You will get ECG and PCG classification system
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Project details
I’m proposing an end-to-end ECG & PCG classification solution wrapped in a user-friendly Django web app. The backend handles signal ingestion (file upload or live stream), robust preprocessing (baseline-wander removal, band-pass filtering, denoising), and segmentation (R-peaks for ECG; S1/S2 heart-sound segmentation for PCG). Features are extracted (time, frequency, time-frequency) or learned via compact 1D CNN/Transformer models and log-mel/spectrogram pipelines, then classified for tasks like arrhythmia and murmur detection with calibrated confidence. The web interface provides secure logins and roles, interactive plots of waveforms/spectrograms, result explanations (saliency/feature attributions), batch processing, and exports (PDF/CSV). Admin tools cover dataset management, model/version switching, and audit logs; APIs enable integration with hospital or research systems. Everything ships documented, tested, and containerized, ready for on-prem or cloud deployment.
What's included
| Service Tiers |
Starter
$100
|
Standard
$250
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 1 day | 2 days | 7 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 4 |
Number of Graphs/Charts | 1 | 4 | 8 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
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About Rachid
Signal processing, Biomedical, Radar, AI, DL, Python, MATLAB with PhD
100%
Job Success
Saida, Algeria - 5:00 pm local time
- Signal processing projects;
- Artificial intelligence;
-Machine learning;
- Deep learning;
- Teaching;
- Telecommunications;
- Python and matlab programming;
- Radars;
- Academic documents writing.
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