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filtered_data/X_test_filtered.npy

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+size 176840128

filtered_data/X_train_filtered.npy

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+oid sha256:f4fda7b9ff94d7e614c9541c6c6f4a9e948aec61b022d010f53e4936dc408fb2
+size 1414480128

filtered_data/X_val_filtered.npy

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+oid sha256:34dbb38b54b83189b7941673086065bc818d34ca4180b8981d7381588b99f414
+size 176800128

filtered_data/class_names.npy

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+version https://git-lfs.github.com/spec/v1
+oid sha256:55a079fa8b25ee383fec13c408f58ca33caa665b0eb5109eb658e6cccaa94ac6
+size 272

filtered_data/filter.py

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+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()

filtered_data/y_test.npy

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filtered_data/y_train.npy

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+size 283024

filtered_data/y_val.npy

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