174 lines
6.3 KiB
Python
174 lines
6.3 KiB
Python
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import numpy as np
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from global_var import global_var_init
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cycle, fs_ecg, fs_ppg, record_name, record_name_csv = global_var_init()
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from ecg_time_interval import compute_interval
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(intervals_ecg, ratio_pp_tt)= compute_interval()
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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 ecg_peaks_val import ecg_peaks_val
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(P_peaks, Q_peaks, R_peaks, S_peaks, T_peaks,
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P_onsets, P_offsets, T_onsets, T_offsets,
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P_peaks_values, Q_peaks_values, R_peaks_values, S_peaks_values, T_peaks_values,
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P_onsets_values, P_offsets_values, T_onsets_values, T_offsets_values,
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PQ_baseline) = ecg_peaks_val()
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num = len(R_peaks)
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#--------------------------------------------------------------------------------------------------------
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#--------------------------------------------------------------------------------------------------------
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#--------------------------------------------------------------------------------------------------------
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#--------------------------------------------------------------------------------------------------------
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#----------------------------------- 计算面积 ----------------------------------------
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#--------------------------------------------------------------------------------------------------------
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#--------------------------------------------------------------------------------------------------------
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#--------------------------------------------------------------------------------------------------------
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#--------------------------------------------------------------------------------------------------------
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# 计算每个周期相对于基线的面积
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def ecg_areas_calculate():
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ecg_areas = []
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for i in range(num):
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# 获取当前周期的起点和终点，并将其转换为整数索引
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start = int(P_onsets[i])
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end = int(T_offsets[i])
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# 提取当前周期的ECG信号片段
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signal_segment = ecg_signal[start:end+1]
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# 提取当前周期的时间片段
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time_segment = t_ecg[start:end+1]
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# 创建一个与信号片段大小相同的基线片段
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baseline_segment = np.full_like(signal_segment.shape, PQ_baseline)
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# 计算相对于基线的信号
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relative_signal = signal_segment - baseline_segment
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# 相对于基线的信号进行积分，计算面积
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area = np.trapz(relative_signal, time_segment)
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# 将计算得到的面积添加到列表中
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ecg_areas.append(area)
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# 输出所有周期的面积列表
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# print("ecg_areas：", ecg_areas)
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# 计算P_areas
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P_areas = []
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for i in range(num):
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# 获取当前周期的起点和终点，并将其转换为整数索引
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start = int(P_onsets[i]) # 500 是采样率
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end = int(P_offsets[i]) # 500 是采样率
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# 提取当前周期的ECG信号片段
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signal_segment = ecg_signal[start:end+1]
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# 提取当前周期的时间片段
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time_segment = t_ecg[start:end+1]
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# 创建一个与信号片段大小相同的基线片段
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baseline_segment = np.full(signal_segment.shape, PQ_baseline)
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# 计算相对于基线的信号
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relative_signal = signal_segment - baseline_segment
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# 相对于基线的信号进行积分，计算面积
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area = np.trapz(relative_signal, time_segment)
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# 将计算得到的面积添加到列表中
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P_areas.append(area)
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# 输出所有周期的面积列表
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# print("P_areas:", P_areas)
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# 计算T_areas
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T_areas = []
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for i in range(num):
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# 获取当前周期的起点和终点，并将其转换为整数索引
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start = int(T_onsets[i]) # 500 是采样率
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end = int(T_offsets[i]) # 500 是采样率
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# 提取当前周期的ECG信号片段
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signal_segment = ecg_signal[start:end+1]
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# 提取当前周期的时间片段
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time_segment = t_ecg[start:end+1]
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# 创建一个与信号片段大小相同的基线片段
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baseline_segment = np.full(signal_segment.shape, PQ_baseline)
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# 计算相对于基线的信号
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relative_signal = signal_segment - baseline_segment
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# 相对于基线的信号进行积分，计算面积
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area = np.trapz(relative_signal, time_segment)
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# 将计算得到的面积添加到列表中
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T_areas.append(area)
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# 输出所有周期的面积列表
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# print("T_areas:", T_areas)
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# 计算QRS_areas
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QRS_areas = []
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for i in range(num):
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# 获取当前周期的起点和终点，并将其转换为整数索引
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start = int(Q_peaks[i])
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end = int(S_peaks[i])
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# 提取当前周期的ECG信号片段
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signal_segment = ecg_signal[start:end+1]
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# 提取当前周期的时间片段
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time_segment = t_ecg[start:end+1]
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# 创建一个与信号片段大小相同的基线片段
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baseline_segment = np.full(signal_segment.shape, PQ_baseline)
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# 计算相对于基线的信号
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relative_signal = signal_segment - baseline_segment
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# 相对于基线的信号进行积分，计算面积
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area = np.trapz(relative_signal, time_segment)
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# 将计算得到的面积添加到列表中
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QRS_areas.append(area)
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# 输出所有周期的面积列表
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# print("QRS_areas:", QRS_areas)
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# values = [ ecg_areas, P_areas, T_areas, QRS_areas]
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# # 遍历并打印每个列表的长度
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# for idx, values in enumerate(values):
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# print(f"List {idx+1}: {len(values)}")
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return ecg_areas, P_areas, T_areas, QRS_areas
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if __name__ == "__main__":
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ecg_areas_calculate() |