ecg信号分类预测代码

这里提供一个使用深度学习模型（卷积神经网络）对ECG信号进行分类预测的Python代码示例：

import os
import wfdb
import numpy as np
from sklearn.utils import shuffle
from sklearn.metrics import classification_report
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten

# 设置数据路径和类别标签
data_path = "/path/to/data"
class_labels = ["N", "A", "V", "L", "R"]

# 读取数据
def load_data():
    files = os.listdir(data_path)
    ecg_signals = []
    ecg_labels = []
    for filename in files:
        record = wfdb.rdrecord(os.path.join(data_path, filename))
        annotation = wfdb.rdann(os.path.join(data_path, filename), 'atr')
        signal = record.p_signal[:,0]
        ecg_class = annotation.symbol
        for i in range(len(ecg_class)):
            if ecg_class[i] in class_labels:
                ecg_signal = signal[i*360:(i+1)*360]
                ecg_signals.append(ecg_signal)
                ecg_labels.append(ecg_class[i])
    return ecg_signals, ecg_labels

# 对数据进行预处理
def preprocess_data(ecg_signals, ecg_labels):
    ecg_signals = np.array(ecg_signals)
    ecg_signals = np.expand_dims(ecg_signals, axis=-1)
    ecg_labels = [class_labels.index(label) for label in ecg_labels]
    ecg_labels = to_categorical(ecg_labels)
    ecg_signals, ecg_labels = shuffle(ecg_signals, ecg_labels)
    return ecg_signals, ecg_labels

# 构建卷积神经网络模型
def build_model():
    model = Sequential()
    model.add(Conv1D(32, 3, activation='relu', input_shape=(360, 1)))
    model.add(Conv1D(64, 3, activation='relu'))
    model.add(MaxPooling1D(2))
    model.add(Flatten())
    model.add(Dense(128, activation='relu'))
    model.add(Dense(len(class_labels), activation='softmax'))
    model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
    return model

# 训练模型
def train_model(ecg_signals, ecg_labels):
    model = build_model()
    model.fit(ecg_signals, ecg_labels, validation_split=0.2, epochs=10, batch_size=32)
    return model

# 评估模型
def evaluate_model(model, ecg_signals, ecg_labels):
    ecg_labels = np.argmax(ecg_labels, axis=1)
    predictions = model.predict(ecg_signals)
    predictions = np.argmax(predictions, axis=1)
    print(classification_report(ecg_labels, predictions, target_names=class_labels))

# 加载数据
ecg_signals, ecg_labels = load_data()

# 预处理数据
ecg_signals, ecg_labels = preprocess_data(ecg_signals, ecg_labels)

# 训练模型
model = train_model(ecg_signals, ecg_labels)

# 评估模型
evaluate_model(model, ecg_signals, ecg_labels)

在这个示例中，我们使用WFDB库读取ECG信号数据，然后使用Keras构建一个卷积神经网络模型。我们使用交叉熵损失和Adam优化器训练模型，并使用分类准确度评估模型性能。最后，我们使用标准的分类报告评估模型的性能。