仿Yasa算法-睡眠分期，目前五分期86.5%

参考链接

具体内容可见：个人文章

参考代码

The code that was used to train the classifiers can be found on GitHub at:
Link

验证数据集

PhysiNet-Haaglanden
选取了其中150例数据（剔除了No.98，标签数据长度不匹配）

特征

参考Yasa的定义，每个脑电通道筛选了（283=84）个特征，每个眼电通道筛选了（233=69）个特征，肌电通道筛选了（10*3=30）个特征。

结果

（1）论文作者以训练好的模型（对单人）

1.三通道

使用通道名称：eeg_name=“EEG C4-M1”, eog_name=“EOG E1-M2”, emg_name=“EMG chin”
准确率：73.62%

// 
def calculate_accuracy(edf_file, txt_file):
    # 这里应该是你的准确率计算逻辑
    # 例如，打开EDF文件，进行一些处理，然后使用TXT文件中的数据进行比较
    # 返回计算得到的准确率

    # 加载 EDF 文件
    raw = mne.io.read_raw_edf(edf_file, preload=True)
    # Let's now load the human-scored hypnogram, where each value represents a 30-sec epoch.
    hypno = np.loadtxt(txt_file, dtype=str)

    sls = yasa.SleepStaging(raw, eeg_name="EEG C4-M1", eog_name="EOG E1-M2", emg_name="EMG chin")

    y_pred = sls.predict()

    # What is the accuracy of the prediction, compared to the human scoring
    accuracy = (hypno == y_pred).sum() / y_pred.size

    return accuracy

2.单通道脑电

使用通道名称：eeg_name=“EEG C4-M1”
准确率：72.79%

def calculate_accuracy(edf_file, txt_file):
    # 这里应该是你的准确率计算逻辑
    # 例如，打开EDF文件，进行一些处理，然后使用TXT文件中的数据进行比较
    # 返回计算得到的准确率

    # 加载 EDF 文件
    raw = mne.io.read_raw_edf(edf_file, preload=True)
    # Let's now load the human-scored hypnogram, where each value represents a 30-sec epoch.
    hypno = np.loadtxt(txt_file, dtype=str)

    # sls = yasa.SleepStaging(raw, eeg_name="EEG C4-M1", eog_name="EOG E1-M2", emg_name="EMG chin")
    sls = yasa.SleepStaging(raw, eeg_name="EEG C4-M1")

    y_pred = sls.predict()

    # What is the accuracy of the prediction, compared to the human scoring
    accuracy = (hypno == y_pred).sum() / y_pred.size

    return accuracy

（2）全连接，单通道脑电

通道：EEG C4-M1
准确率：77.23%

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 构建模型
model = Sequential()
model.add(Dense(128, input_shape=(84,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(5, activation='softmax'))  # 5 类分类

# 训练模型
model.fit(X_train, y_train, epochs=500, batch_size=32, validation_data=(X_test, y_test))

（3）全连接，4通道脑电

通道：‘EEG F4-M1’, ‘EEG C4-M1’, ‘EEG O2-M1’, ‘EEG C3-M2’
准确率：79.26%

# 划分训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)

# 构建模型
model = Sequential()
model.add(Dense(128, input_shape=(336,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(5, activation='softmax'))  # 5 类分类

# 训练模型
model.fit(X_train, y_train, epochs=500, batch_size=32, validation_data=(X_test, y_test))

（4）LightGBM，4通道脑电

通道：‘EEG F4-M1’, ‘EEG C4-M1’, ‘EEG O2-M1’, ‘EEG C3-M2’
准确率：82.62%

# 创建 LightGBM 分类器
lgbm = LGBMClassifier(n_estimators=100, learning_rate=0.05)
y_train_1d = np.argmax(y_train, axis=1)
y_test_1d = np.argmax(y_test, axis=1)

# 然后使用这个一维数组进行训练
lgbm.fit(X_train, y_train_1d)

# 预测和评估
y_pred = lgbm.predict(X_test)
accuracy = accuracy_score(y_test_1d, y_pred)
print(f"Accuracy: {accuracy}")

（5）全连接，4通道脑电+EMG+2通道眼电

通道：‘EEG F4-M1’, ‘EEG C4-M1’, ‘EEG O2-M1’, ‘EEG C3-M2’, ‘EMG chin’, ‘EOG E1-M2’, ‘EOG E2-M2’
准确率：81.24%

# 构建模型
model = Sequential()
model.add(Dense(128, input_shape=(504,), activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dense(5, activation='softmax'))  # 5 类分类

（6）LSTM，4通道脑电+EMG+2通道眼电

通道：‘EEG F4-M1’, ‘EEG C4-M1’, ‘EEG O2-M1’, ‘EEG C3-M2’, ‘EMG chin’, ‘EOG E1-M2’, ‘EOG E2-M2’
准确率：81.68%

def build_lstm_model(input_shape, num_classes):
    model = Sequential()

    # model.add(LSTM(50, input_shape=input_shape))
    model.add(LSTM(100, input_shape=input_shape))

    model.add(Dense(num_classes, activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

（7）LightGBM，4通道脑电+EMG+2通道眼电

通道：‘EEG F4-M1’, ‘EEG C4-M1’, ‘EEG O2-M1’, ‘EEG C3-M2’, ‘EMG chin’, ‘EOG E1-M2’, ‘EOG E2-M2’
准确率：84.22%

# 创建 LightGBM 分类器
lgbm = LGBMClassifier(n_estimators=100, learning_rate=0.05)

（8）CNN，4通道脑电+EMG+2通道眼电

通道：‘EEG F4-M1’, ‘EEG C4-M1’, ‘EEG O2-M1’, ‘EEG C3-M2’, ‘EMG chin’, ‘EOG E1-M2’, ‘EOG E2-M2’
准确率：86.50%

# 由于是1D数据，我们需要扩展一个维度来适配CNN
X_train = X_train[..., np.newaxis]
X_test = X_test[..., np.newaxis]

# 构建模型
model = tf.keras.models.Sequential([
    tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu', input_shape=(504, 1)),
    tf.keras.layers.Conv1D(filters=64, kernel_size=3, activation='relu'),
    tf.keras.layers.Dropout(0.5),
    tf.keras.layers.MaxPooling1D(pool_size=2),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(100, activation='relu'),
    tf.keras.layers.Dense(5, activation='softmax')
])