MIT心电信号数据p波检测python代码

以下是使用Python进行MIT心电信号的P波检测的代码：

首先，需要安装wfdb包，可以使用以下命令进行安装：

!pip install wfdb

然后，可以使用以下代码读取MIT心电信号数据：

import wfdb
record = wfdb.rdrecord('mitdb/100', channels=[0])
signal = record.p_signal.flatten()

接下来，我们需要对信号进行滤波，可以使用以下代码进行带通滤波:

from scipy.signal import butter, filtfilt

def butter_bandpass(lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = filtfilt(b, a, data)
    return y

fs = record.fs
signal_filtered = butter_bandpass_filter(signal, 1, 30, fs, order=4)

接下来，我们需要找到P波。P波通常是QRS波群之前的第一个正向波峰。可以使用以下代码找到P波：

import numpy as np
from scipy.signal import find_peaks

peaks, _ = find_peaks(signal_filtered, distance=int(0.2*fs), height=0.2)
rpeaks, _ = wfdb.processing.gqrs_detect(signal_filtered, fs=fs)

p_peaks = []
for p in peaks:
    if p < rpeaks[0]:
        p_peaks.append(p)

p_peak = np.min(p_peaks)

最后，我们可以将P波的位置可视化：

import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

fig, ax = plt.subplots()
ax.plot(signal_filtered)
ax.add_patch(Rectangle((p_peak-20, -0.5), 40, 1, alpha=0.2, color='r'))
plt.show()

完整的代码如下：

import wfdb
from scipy.signal import butter, filtfilt, find_peaks
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle

def butter_bandpass(lowcut, highcut, fs, order=5):
    nyq = 0.5 * fs
    low = lowcut / nyq
    high = highcut / nyq
    b, a = butter(order, [low, high], btype='band')
    return b, a

def butter_bandpass_filter(data, lowcut, highcut, fs, order=5):
    b, a = butter_bandpass(lowcut, highcut, fs, order=order)
    y = filtfilt(b, a, data)
    return y

record = wfdb.rdrecord('mitdb/100', channels=[0])
signal = record.p_signal.flatten()

fs = record.fs
signal_filtered = butter_bandpass_filter(signal, 1, 30, fs, order=4)

peaks, _ = find_peaks(signal_filtered, distance=int(0.2*fs), height=0.2)
rpeaks, _ = wfdb.processing.gqrs_detect(signal_filtered, fs=fs)

p_peaks = []
for p in peaks:
    if p < rpeaks[0]:
        p_peaks.append(p)

p_peak = np.min(p_peaks)

fig, ax = plt.subplots()
ax.plot(signal_filtered)
ax.add_patch(Rectangle((p_peak-20, -0.5), 40, 1, alpha=0.2, color='r'))
plt.show()