Measurements and Feature Extraction

Peaks, signal statistics, pulse and transition metrics, power, bandwidth, distortion

Signal Processing Toolbox™ provides functions that let you measure common distinctive features of a signal. Locate signal peaks and determine their height, width, and distance to neighbors. Measure time-domain features such as peak-to-peak amplitudes and signal envelopes. Measure pulse metrics such as overshoot and duty cycle.

In the frequency domain, measure fundamental, mean, median, and harmonic frequencies, channel bandwidth, and power in a frequency band. Characterize systems by measuring spurious free dynamic range (SFDR), signal-to-noise ratio (SNR), total harmonic distortion (THD), signal to noise and distortion ratio (SINAD), and third-order intercept (TOI).

Extract signal features to enhance key characteristics and reduce data variability and dimensionality when training AI models.

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AI for Signals

Featured Examples

Classify ECG Signals Using Long Short-Term Memory Networks

Classify heartbeat electrocardiogram data using deep learning and signal processing.

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Human Activity Recognition Using Signal Feature Extraction and Machine Learning

Extract features from smartphone sensor signals and use them to classify human activity.

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Waveform Segmentation Using Deep Learning

Segment human electrocardiogram signals using time-frequency analysis and deep learning.

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Distortion Measurements

Generate 2048 samples of a sinusoid of frequency 2.5 kHz sampled at 50 kHz. Add white Gaussian noise such that the signal-to-noise ratio (SNR) is 80 dB.

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Determine Peak Widths

Create a signal that consists of a sum of bell curves. Specify the location, height, and width of each curve.

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