point for ECG processing.
• analog_digital_signal.py: Handles analog-to-digital conversion of ECG signals.
• prcocess_beat.py: Processes and detects heartbeats in ECG data.
• st_analysis.py: Analyzes the ST segment of the ECG.
• test.py: Script for running tests and demonstrations.
• requirements.txt: Python dependencies.

Notes

• Ensure your input ECG data is in the correct format expected by the scripts (AD8232, 250Hz).
• Modify or extend the scripts as needed for your specific use case.

License

Tag the repo and notify on usage for any commercial use.