tools.py 文件源码

def feat_eog(signals):
    """
    calculate the EOG features
    :param signals: 1D or 2D signals
    """

    if signals.ndim == 1: signals = np.expand_dims(signals,0)
    sfreq = use_sfreq
    nsamp = float(signals.shape[1])
    w = (fft(signals,axis=1)).real   
    feats = np.zeros((signals.shape[0],15),dtype='float32')
    delta = np.sum(np.abs(w[:,np.arange(0.5*nsamp/sfreq,4*nsamp/sfreq, dtype=int)]),axis=1)
    theta = np.sum(np.abs(w[:,np.arange(4*nsamp/sfreq,8*nsamp/sfreq, dtype=int)]),axis=1)
    alpha = np.sum(np.abs(w[:,np.arange(8*nsamp/sfreq,13*nsamp/sfreq, dtype=int)]),axis=1)
    beta  = np.sum(np.abs(w[:,np.arange(13*nsamp/sfreq,20*nsamp/sfreq, dtype=int)]),axis=1)
    gamma = np.sum(np.abs(w[:,np.arange(20*nsamp/sfreq,50*nsamp/sfreq, dtype=int)]),axis=1)   # only until 50, because hz=100
    sum_abs_pow = delta + theta + alpha + beta + gamma
    feats[:,0] = delta /sum_abs_pow
    feats[:,1] = theta /sum_abs_pow
    feats[:,2] = alpha /sum_abs_pow
    feats[:,3] = beta  /sum_abs_pow
    feats[:,4] = gamma /sum_abs_pow
    feats[:,5] = np.dot(np.array([3.5,4,5,7,30]),feats[:,0:5].T ) / (sfreq/2-0.5) #smean
    feats[:,6] = np.sqrt(np.max(signals, axis=1))    #PAV
    feats[:,7] = np.sqrt(np.abs(np.min(signals, axis=1)))   #VAV   
    feats[:,8] = np.argmax(signals, axis=1)/nsamp #PAP
    feats[:,9] = np.argmin(signals, axis=1)/nsamp #VAP
    feats[:,10] = np.sqrt(np.sum(np.abs(signals), axis=1)/ np.mean(np.sum(np.abs(signals), axis=1))) # AUC
    feats[:,11] = np.sum(((np.roll(np.sign(signals), 1,axis=1) - np.sign(signals)) != 0).astype(int),axis=1)/nsamp #TVC
    feats[:,12] = np.log10(np.std(signals, axis=1)) #STD/VAR
    feats[:,13] = np.log10(stats.kurtosis(signals,fisher=False,axis=1))       # kurtosis
    feats[:,14] = np.log10(-np.sum([(x/nsamp)*((np.log((x+np.spacing(1))/nsamp))) for x in np.apply_along_axis(lambda x: np.histogram(x, bins=8)[0], 1, signals)],axis=1))  # entropy.. yay, one line...
    if np.any(feats==np.nan): print('NaN detected')
    return np.nan_to_num(feats)