可以给出python具体代码吗

当然可以，以下是一个使用卷积神经网络（CNN）进行心跳信号分类的Python示例代码：

import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from keras.utils import to_categorical
from keras.models import Sequential
from keras.layers import Dense, Conv1D, MaxPooling1D, Flatten

# 加载数据
train_df = pd.read_csv('train.csv')
test_df = pd.read_csv('testA.csv')

# 数据预处理
train_data = np.array([list(map(float, s.split(','))) for s in train_df['heartbeat_signals'].values])
test_data = np.array([list(map(float, s.split(','))) for s in test_df['heartbeat_signals'].values])
train_labels = to_categorical(train_df['label'].values)

# 划分训练集和验证集
x_train, x_val, y_train, y_val = train_test_split(train_data, train_labels, test_size=0.2, random_state=42)

# 构建CNN模型
model = Sequential()
model.add(Conv1D(filters=32, kernel_size=5, activation='relu', input_shape=(187, 1)))
model.add(MaxPooling1D(pool_size=2))
model.add(Conv1D(filters=64, kernel_size=5, activation='relu'))
model.add(MaxPooling1D(pool_size=2))
model.add(Flatten())
model.add(Dense(100, activation='relu'))
model.add(Dense(4, activation='softmax'))

# 编译模型
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

# 训练模型
model.fit(x_train.reshape(-1, 187, 1), y_train, epochs=10, batch_size=64, validation_data=(x_val.reshape(-1, 187, 1), y_val))

# 预测测试集
test_pred = model.predict(test_data.reshape(-1, 187, 1))

# 保存结果
test_df['label'] = np.argmax(test_pred, axis=1)
test_df[['id', 'label']].to_csv('submission.csv', index=False)

该代码中使用了Keras框架，首先将心跳信号序列转换为numpy数组，然后将标签进行one-hot编码，划分训练集和验证集，构建了一个包含卷积层、池化层和全连接层的CNN模型，并使用交叉熵损失函数和Adam优化器进行模型训练。最后，使用训练好的模型对测试集进行预测，并保存结果。