Denoising Raw ECG data which is in EDF format

Introduction:
Our project focuses on processing raw ECG signals recorded in European Data Format (EDF) to produce denoised, clear ECG data for further analysis. The primary goal is to reduce noise in the raw ECG signals and convert them into a clean, vectorized format saved as CSV. This will ensure easier interpretation and analysis of the ECG data for medical professionals and algorithm-based diagnostics.

Project Overview:
We are looking for an experienced developer to develop a solution for denoising raw ECG signals, specifically stored in the EDF format. The solution should be capable of reading EDF files, applying noise-reduction techniques, and outputting a high-quality, clear ECG signal in CSV format.

The developer will be responsible for the following:

EDF File Handling: Read and extract ECG signal data from EDF (European Data Format) files, which are widely used for storing physiological signals.
Noise Reduction: Implement denoising algorithms to clean the raw ECG signal and remove common noise artifacts such as baseline wander, muscle noise, and power line interference, while preserving the integrity of the ECG signal.
Output in CSV Format: Convert the denoised ECG signal into a CSV file format. Each CSV file should contain clean, vectorized data points that are easily interpretable for subsequent stages of analysis.
Comparative Signal Analysis: Ensure the output data is visually clear and shows noticeable improvements in signal quality compared to the original raw data.
Scope of Work:

Reading EDF Files:

Implement a robust method to load and extract ECG data from EDF files.
Ensure compatibility with various EDF files, focusing on ECG channels.
Noise Reduction Implementation:

Apply techniques such as digital filtering (e.g., low-pass, high-pass, band-pass filters), wavelet transforms, or adaptive filtering to reduce noise.
Address common noise types, including baseline drift, powerline interference (50/60 Hz), and muscle noise.
Preserve important signal features, such as QRS complexes, P-waves, and T-waves.
CSV Output:

Convert the denoised ECG signal into a CSV format, ensuring clear vector points with columns for time and corresponding ECG signal amplitude.
Ensure the CSV output format is consistent, labeled, and ready for further analysis or integration into machine learning algorithms.
Quality Assurance:

Compare the denoised output to the raw ECG signal to verify improvement.
Visualize and demonstrate the effectiveness of noise reduction through graphs or plots.
Required Skills:

Experience with signal processing techniques, especially for ECG signals.
Familiarity with EDF (European Data Format) and conversion processes.
Expertise in implementing noise-reduction algorithms using filters and other advanced methods.
Proficiency in Python, MATLAB, or similar tools for signal processing and data manipulation.
Ability to output processed data into CSV format with clear and interpretable vector points.

Deliverables:

Fully functional code to read and denoise ECG signals from EDF files.
Output ECG signal in CSV format with reduced noise.
Documentation of the process, including explanations of the denoising techniques used.
Comparison and visualization of raw and denoised ECG signals.
Timeline: We expect this project to be completed within [insert desired timeframe], with the initial implementation and testing of denoising algorithms to be completed in [insert milestone period].

Budget:
1500 - 2000 USD

Conclusion:
We seek a skilled developer with experience in ECG signal processing to help us denoise raw ECG signals and convert them into a clear, vectorized CSV format. This project will improve the quality of ECG data for further medical analysis and algorithmic processing.

Please provide relevant experience, your approach to this task, and your estimated time for completion.