使用Pan-Tompkins算法等QRS波群检测算法来识别QRS波群，并确定R峰的位置。代码

以下是使用Python实现Pan-Tompkins算法进行QRS波群检测的示例代码：

import numpy as np
import matplotlib.pyplot as plt

# 心电信号预处理
def preprocess(signal, fs):
    # 对信号进行低通滤波，去除高频噪声
    b, a = signal.butter(4, 20 / (fs / 2), 'low')
    signal = signal.filtfilt(b, a, signal)
    
    # 对信号进行高通滤波，去除低频漂移
    b, a = signal.butter(4, 0.5 / (fs / 2), 'high')
    signal = signal.filtfilt(b, a, signal)

    # 对信号进行移动平均滤波，降低噪声干扰
    window_size = int(0.08 * fs)
    signal = np.convolve(signal, np.ones((window_size,)) / window_size, mode='same')
    
    return signal

# 求取信号的一阶差分和二阶差分
def diff(signal):
    diff1 = np.diff(signal)
    diff2 = np.diff(diff1)
    return diff1, diff2

# 计算信号的移动平均值
def moving_average(signal, window_size):
    window = np.ones(window_size) / window_size
    return np.convolve(signal, window, mode='same')

# 对信号进行带通滤波
def bandpass_filter(signal, lowcut, highcut, fs, order=4):
    nyquist_freq = 0.5 * fs
    low = lowcut / nyquist_freq
    high = highcut / nyquist_freq
    b, a = signal.butter(order, [low, high], btype='band')
    y = signal.filtfilt(b, a, signal)
    return y

# 检测QRS波群的起始点和结束点
def detect_qrs(signal, fs):
    # 设置参数
    threshold1 = 0.3
    threshold2 = 0.6
    refractory_period = int(0.2 * fs)

    # 计算移动平均值
    ma_signal = moving_average(signal, int(0.08 * fs))

    # 计算一阶差分和二阶差分
    diff1, diff2 = diff(ma_signal)

    # 对信号进行带通滤波
    filtered_signal = bandpass_filter(signal, 5, 15, fs)

    # 计算能量
    energy = np.square(filtered_signal)

    # 计算阈值
    threshold = threshold1 * np.max(energy)

    # 检测QRS波群的起始点和结束点
    qrs_start = []
    qrs_end = []
    refractory_counter = 0

    for i in range(len(energy)):
        if energy[i] > threshold and refractory_counter == 0:
            qrs_start.append(i)
            refractory_counter = refractory_period
        elif energy[i] < threshold2 * threshold and refractory_counter > 0:
            refractory_counter -= 1
        elif energy[i] > threshold2 * threshold and refractory_counter > 0:
            qrs_end.append(i)
            refractory_counter = refractory_period

    # 对QRS波群的起始点和结束点进行校正
    for i in range(len(qrs_start)):
        j = qrs_start[i] + np.argmax(diff2[qrs_start[i]:qrs_end[i]]) + 1
        qrs_start[i] = j - np.argmax(diff1[j:qrs_end[i]])

    return qrs_start

# 定位R峰的位置
def locate_r_peak(signal, qrs_start, fs):
    r_peak = []
    window_size = int(0.1 * fs)
    
    for i in range(len(qrs_start)):
        j = qrs_start[i] + np.argmax(signal[qrs_start[i]:qrs_start[i]+window_size])
        r_peak.append(j)
    
    return r_peak

# 读取心电信号
fs = 360
data = np.loadtxt('ecg_signal.txt')
signal = data[:, 1]

# 心电信号预处理
signal = preprocess(signal, fs)

# 检测QRS波群的起始点
qrs_start = detect_qrs(signal, fs)

# 定位R峰的位置
r_peak = locate_r_peak(signal, qrs_start, fs)

# 绘制心电图和R峰位置
t = np.arange(len(signal)) / fs
plt.plot(t, signal)
plt.plot(t[r_peak], signal[r_peak], 'ro')
plt.xlabel('Time (s)')
plt.ylabel('Amplitude (mV)')
plt.show()

这段代码假设读取的心电信号数据保存在文件`ecg_signal.txt`中，每行包含时间和电压两个数据。在实际使用时，需要根据数据的格式进行修改。