File size: 4,605 Bytes
854b533 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | import numpy as np
import pywt
import os
from scipy.signal import butter, filtfilt, iirnotch, medfilt, find_peaks
import matplotlib.pyplot as plt
# 1. Load the Data
print("Loading datasets...")
data_train = np.load("X_train.npy")
data_valid = np.load("X_val.npy")
data_test = np.load("X_test.npy")
print("\n--- Dataset Shapes ---")
print(f"Train data shape: {data_train.shape}")
print(f"Validation data shape: {data_valid.shape}")
print(f"Test data shape: {data_test.shape}")
print("----------------------\n")
os.mkdir("filtered_data")
# 2. Define the Processing Functions
def _remove_baseline_wander(ecg_signal, wavelet='sym8'):
desired = int(np.ceil(np.log2(100/0.5)))
max_level = pywt.dwt_max_level(len(ecg_signal), pywt.Wavelet(wavelet).dec_len)
level = min(desired, max_level)
coeffs = pywt.wavedec(ecg_signal, wavelet, level=level)
coeffs_filt = [coeffs[0]] + [np.zeros_like(c) for c in coeffs[1:]]
baseline = pywt.waverec(coeffs_filt, wavelet)
baseline = baseline[:len(ecg_signal)]
return ecg_signal - baseline
def _denoise(data):
# Wavelet transform
coeffs = pywt.wavedec(data=data, wavelet='db5', level=9)
cA9, cD9, cD8, cD7, cD6, cD5, cD4, cD3, cD2, cD1 = coeffs
# Threshold denoising
threshold = (np.median(np.abs(cD1)) / 0.6745) * (np.sqrt(2 * np.log(len(cD1))))
cD1.fill(0)
cD2.fill(0)
for i in range(1, len(coeffs) - 2):
coeffs[i] = pywt.threshold(coeffs[i], threshold)
# Inverse wavelet transform to obtain the denoised signal
rdata = pywt.waverec(coeffs=coeffs, wavelet='db5')
return rdata
def remove_noise_and_wander(data):
baseline_wander = _remove_baseline_wander(data)
cleaned = _denoise(baseline_wander)
return cleaned
# 3. Apply the Filter
# Assuming your data shape is (num_samples, signal_length).
# If it's a 1D array of a single continuous signal, remove the list comprehension loop.
def process_batch(dataset_array):
print(f"Processing {len(dataset_array)} samples...")
# Applies the cleaning function to each row/signal in the array
return [remove_noise_and_wander(signal) for signal in dataset_array]
print("\nFiltering Train Data...")
train_filtered = process_batch(data_train)
print("Filtering Validation Data...")
valid_filtered = process_batch(data_valid)
print("Filtering Test Data...")
test_filtered = process_batch(data_test)
np.save("filtered_data/X_train_filtered.npy", train_filtered)
np.save("filtered_data/X_val_filtered.npy", valid_filtered)
np.save("filtered_data/X_test_filtered.npy", test_filtered)
print("Loading data...")
data_train_new = np.load("filtered_data/X_train_filtered.npy")
data_valid_new = np.load("filtered_data/X_val_filtered.npy")
data_test_new = np.load("filtered_data/X_test_filtered.npy")
print("\n--- Dataset Shapes ---")
print(f"Train data shape: {data_train_new.shape}")
print(f"Validation data shape: {data_valid_new.shape}")
print(f"Test data shape: {data_test_new.shape}")
print("----------------------\n")
# --- 3. Plot 5 Samples Before and After ---
# We assume data_train is a 2D array of shape (num_samples, signal_length)
num_samples_to_plot = 5
# Create a figure with 5 rows and 2 columns
fig, axes = plt.subplots(nrows=num_samples_to_plot, ncols=2, figsize=(16, 12))
fig.suptitle('ECG Signal Processing: Before vs. After', fontsize=18, fontweight='bold')
for i in range(num_samples_to_plot):
# Get the original 1D signal for the i-th sample
original_signal = data_train_new[i]
# Process the signal
filtered_signal = remove_noise_and_wander(original_signal)
# Plot Original (Before) - Left Column
axes[i, 0].plot(original_signal, color='crimson', linewidth=1)
axes[i, 0].set_title(f"Sample {i+1}: Original (Raw)")
axes[i, 0].set_ylabel("Amplitude")
axes[i, 0].grid(True, linestyle='--', alpha=0.6)
# Plot Filtered (After) - Right Column
axes[i, 1].plot(filtered_signal, color='navy', linewidth=1)
axes[i, 1].set_title(f"Sample {i+1}: Filtered (Denoised & Baseline Removed)")
axes[i, 1].grid(True, linestyle='--', alpha=0.6)
# Only show x-axis label for the bottom-most plots to keep it clean
if i == num_samples_to_plot - 1:
axes[i, 0].set_xlabel("Time steps")
axes[i, 1].set_xlabel("Time steps")
# Adjust layout so titles and labels don't overlap
plt.tight_layout(rect=[0, 0.03, 1, 0.96])
# Save the figure as a high-resolution PNG
plt.savefig("ecg_filtering_results.png", dpi=300, bbox_inches='tight')
print("Image saved successfully as 'ecg_filtering_results.png'")
plt.show() |