Wave Detector

Wave Detector#

WaveDetector object detects minimums and maximums of waves in a given frequency bandpass. Designed mainly to detect slow (delta - 0.5 - 4 Hz) waves. WaveDetector can also return statistical report of detected waves such as Δt, slope, peak2peak and min and max values.

Example

import numpy as np
import matplotlib.pyplot as plt
from sleep_classification.WaveDetector import WaveDetector

fs = 200
signal_length = 5 # s
wave_freq1 = 2
wave_freq2 = 1
noise_level = 0.5

t = np.arange(0, signal_length * fs) / fs
noise = np.random.randn(t.shape[0]) * noise_level
x1 = np.sin(2*np.pi * wave_freq1 * t)
x2 = np.sin(2*np.pi * wave_freq2 * t) * 0.5
x_noise = x1 + x2 + noise

WDet = WaveDetector(fs=fs, cutoff_low=0.5, cutoff_high=4)

stats, det = WDet(x_noise)
min_pos = det['min_pos']
min_val = det['min_val']
max_pos = det['max_pos']
max_val = det['max_val']

#plt.plot(t, x_noise)
#plt.xlabel('t [s]')
#plt.title('Original')
#plt.show()

plt.plot(t, x_noise)
plt.stem(t[min_pos], min_val, 'r')
plt.stem(t[max_pos], max_val, 'k')
plt.title('Detections')
plt.show()

print('Slope Stats')
print(stats['slope_stats'])

print('Peak2Peak Stats')
print(stats['pk2pk_stats'])

print('delta_t Stats')
print(stats['delta_t_stats'])
_images/1_det.png
class brainmaze_eeg.features.wave_detector.WaveDetector(fs, cutoff_low=0.5, cutoff_high=4)#

Designed for Delta wave detection. Can be deployed on any frequency range. See the documentation above for an usage example.

classmethod delta_t_stats(min_pos=None, min_vals=None, max_pos=None, max_vals=None, fs=None)#
detect_waves(X)#
classmethod max_stats(min_pos=None, min_vals=None, max_pos=None, max_vals=None, fs=None)#
classmethod min_stats(min_pos=None, min_vals=None, max_pos=None, max_vals=None, fs=None)#
classmethod pk2pk_stats(min_pos=None, min_vals=None, max_pos=None, max_vals=None, fs=None)#
classmethod slope_stats(min_pos=None, min_vals=None, max_pos=None, max_vals=None, fs=None)#
static stat(X)#
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