108 lines
4.7 KiB
Python
108 lines
4.7 KiB
Python
import numpy as np
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from scipy.interpolate import interp1d
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import utils
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def signal_filter_split(conf, signal_data_raw, signal_fs, verbose=True):
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# 滤波
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# 50Hz陷波滤波器
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# signal_data = utils.butterworth(data=signal_data, _type="bandpass", low_cut=0.5, high_cut=45, order=10, sample_rate=signal_fs)
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if verbose:
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print("Applying 50Hz notch filter...")
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signal_data = utils.notch_filter(data=signal_data_raw, notch_freq=50.0, quality_factor=30.0, sample_rate=signal_fs)
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resp_data_0 = utils.butterworth(data=signal_data, _type="lowpass", low_cut=50, order=10, sample_rate=signal_fs)
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resp_fs = conf["resp"]["downsample_fs_1"]
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resp_data_1 = utils.downsample_signal_fast(original_signal=resp_data_0, original_fs=signal_fs, target_fs=resp_fs)
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resp_data_1 = utils.average_filter(raw_data=resp_data_1, sample_rate=resp_fs, window_size_sec=20)
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resp_data_2 = utils.butterworth(data=resp_data_1, _type=conf["resp_filter"]["filter_type"],
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low_cut=conf["resp_filter"]["low_cut"],
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high_cut=conf["resp_filter"]["high_cut"], order=conf["resp_filter"]["order"],
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sample_rate=resp_fs)
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if verbose:
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print("Begin plotting signal data...")
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# fig = plt.figure(figsize=(12, 8))
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# # 绘制三个图raw_data、resp_data_1、resp_data_2
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# ax0 = fig.add_subplot(3, 1, 1)
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# ax0.plot(np.linspace(0, len(signal_data) // signal_fs, len(signal_data)), signal_data, color='blue')
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# ax0.set_title('Raw Signal Data')
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# ax1 = fig.add_subplot(3, 1, 2, sharex=ax0)
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# ax1.plot(np.linspace(0, len(resp_data_1) // resp_fs, len(resp_data_1)), resp_data_1, color='orange')
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# ax1.set_title('Resp Data after Average Filtering')
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# ax2 = fig.add_subplot(3, 1, 3, sharex=ax0)
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# ax2.plot(np.linspace(0, len(resp_data_1) // resp_fs, len(resp_data_2)), resp_data_2, color='green')
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# ax2.set_title('Resp Data after Butterworth Filtering')
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# plt.tight_layout()
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# plt.show()
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bcg_data = utils.butterworth(data=signal_data, _type=conf["bcg_filter"]["filter_type"],
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low_cut=conf["bcg_filter"]["low_cut"],
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high_cut=conf["bcg_filter"]["high_cut"], order=conf["bcg_filter"]["order"],
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sample_rate=signal_fs)
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return signal_data, resp_data_2, resp_fs, bcg_data, signal_fs
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def psg_effort_filter(conf, effort_data_raw, effort_fs):
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# 滤波
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effort_data_1 = utils.bessel(data=effort_data_raw, _type=conf["effort_filter"]["filter_type"],
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low_cut=conf["effort_filter"]["low_cut"],
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high_cut=conf["effort_filter"]["high_cut"], order=conf["effort_filter"]["order"],
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sample_rate=effort_fs)
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# 移动平均
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effort_data_2 = utils.average_filter(raw_data=effort_data_1, sample_rate=effort_fs, window_size_sec=20)
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return effort_data_raw, effort_data_2, effort_fs
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def rpeak2hr(rpeak_indices, signal_length, ecg_fs):
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hr_signal = np.zeros(signal_length)
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for i in range(1, len(rpeak_indices)):
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rri = rpeak_indices[i] - rpeak_indices[i - 1]
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if rri == 0:
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continue
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hr = 60 * ecg_fs / rri # 心率，单位：bpm
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if hr > 120:
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hr = 120
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elif hr < 30:
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hr = 30
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hr_signal[rpeak_indices[i - 1]:rpeak_indices[i]] = hr
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# 填充最后一个R峰之后的心率值
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if len(rpeak_indices) > 1:
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hr_signal[rpeak_indices[-1]:] = hr_signal[rpeak_indices[-2]]
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return hr_signal
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def rpeak2rri_repeat(rpeak_indices, signal_length, ecg_fs):
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rri_signal = np.zeros(signal_length)
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for i in range(1, len(rpeak_indices)):
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rri = rpeak_indices[i] - rpeak_indices[i - 1]
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rri_signal[rpeak_indices[i - 1]:rpeak_indices[i]] = rri
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# 填充最后一个R峰之后的RRI值
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if len(rpeak_indices) > 1:
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rri_signal[rpeak_indices[-1]:] = rri_signal[rpeak_indices[-2]]
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# 遍历异常值
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for i in range(1, len(rpeak_indices)):
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rri = rpeak_indices[i] - rpeak_indices[i - 1]
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if rri < 0.3 * ecg_fs or rri > 2 * ecg_fs:
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rri_signal[rpeak_indices[i - 1]:rpeak_indices[i]] = 0
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return rri_signal
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def rpeak2rri_interpolation(rpeak_indices, ecg_fs, rri_fs):
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r_peak_time = np.asarray(rpeak_indices) / ecg_fs
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rri = np.diff(r_peak_time)
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t_rri = r_peak_time[1:]
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mask = (rri > 0.3) & (rri < 2.0)
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rri_clean = rri[mask]
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t_rri_clean = t_rri[mask]
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t_uniform = np.arange(t_rri_clean[0], t_rri_clean[-1], 1/rri_fs)
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f = interp1d(t_rri_clean, rri_clean, kind='linear', fill_value="extrapolate")
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rri_uniform = f(t_uniform)
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return rri_uniform, rri_fs
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