Transforms
========================

This section covers various signal transformation techniques provided by the VitalDSP library. These transformations are crucial for analyzing signals in different domains, such as time-frequency analysis and feature extraction.

Chroma STFT
-----------
Short-time Fourier transform (STFT) applied to chroma features, which is useful for analyzing pitch content in signals like audio.

.. automodule:: vitalDSP.transforms.chroma_stft
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DCT Wavelet Fusion
------------------
Combining Discrete Cosine Transform (DCT) with wavelet transforms to extract meaningful features from signals, particularly in multi-resolution analysis.

.. automodule:: vitalDSP.transforms.dct_wavelet_fusion
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Discrete Cosine Transform
-------------------------
The Discrete Cosine Transform (DCT) is used to convert signals into a sum of cosine functions at different frequencies. This is particularly useful in signal compression and feature extraction.

.. automodule:: vitalDSP.transforms.discrete_cosine_transform
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Event Related Potential
-----------------------
Analysis of Event-Related Potentials (ERP) in signals, which are measured brain responses triggered by specific stimuli, useful in neuroscience research.

.. automodule:: vitalDSP.transforms.event_related_potential
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Fourier Transform
-----------------
The Fourier Transform decomposes signals into their constituent frequencies, making it a powerful tool for frequency-domain analysis.

.. automodule:: vitalDSP.transforms.fourier_transform
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Hilbert Transform
-----------------
The Hilbert Transform is used to derive the analytical signal, useful for envelope detection and instantaneous frequency analysis.

.. automodule:: vitalDSP.transforms.hilbert_transform
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MFCC (Mel Frequency Cepstral Coefficients)
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MFCC is widely used in audio signal processing for feature extraction, particularly in speech recognition systems.

.. automodule:: vitalDSP.transforms.mfcc
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PCA and ICA Signal Decomposition
--------------------------------
Principal Component Analysis (PCA) and Independent Component Analysis (ICA) are used for signal decomposition, particularly in separating mixed signals into independent sources.

.. automodule:: vitalDSP.transforms.pca_ica_signal_decomposition
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STFT (Short-Time Fourier Transform)
-----------------------------------
The Short-Time Fourier Transform is used for analyzing signals whose frequency content changes over time, providing a time-frequency representation of the signal.

.. automodule:: vitalDSP.transforms.stft
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Time-Frequency Representation
-----------------------------
This module covers various methods for representing signals in both time and frequency domains simultaneously.

.. automodule:: vitalDSP.transforms.time_freq_representation
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Vital Transformation
-----------------------------
This module covers perform comprehensive signal processing on ECG and PPG signals using advanced filtering and artifact removal techniques.

A series of transformations aimed at enhancing the quality of ECG and PPG signals by eliminating noise, detrending, normalizing, and enhancing critical points for easier detection. Each transformation step is modular and customizable.

.. automodule:: vitalDSP.transforms.vital_transformation
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Wavelet FFT Fusion
------------------
Combining Wavelet Transform with FFT (Fast Fourier Transform) to exploit the strengths of both in analyzing different aspects of the signal.

.. automodule:: vitalDSP.transforms.wavelet_fft_fusion
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Wavelet Transform
-----------------
Wavelet Transform is a powerful tool for multi-resolution analysis of signals, offering both time and frequency localization.

.. automodule:: vitalDSP.transforms.wavelet_transform
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