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klara 2024-06-26 16:01:27 +02:00
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@ -114,14 +114,16 @@ Through this process, Emma was able to leverage our project to generate meaningf
## Progress ## Progress
- **Data was searched and found at : (https://doi.org/10.13026/wgex-er52, last visit: 15.05.2024)** - **Data was searched and found at : (https://doi.org/10.13026/wgex-er52, last visit: 15.05.2024)**
- **[Data was cleaned](#data-cleaning)** - **[Data was cleaned](#data-cleaning)** (version 12.06)
- **[Demographic data was plotted](#demographic-plots)** - **[Demographic data was plotted](#demographic-plots)** (version 12.06)
- **[Hypotheses put forward](#hypotheses)** - **[Hypotheses put forward](#hypotheses)** (version 12.06 & 03.07)
- **[Noise reduction](#noise-reduction)** - **[Noise reduction](#noise-reduction)** (version 12.06)
- **[Features](#features)** - **[Features](#features)**(version 12.06)
- **[ML-models](#ml-models)** - **[ML-models](#ml-models)** (version 12.06 & 03.07)
- **[Cluster analysis](#cluster-analysis)** - **[Cluster analysis](#cluster-analysis)** (version 03.07)
- **[Legal basis](#legal-basis) - **[Legal basis](#legal-basis)** (version 03.07)
- **[Conclusion](#conclusion)** (version 03.07)
- **[Outlook](#outlook)** (version 03.07)
### Data cleaning ### Data cleaning
@ -205,6 +207,7 @@ With those Classifiers, the hypothesis can be proven, that a classifier is able
### Noise reduction ### Noise reduction
(version 12.06) (version 12.06)
Noise suppression was performed on the existing ECG data. A three-stage noise reduction was performed to reduce the noise in the ECG signals. First, a Butterworth filter was applied to the signals to remove the high frequency noise. Then a Loess filter was applied to the signals to remove the low frequency noise. Finally, a non-local-means filter was applied to the signals to remove the remaining noise. For noise reduction, the built-in noise reduction function from NeuroKit2 `ecg_clean` was utilized for all data due to considerations of time performance. Noise suppression was performed on the existing ECG data. A three-stage noise reduction was performed to reduce the noise in the ECG signals. First, a Butterworth filter was applied to the signals to remove the high frequency noise. Then a Loess filter was applied to the signals to remove the low frequency noise. Finally, a non-local-means filter was applied to the signals to remove the remaining noise. For noise reduction, the built-in noise reduction function from NeuroKit2 `ecg_clean` was utilized for all data due to considerations of time performance.
How the noise reduction was performed in detail can be seen in the following notebook: [noise_reduction.ipynb](notebooks/noise_reduction.ipynb) How the noise reduction was performed in detail can be seen in the following notebook: [noise_reduction.ipynb](notebooks/noise_reduction.ipynb)
@ -255,7 +258,7 @@ For machine learning, the initial step involved tailoring the features for the m
- Improvement of diagnostics and treatment possible through predictive modeling - Improvement of diagnostics and treatment possible through predictive modeling
## Outlook into the future ## Outlook
(03.07) (03.07)
- In der Zukunft weitere Modelle anwenden und verbessern - In der Zukunft weitere Modelle anwenden und verbessern
- -