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PhysioFeat is a collection of feature extraction methods. These signal processing methods can be applied on non-stationary physiological signals such as electromyography (EMG), electrocardiography (ECG), heart rate variability (HRV), respiration and electroencephalography (EEG).

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PhysioFeat

PhysioFeat is a collection of feature extraction methods. These signal processing methods can be applied on non-stationary physiological signals such as electromyography (EMG), electrocardiography (ECG) and electroencephalography (EEG).

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[HRV] = getclassicalHRV(phi_eIntervals) Calculates classical Heart Rate Variabiliry (HRV) measures: SDNN, Vagal Trend. Vagal Tone, mean value of RR and mean value of HR RMSSD, NN50, pNN50 input: Intervals between beats (RR intervals)

[VLF, LF, HF, LFHFratio, nLF, nHF] = getFreqFeaturesHRV(f,PSD)
Calculates very low, low and high frequency components of Heart Rate and LF/HF ratio inputs PSD: power spectrum density f: frequency values which used to calculate power spectrum

feat = getrecewpcdb5feat(x,winsize,wininc,datawin,dispstatus) Calculates energy of wavelet packets of non-stationary signals (Electromyography - EMG) x is data/signal datawin = hamming(winsize);

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PhysioFeat is a collection of feature extraction methods. These signal processing methods can be applied on non-stationary physiological signals such as electromyography (EMG), electrocardiography (ECG), heart rate variability (HRV), respiration and electroencephalography (EEG).

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