Reshaping Data in NumPy Can Lead to Unforeseen Consequences
In the realm of machine learning, particularly in projects involving electrocardiograms (ECGs), it is crucial to handle time-series data with care. A recent study has highlighted the importance of preserving the temporal ordering when using NumPy's reshape method, as unintentional changes can significantly impact the physiological properties of the data.
For instance, the reshape method unintentionally altered an ECG, causing a dramatic increase in the measured heart rate by a factor of 12. This unexpected change was evident when comparing the original ECG with the reshaped version, as shown in Figure 7, which depicts lead I after applying the reshape method and appears strikingly different from the expected ECG.
However, when the same reshape method was applied to the same three numbers or to the ECG array in previous projects involving image data, no visible difference was observed in the plots.
To address this issue, it is recommended to follow these guidelines:
1. Preserve temporal ordering: Ensure that the reshaping does not reorder or scramble the time dimension unintentionally. 2. Use reshape carefully with the correct shape parameters: Always reshape based on known dimensions of the data samples and their time length. 3. Avoid flattening or merging time steps with features: Keep time and feature axes distinct. 4. Validate reshaped data: Verify that the time-series patterns remain consistent after reshaping. 5. Consider specialized data structures: Utilize frameworks like TensorFlow, PyTorch, or libraries such as pandas for complex time series. 6. Document assumptions and shapes clearly: Clearly document the input and output shapes in preprocessing code.
In the case of ECG data, reshaping was necessary to make the array fit the input shape of the 1D Convolutional Neural Network. However, applying the reshape method resulted in all leads becoming the same, as demonstrated in Figure 6. To overcome this problem, it is advisable to use numpy.moveaxis instead of reshaping the ECG array.
Figure 8 compares the flattened original ECG signal and the reshaped signal, showing that the reshape method resamples all original 12 leads and makes 12 copies.
This study serves as a reminder of the importance of handling time-series data like ECGs with care when using NumPy's reshape method in machine learning projects. By following these guidelines, researchers can help ensure the integrity of their data and avoid unintentional changes that could impact their findings.
[1] For more information on specific architecture adaptations for temporal data, literature on residual networks for time series or temporal convolutional networks might be helpful.
Data-and-cloud-computing technology plays a significant role in the preservation of temporal ordering when handling ECG data, allowing researchers to apply machine learning techniques effectively. To maintain the integrity of ECG data and avoid unintentional changes that may affect the findings, it is prudent to adhere to data-handling guidelines such as using specialized data structures, preserving temporal ordering, and verifying time-series patterns after reshaping.
