Peak detection with causal recursive moving average in ABP [ex9xx.x]ΒΆ

Recursive implementation.

import numpy as np
import matplotlib.pyplot as plt
from scipy.signal import find_peaks
import csv as csv

import lmlib as lm
from lmlib.utils.generator import gen_slope, gen_wgn
from lmlib.utils.beta import find_max_mask
from lmlib.utils.beta import load_source_csv

def causal_moving_average_recursive(y, L):
    Recursive, causal moving average filter with sliding of window size L+1.

    :param y: signal
    :param L: Window length
    :return: filtered signal

    y_avg = np.zeros_like(y) # create empty array
    K = len(y)

    y_avg[L] = y[0]  # for correct initialization

    for k in range(L, K-1):
        y_avg[k+1] = y_avg[k] + (y[k+1]-y[k-L] ) / (L+1)

    return y_avg

# Load Signal
k0 = 2200 # signal start index
K = 1300 # number of samples to load
fs = 500 # sampling frequency

y_raw = load_source_csv('./csv-data/cerebral-vasoreg-diabetes-heaad-up-tilt_day1-s0030DA-noheader.csv', time_format = "H:M:S", K = K, k = k0)

k = range(K)

# select abp signal and add Gaussian noise (to test robustness of detection)
y = y_raw[:, 3]  + gen_wgn(K, sigma=1.0, seed=43141)

# --- standard peak detection on abp
(peaks_abp_ind, _) = find_peaks(y, height=0, distance=fs*0.8)

# --- moving average filter and peak detection
L=3 # averaging window size is L+1

y_ma_asym = causal_moving_average_recursive(y, L=L) # non-symmetric filter
(peaks_abp_mean_asym_ind, _) = find_peaks(y_ma_asym, distance=fs*0.8)

# Plots
_, axs = plt.subplots(1, 1, sharex='all', figsize=(8, 3.5))

ax = axs
ax.plot(k, y, lw=0.5, c='tab:gray', label='$y$')
ax.plot(k, y_ma_asym, lw=1.0, c='b', label='${y}_{avg}$')
ax.scatter(peaks_abp_ind, y[peaks_abp_ind],s=40, marker=7,  c='tab:gray', label='peaks', zorder=20)
ax.scatter(peaks_abp_mean_asym_ind, y_ma_asym[peaks_abp_mean_asym_ind],s=40, marker=7,  c='b', label='peaks', zorder=20)
ax.set_title('non-symmetric, causal moving average filter with L='+str(L))

Total running time of the script: ( 0 minutes 0.217 seconds)

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