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).

Highlighted Topics

Descriptive Statistics
Peaks, RMS level, peak-to-peak amplitude, crest factor, dynamic time warping, CUSUM control chart, edit distance
Pulse and Transition Metrics
Rise time, fall time, slew rate, overshoot, undershoot, settling time, pulse width, duty cycle
Spectral Measurements
Channel power, bandwidth, mean frequency, median frequency, harmonic distortion

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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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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