53 lines
2.1 KiB
Python
53 lines
2.1 KiB
Python
import utils
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import pandas as pd
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import numpy as np
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from scipy import signal
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def normalize_resp_signal_by_segment(resp_signal: np.ndarray, resp_fs, movement_mask, enable_list):
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# 根据呼吸信号的幅值改变区间，对每段进行Z-Score标准化
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normalized_resp_signal = np.zeros_like(resp_signal)
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# 全部填成nan
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normalized_resp_signal[:] = np.nan
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resp_signal_no_movement = resp_signal.copy()
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resp_signal_no_movement[np.array(movement_mask == 1).repeat(resp_fs)] = np.nan
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for i in range(len(enable_list)):
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enable_start = enable_list[i][0] * resp_fs
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enable_end = enable_list[i][1] * resp_fs
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segment = resp_signal_no_movement[enable_start:enable_end]
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# print(f"Normalizing segment {i+1}/{len(enable_list)}: start={enable_start}, end={enable_end}, length={len(segment)}")
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segment_mean = np.nanmean(segment)
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segment_std = np.nanstd(segment)
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if segment_std == 0:
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raise ValueError(f"segment_std is zero! segment_start: {enable_start}, segment_end: {enable_end}")
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# 同下一个enable区间的体动一起进行标准化
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if i <= len(enable_list) - 2:
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enable_end = enable_list[i + 1][0] * resp_fs
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raw_segment = resp_signal[enable_start:enable_end]
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normalized_resp_signal[enable_start:enable_end] = (raw_segment - segment_mean) / segment_std
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#如果enable区间不从0开始，则将前面的部分也进行标准化
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if enable_list[0][0] > 0:
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new_enable_start = 0
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enable_start = enable_list[0][0] * resp_fs
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enable_end = enable_list[0][1] * resp_fs
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segment = resp_signal_no_movement[enable_start:enable_end]
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segment_mean = np.nanmean(segment)
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segment_std = np.nanstd(segment)
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if segment_std == 0:
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raise ValueError(f"segment_std is zero! segment_start: {enable_start}, segment_end: {enable_end}")
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raw_segment = resp_signal[new_enable_start:enable_start]
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normalized_resp_signal[new_enable_start:enable_start] = (raw_segment - segment_mean) / segment_std
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return normalized_resp_signal
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