Extraction of 16 Time Domain and Frequency Domain Signal Features: Mean, Standard Deviation, Variance, and Key Statistical Indicators

In this study, we employ 16 distinct time domain and frequency domain indicators for comprehensive signal feature extraction. The implemented metrics include: mean (average signal value), standard deviation (signal variability measure), variance (signal power distribution), skewness indicator (asymmetry measurement), kurtosis indicator (peak sharpness assessment), peak-to-peak value (maximum amplitude range), peak value (maximum absolute amplitude), root mean square amplitude (energy content measure), average amplitude (mean absolute value), root amplitude (square root based magnitude), waveform indicator (shape characteristic), and peak indicator (crest factor analysis). These features enable thorough characterization of signal properties through numerical quantification. From a code implementation perspective, these metrics can be efficiently computed using libraries like NumPy or MATLAB, where functions such as mean(), std(), var(), skew(), kurtosis(), max(), min(), and custom RMS calculations provide the foundational building blocks for comprehensive signal analysis pipelines.