Initial Commit

app.py

@@ -0,0 +1,11 @@
+import numpy as np
+from make_predictions import make_prediction
+import gradio as gr
+
+def predict(file):
+    # print(file.name)
+    ans = make_prediction(file.name)
+    return {ans[0] :ans[1]}
+
+iface = gr.Interface(predict, "file", "label", title="ECG Classification", description="Upload an ECG file to classify it as Normal or Abnormal", examples=[["data/faizan_r8.txt"]])
+iface.launch()

baseline_wander_removal.py

@@ -0,0 +1,98 @@
+import numpy as np
+from scipy.signal import medfilt
+import matplotlib.pyplot as plt
+# import seaborn as sns
+
+# sns.set_theme()
+
+BASIC_SRATE = 160
+
+
+def get_median_filter_width(sampling_rate, duration):
+    res = int(sampling_rate*duration)
+    res += ((res % 2) - 1)  # needs to be an odd number
+    return res
+# baseline fitting by filtering
+# === Define Filtering Params for Baseline fitting Leads======================
+# ms_flt_array = [0.2, 0.6]    #<-- length of baseline fitting filters (in seconds)
+# mfa = np.zeros(len(ms_flt_array), dtype='int')
+# for i in range(0, len(ms_flt_array)):
+#     mfa[i] = get_median_filter_width(BASIC_SRATE,ms_flt_array[i])
+
+# def filter_signal(X):
+#     global mfa
+#     X0 = X  #read orignal signal
+#     for mi in range(0,len(mfa)):
+#         X0 = medfilt(X0,mfa[mi]) # apply median filter one by one on top of each other
+#         print(X0 - X)
+#     X0 = np.subtract(X,X0)  # finally subtract from orignal signal
+#     return X0
+
+
+def bw_remover(fs, X):
+    kernel = get_median_filter_width(fs, 0.4)
+    X0 = X  # read orignal signal
+    # apply median filter one by one on top of each other
+    X0 = medfilt(X0, kernel)
+    # print(X0 - X)
+    X0 = np.subtract(X, X0)  # finally subtract from orignal signal
+    return X0
+
+
+
+def band_pass_filter(signal):
+    # Bandpass filter
+    if type(signal) != np.ndarray:
+        signal = signal.to_numpy()
+    result = None
+    sig = signal.copy()
+    for index in range(len(signal)):
+        sig[index] = signal[index]
+
+        if (index >= 1):
+            sig[index] += 2*sig[index-1]
+
+        if (index >= 2):
+            sig[index] -= sig[index-2]
+
+        if (index >= 6):
+            sig[index] -= 2*signal[index-6]
+
+        if (index >= 12):
+            sig[index] += signal[index-12]
+
+    result = sig.copy()
+    for index in range(len(signal)):
+        result[index] = -1*sig[index]
+
+        if (index >= 1):
+            result[index] -= result[index-1]
+
+        if (index >= 16):
+            result[index] += 32*sig[index-16]
+
+        if (index >= 32):
+            result[index] += sig[index-32]
+
+    # Normalize the result from the high pass filter
+
+    # max_val = max(max(result), -min(result))
+    # result = result/max_val
+
+    return result
+
+
+if __name__ == '__main__':
+    signal = np.loadtxt("./Lead 2/n1_lead2.txt")[:1000]
+    signal_bs = bw_remover(BASIC_SRATE, signal)
+    signal_flt = band_pass_filter(signal_bs)
+    plt.figure(figsize=(16, 9))
+    plt.subplot(2, 1, 1)
+    plt.plot(signal)
+    plt.grid(True)
+    plt.title("RAW signal")
+    plt.subplot(2, 1, 2)
+    plt.plot(signal_flt)
+    plt.title("baseline removed signal")
+    plt.grid(True)
+    plt.show()

data/faizan_r8.txt

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make_predictions.py

@@ -0,0 +1,25 @@
+import numpy as np
+from read_data import prepare_all_leads, visualize_sig
+from postprocessing import make_predictions_indi, labels_map
+from scipy import stats as st
+import warnings
+
+warnings.filterwarnings("ignore")
+
+def make_prediction(path):
+    leads = prepare_all_leads(path)
+    # print(leads[0][0].shape)
+    # print(leads[1][0].shape)
+    # print(leads[2][0].shape)
+    # visualize_sig([leads[0][0], leads[1][0], leads[2][0]])
+    x = make_predictions_indi(*leads)
+    # print(x.mean(axis=0))
+    # print(np.argmax(x, axis=1))
+    index = st.mode(np.argmax(x, axis=1))[0][0]
+    confidence = x.mean(axis=0)[index]
+    return labels_map[index], float(confidence)
+
+if __name__ == "__main__":
+    prediction = make_prediction("data/faizan_r8.txt")
+    # prediction = make_prediction("data/a_fib.npy")
+    print(prediction)

model/ResNet-lead-0.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f573373d1fb5d86be738594aebd2ac0a42dde9c3358da27324277cc88ce0fd03
+size 2081879

model/ResNet-lead-1.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e649f7c20301dfd614f1504ff8c23da284a1e422acbdbb74f63acbece25c3f97
+size 2081879

model/ResNet-lead-2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bb6a7635539ee96d4dd6011afb46e06a94d54a49e2f70234cfbfb990f1149918
+size 2081879

model_def.py

@@ -0,0 +1,67 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class NeuralNetwork(nn.Module):
+    def __init__(self):
+        super().__init__()
+        n_filters = 64
+        self.conv_1 = nn.Conv1d(        1, n_filters, 8, stride=1, padding='same')
+        self.conv_2 = nn.Conv1d(n_filters, n_filters, 5, stride=1, padding='same')
+        self.conv_3 = nn.Conv1d(n_filters, n_filters, 3, stride=1, padding='same')
+        self.conv_4 = nn.Conv1d(        1, n_filters, 1, stride=1, padding='same') # Expanding for addition
+
+        self.conv_5 = nn.Conv1d(  n_filters, n_filters*2, 8, stride=1, padding='same')
+        self.conv_6 = nn.Conv1d(n_filters*2, n_filters*2, 5, stride=1, padding='same')
+        self.conv_7 = nn.Conv1d(n_filters*2, n_filters*2, 3, stride=1, padding='same')
+        self.conv_8 = nn.Conv1d(  n_filters, n_filters*2, 1, stride=1, padding='same')
+        
+        self.conv_9  = nn.Conv1d(n_filters*2, n_filters*2, 8, stride=1, padding='same')
+        self.conv_10 = nn.Conv1d(n_filters*2, n_filters*2, 5, stride=1, padding='same')
+        self.conv_11 = nn.Conv1d(n_filters*2, n_filters*2, 3, stride=1, padding='same')
+        self.conv_12 = nn.Conv1d(n_filters*2, n_filters*2, 1, stride=1, padding='same')
+
+        self.classifier = nn.Linear(128, 5)
+        self.log_softmax = nn.LogSoftmax(dim=1)
+
+    def forward(self, x):
+        x = x.float()
+        
+        # Block 1
+        a = self.conv_1(x)
+        a = F.relu(a)
+        b = self.conv_2(a)
+        b = F.relu(b)
+        c = self.conv_3(b)
+        shortcut = self.conv_4(x)
+        output_1 = torch.add(c, shortcut)
+        output_1 = F.relu(output_1)
+        
+        #Block 2
+        a = self.conv_5(output_1)
+        a = F.relu(a)
+        b = self.conv_6(a)
+        b = F.relu(b)
+        c = self.conv_7(b)
+        shortcut = self.conv_8(output_1)
+        output_2 = torch.add(c, shortcut)
+        output_2 = F.relu(output_2)
+        
+        #Block 3
+        a = self.conv_9(output_2)
+        a = F.relu(a)
+        b = self.conv_10(a)
+        b = F.relu(b)
+        c = self.conv_11(b)
+        shortcut = self.conv_12(output_2)
+        output_3 = torch.add(c, shortcut)
+        output_3 = F.relu(output_3)
+        res = self.classifier(output_3.mean((2,)))
+        logits = self.log_softmax(res)
+        return logits
+
+
+if __name__ == '__main__':
+    model = NeuralNetwork()
+    print(model)

postprocessing.py

@@ -0,0 +1,38 @@
+import numpy as np
+import torch
+from torch import nn
+from model_def import NeuralNetwork
+
+labels_map = {
+    0 : "atrial fibrillation",
+    1 : "sinus arrhythmia",
+    2 : "bradycardia",
+    3 : "1st degree av block",
+    4 : "sinus rhythm",
+}
+
+PATH = 'model/' #ResNet-lead-0.pth'
+
+lead_1_model = NeuralNetwork()
+lead_1_model.load_state_dict(torch.load(f"{PATH}/ResNet-lead-0.pth", map_location=torch.device('cpu')))
+
+lead_2_model = NeuralNetwork()
+lead_2_model.load_state_dict(torch.load(f"{PATH}/ResNet-lead-1.pth", map_location=torch.device('cpu')))
+
+lead_3_model = NeuralNetwork()
+lead_3_model.load_state_dict(torch.load(f"{PATH}/ResNet-lead-2.pth", map_location=torch.device('cpu')))
+
+def helper(sig, model):
+    inpt = sig[:, np.newaxis, :]
+    with torch.no_grad():
+        res = model(torch.from_numpy(inpt))
+    res = torch.exp(res)
+    prediction_scores = res.numpy()
+    return prediction_scores
+
+def make_predictions_indi(lead1, lead2, lead3):
+    p1 = helper(lead1, lead_1_model)
+    p2 = helper(lead2, lead_2_model)
+    p3 = helper(lead3, lead_3_model)
+    p_avg = (p1 + p2 + p3)/3
+    return p_avg

read_data.py

@@ -0,0 +1,59 @@
+import numpy as np
+from scipy.signal import resample
+from baseline_wander_removal import bw_remover
+import pywt
+import matplotlib.pyplot as plt
+
+def normalize(sig):
+    return 2*((sig-np.min(sig))/(np.max(sig)-np.min(sig)))
+
+def visualize_sig(sig):
+    fig, ax = plt.subplots(3, 1, figsize=(10, 10))
+    ax[0].plot(sig[0][:500])
+    ax[1].plot(sig[1][:500])
+    ax[2].plot(sig[2][:500])
+    plt.show()
+
+def prepare_all_leads(path):
+    if path.endswith(".txt"):
+        sig = np.loadtxt(path, delimiter=',', unpack=True)
+    elif path.endswith(".npy"):
+        sig = np.load(path, allow_pickle=True)
+        x = pywt.wavedec(sig[0], 'db6', level=2)[0]
+        y = pywt.wavedec(sig[1], 'db6', level=2)[0]
+        z = pywt.wavedec(sig[2], 'db6', level=2)[0]
+        # visualize_sig([x, y, z])
+        return x[None, :], y[None, :], z[None, :]
+    # return sig
+    freq = sig.shape[1] // 60
+    sig[0] = bw_remover(freq, sig[0])
+    sig[1] = bw_remover(freq, sig[1])
+    sig[2] = bw_remover(freq, sig[2])
+
+    # visualize_sig(sig)
+    sig_length = freq*2
+    total_samples = sig[0].shape[0] // sig_length
+
+    lead_1 = []
+    lead_2 = []
+    lead_3 = []
+    for i in range(total_samples):
+        x = sig[0][i*sig_length:(i+1)*sig_length]
+        x = pywt.wavedec(x, 'db6', level=1)[0]
+        x = resample(x, 259)
+        x = normalize(x)
+        lead_1.append(x)
+
+        y = sig[1][i*sig_length:(i+1)*sig_length]
+        y = pywt.wavedec(y, 'db6', level=1)[0]
+        y = resample(y, 259)
+        y = normalize(y)
+        lead_2.append(y)
+
+        z = sig[2][i*sig_length:(i+1)*sig_length]
+        z = pywt.wavedec(z, 'db6', level=1)[0]
+        z = resample(z, 259)
+        z = normalize(z)
+        lead_3.append(z)
+    
+    return np.asarray(lead_1), np.asarray(lead_2), np.asarray(lead_3)