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utils_sig.py
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import numpy as np
from scipy.fft import fft
from scipy import signal
from scipy.signal import butter, filtfilt
def butter_bandpass(sig, lowcut, highcut, fs, order=2):
# butterworth bandpass filter
sig = np.reshape(sig, -1)
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
y = filtfilt(b, a, sig)
return y
def butter_bandpass_batch(sig_list, lowcut, highcut, fs, order=2):
# butterworth bandpass filter (batch version)
# signals are in the sig_list
y_list = []
for sig in sig_list:
sig = np.reshape(sig, -1)
nyq = 0.5 * fs
low = lowcut / nyq
high = highcut / nyq
b, a = butter(order, [low, high], btype='band')
y = filtfilt(b, a, sig)
y_list.append(y)
return np.array(y_list)
def hr_fft(sig, fs, harmonics_removal=True):
# get heart rate by FFT
# return both heart rate and PSD
sig = sig.reshape(-1)
sig = sig * signal.windows.hann(sig.shape[0])
sig_f = np.abs(fft(sig))
low_idx = np.round(0.6 / fs * sig.shape[0]).astype('int')
high_idx = np.round(4 / fs * sig.shape[0]).astype('int')
sig_f_original = sig_f.copy()
sig_f[:low_idx] = 0
sig_f[high_idx:] = 0
peak_idx, _ = signal.find_peaks(sig_f)
sort_idx = np.argsort(sig_f[peak_idx])
sort_idx = sort_idx[::-1]
peak_idx1 = peak_idx[sort_idx[0]]
peak_idx2 = peak_idx[sort_idx[1]]
f_hr1 = peak_idx1 / sig.shape[0] * fs
hr1 = f_hr1 * 60
f_hr2 = peak_idx2 / sig.shape[0] * fs
hr2 = f_hr2 * 60
if harmonics_removal:
if np.abs(hr1-2*hr2)<10:
hr = hr2
else:
hr = hr1
else:
hr = hr1
x_hr = np.arange(len(sig))/len(sig)*fs*60
return hr, sig_f_original, x_hr
def hr_fft_batch(sig_list, fs, harmonics_removal=True):
# get heart rate by FFT (batch version)
# return both heart rate and PSD
hr_list = []
for sig in sig_list:
sig = sig.reshape(-1)
sig = sig * signal.windows.hann(sig.shape[0])
sig_f = np.abs(fft(sig))
low_idx = np.round(0.6 / fs * sig.shape[0]).astype('int')
high_idx = np.round(4 / fs * sig.shape[0]).astype('int')
sig_f_original = sig_f.copy()
sig_f[:low_idx] = 0
sig_f[high_idx:] = 0
peak_idx, _ = signal.find_peaks(sig_f)
sort_idx = np.argsort(sig_f[peak_idx])
sort_idx = sort_idx[::-1]
peak_idx1 = peak_idx[sort_idx[0]]
peak_idx2 = peak_idx[sort_idx[1]]
f_hr1 = peak_idx1 / sig.shape[0] * fs
hr1 = f_hr1 * 60
f_hr2 = peak_idx2 / sig.shape[0] * fs
hr2 = f_hr2 * 60
if harmonics_removal:
if np.abs(hr1-2*hr2)<10:
hr = hr2
else:
hr = hr1
else:
hr = hr1
# x_hr = np.arange(len(sig))/len(sig)*fs*60
hr_list.append(hr)
return np.array(hr_list)
def normalize(x):
return (x-x.mean())/x.std()
def SNR_get(waveform, gt_hr, fs, filtered=False):
waveform = np.reshape(waveform, -1)
if filtered:
waveform = butter_bandpass(waveform, 0.6, 4, 60)
N = waveform.shape[0]
# low_idx = np.round(0.6 / fs * waveform.shape[0]).astype('int')
# high_idx = np.round(4 / fs * waveform.shape[0]).astype('int')
# waveform[:low_idx] = 0
# waveform[high_idx:] = 0
bin1 = round(5/60/fs*N)
bin2 = round(10/60/fs*N)
f1 = gt_hr / 60
f2 = f1 * 2
bc1 = round(f1*N/fs)
bc2 = round(f2*N/fs)
window = signal.windows.hann(N)
win_waveform = waveform * window
waveform_f = np.abs(fft(win_waveform))**2
total_power = np.sum(waveform_f)
signal_power1 = 2*np.sum(waveform_f[bc1-bin1:bc1+bin1])
signal_power2 = 2*np.sum(waveform_f[bc2-bin2:bc2+bin2])
signal_power = signal_power1 + signal_power2
noise_power = total_power - signal_power
snr = 10*np.log10(signal_power/noise_power)
return snr