125 lines
4.6 KiB
Python
125 lines
4.6 KiB
Python
import numpy as np
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from scipy.integrate import simps
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from processing import processing
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(signal_dwt , waves_dwt , rpeaks , ecg_signal,
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signal, signal.v, signal.fs, rows_to_extract, t_ecg, t_ppg,
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on_column_list, on_column_list_toArea, on_values, on_values_toArea,
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sp_column_list, sp_values, dn_column_list, dn_values, dp_column_list,
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dp_values, u_column_list, u_column_list_toArea, u_values, u_values_toArea,
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v_column_list, v_values, w_column_list, w_values, a_column_list, a_column_list_toArea,
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a_values, a_values_toArea, b_column_list, b_values, c_column_list, c_values,
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e_column_list, e_values, f_column_list, f_values,
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signal.ppg, signal.vpg, signal.apg, signal.jpg) = processing()
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from ppg_peaks_val import ppg_peaks_val
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(on, on_toArea, sp, dn, dp,
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on_values, on_toArea_values, sp_values, dn_values, dp_values,
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u, u_toArea, v, w,
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u_values, u_toArea_values, v_values, w_values,
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a, a_toArea, b, c, e, f,
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a_values, a_toArea_values, b_values, c_values, e_values, f_values) = ppg_peaks_val()
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def ppg_areas():
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# 计算'on'波的纵坐标平均值作为基线
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baseline_on = np.mean(on_toArea_values)
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# 初始化'on'波之间周期的面积列表
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ppg_areas_values = []
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# 遍历'on'波的索引，计算每个周期的面积
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for i in range(len(on_toArea) - 1):
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start_idx = on_toArea[i]
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end_idx = on_toArea[i + 1]
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# 提取当前周期的PPG信号
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current_ppg_segment = signal.ppg[start_idx:end_idx]
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# 创建时间向量
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t_segment = t_ppg[start_idx:end_idx]
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# 计算相对于基线的PPG信号
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baseline_segment = np.full_like(current_ppg_segment, baseline_on) # 创建一个与 current_ppg_segment 相同长度的数组，所有值都等于 baseline_on
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diff_signal = current_ppg_segment - baseline_segment
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# 使用辛普森法计算面积
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if len(diff_signal) == 0 or len(t_segment) == 0:
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continue # 跳过空的信号或时间片段
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area = simps(diff_signal, t_segment)
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ppg_areas_values.append(area)
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# print("ppg_areas:",ppg_areas_values)
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# 初始化'u'波之间周期的面积列表
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vpg_areas_values = []
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# 遍历'u'波的索引，计算每个周期的VPG信号相对于X轴的面积
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for i in range(len(u_toArea) - 1):
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start_idx = u_toArea[i]
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end_idx = u_toArea[i + 1]
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# 提取当前周期的VPG信号
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current_vpg_segment = signal.vpg[start_idx:end_idx]
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# 创建时间向量
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t_segment = t_ppg[start_idx:end_idx]
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# 使用辛普森法计算面积（相对于X轴，即y=0线）
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area = simps(current_vpg_segment, t_segment)
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vpg_areas_values.append(area)
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# print("vpg_areas:",vpg_areas_values)
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# 初始化'a'波之间周期的面积列表
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apg_areas_values = []
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# 遍历'a'波的索引，计算每个周期的APG信号相对于X轴的面积
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for i in range(len(a_toArea) - 1):
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start_idx = a_toArea[i]
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end_idx = a_toArea[i + 1]
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# 提取当前周期的APG信号
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current_apg_segment = signal.apg[start_idx:end_idx]
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# 创建时间向量
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t_segment = t_ppg[start_idx:end_idx]
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# 使用辛普森法计算面积（相对于X轴，即y=0线）
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area = simps(current_apg_segment, t_segment)
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apg_areas_values.append(area)
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# print("apg_areas:",apg_areas_values)
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# 定义包含所有区域值的字典
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areas_values = {
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"ppg_areas": ppg_areas_values,
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"vpg_areas": vpg_areas_values,
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"apg_areas": apg_areas_values,
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}
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# 计算比值并存储在新字典中
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ratios_areas = {}
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for start_label, start_values in areas_values.items():
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for end_label, end_values in areas_values.items():
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if start_label != end_label:
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ratio_label = f"{start_label}/{end_label}"
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# 计算对应位置的比值
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ratios = [end_val / start_val for start_val, end_val in zip(start_values, end_values)]
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ratios_areas[ratio_label] = ratios
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# 计算差值并存储在新字典中
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diffs_areas = {}
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for start_label, start_values in areas_values.items():
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for end_label, end_values in areas_values.items():
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if start_label != end_label:
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diff_label = f"{start_label}-{end_label}"
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# 计算对应位置的差值
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diffs = [end_val - start_val for start_val, end_val in zip(start_values, end_values)]
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diffs_areas[diff_label] = diffs
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return areas_values, ratios_areas, diffs_areas
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if __name__ == "__main__":
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ppg_areas()
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