# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Application of IMF-Based Features Obtained through EMD  
Intrinsic mode functions (IMFs) derived from empirical mode decomposition (EMD) have been successfully applied in various studies to extract meaningful features from physiological signals for health parameter estimation. For instance, IMF-based features have been utilized for stress level and cognitive load detection from PPG signals [22]. Similarly, in the neurophysiology domain, features such as Shannon's entropy, spectral entropy, standard deviation, skewness, and kurtosis extracted from IMFs have proven effective in diagnosing sleep disorders through electroencephalogram (EEG) signal analysis [23]. Furthermore, energy features derived from IMFs have been utilized in heart rate variability studies for stress detection [24]. These findings highlight the potential of IMF-based features obtained from EMD in capturing relevant information from physiological signals, including PPG, for the estimation of various health parameters. As IMF-based features have not previously been used specifically for blood glucose estimation, we intended to explore their potential for estimating blood glucose levels from PPG signals, given their demonstrated applications in related biomedical fields.  
Comparative Analysis between Wavelet Transform-Based Features and EMD-Based Features for Blood Glucose Estimation from PPG Signals  
Both EMD and wavelet transform are suitable techniques for analyzing nonlinear and nonstationary signals. While EMD adaptively decomposes a signal into its intrinsic oscillatory modes, the wavelet transform decomposes the signal into components in various frequency bands. EMD is completely data-driven and does not require a predefined basis function, whereas the wavelet transform is more mathematically defined and allows for multi-resolution analysis. A recent study [25] has gathered various noninvasive technologies for blood glucose measurement, highlighting a range of features that can be used effectively for glucose level estimation. The study demonstrated the extraction of features from PPG signals shown in different research and discussed the application of machine learning techniques to predict blood glucose levels. A detailed description about wavelet transform-based feature wavelet entropy can also be found in [25]. The wavelet transform provides detailed insights into PPG signal features by decomposing it into wavelet coefficients providing multi-resolution analysis. On the other hand, the EMD analysis proposed in this study is an adaptive method that decomposes the signal into intrinsic mode functions that represent simple oscillations. It is especially effective for nonlinear, nonstationary data, working directly with the data without a fixed basis. The key advantage of EMD over wavelet transform lies in its adaptability and direct extraction of data-driven features. Another study [26] utilized wavelet transform for denoising and removal of baseline drift. EMD can also be an alternative to signal preprocessing for noise reduction by selectively excluding high- and low-frequency noise from the IMF representation.