76 lines
2.9 KiB
Python
76 lines
2.9 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 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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def compute_interval():
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time_interval = 1/fs_ecg
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# 获取各特征的时间点
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P_onsets_time = np.array(P_onsets) * time_interval
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P_peaks_time = np.array(P_peaks) * time_interval
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P_offsets_time = np.array(P_offsets) * time_interval
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Q_peaks_time = np.array(Q_peaks) * time_interval
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R_peaks_time = np.array(R_peaks) * time_interval
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S_peaks_time = np.array(S_peaks) * time_interval
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T_onsets_time = np.array(T_onsets) * time_interval
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T_peaks_time = np.array(T_peaks) * time_interval
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T_offsets_time = np.array(T_offsets) * time_interval
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# 创建一个字典来存储所有的特征点
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features = {
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"P_onset": P_onsets_time,
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"P": P_peaks_time,
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"P_offset": P_offsets_time,
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"Q": Q_peaks_time,
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"R": R_peaks_time,
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"S": S_peaks_time,
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"T_onset": T_onsets_time,
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"T": T_peaks_time,
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"T_offset": T_offsets_time
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}
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# 遍历 features 字典并打印每个键对应的时间序列长度(用于测试)
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for label, times in features.items():
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print(f"{label}: {len(times)}")
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# 计算每对特征点之间的时间间隔
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intervals_ecg = {}
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for start_label, start_times in features.items():
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for end_label, end_times in features.items():
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# 避免重复计算相同的比值
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if start_label != end_label:
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interval_label = f"{start_label}-{end_label}"
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intervals_ecg[interval_label] = end_times - start_times
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# 单独计算P_onset-P_offset/T_onset-T_offset
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ratio_pp_tt = [p / t for p, t in zip(intervals_ecg['P_onset-P_offset'], intervals_ecg['T_onset-T_offset'])]
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# 输出间隔时间
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# for key, value in intervals_ecg.items():
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# print(f"{key} 的间隔时间：", value)
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return intervals_ecg, ratio_pp_tt
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if __name__ == "__main__":
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compute_interval() |