Update HYS_config.yaml and HYS_process.py for signal processing parameters and add movement revision function

HYS_process.py

@@ -43,8 +43,8 @@ def process_one_signal(samp_id):
     label_path = list(label_path)[0]
     print(f"Processing Label_corrected file: {label_path}")
 
-    signal_data = utils.read_signal_txt(signal_path)
-    signal_length = len(signal_data)
+    signal_data_raw = utils.read_signal_txt(signal_path)
+    signal_length = len(signal_data_raw)
     print(f"signal_length: {signal_length}")
     signal_fs = int(signal_path.stem.split("_")[-1])
     print(f"signal_fs: {signal_fs}")
@@ -52,13 +52,13 @@ def process_one_signal(samp_id):
     print(f"signal_second: {signal_second}")
 
     # 根据采样率进行截断
-    signal_data = signal_data[:signal_second * signal_fs]
+    signal_data_raw = signal_data_raw[:signal_second * signal_fs]
 
     # 滤波
     # 50Hz陷波滤波器
     # signal_data = utils.butterworth(data=signal_data, _type="bandpass", low_cut=0.5, high_cut=45, order=10, sample_rate=signal_fs)
     print("Applying 50Hz notch filter...")
-    signal_data = utils.notch_filter(data=signal_data, notch_freq=50.0, quality_factor=30.0, sample_rate=signal_fs)
+    signal_data = utils.notch_filter(data=signal_data_raw, notch_freq=50.0, quality_factor=30.0, sample_rate=signal_fs)
 
     resp_data_0 = utils.butterworth(data=signal_data, _type="lowpass", low_cut=50, order=10, sample_rate=signal_fs)
     resp_fs = conf["resp"]["downsample_fs_1"]
@@ -130,6 +130,30 @@ def process_one_signal(samp_id):
         resp_movement_mask = None
         print("resp_movement_mask is None")
 
+    if resp_movement_mask is not None:
+        # 左右翻转resp_data
+        reverse_resp_data = resp_data[::-1]
+        _, resp_movement_mask_reverse, _, resp_movement_position_list_reverse = signal_method.detect_movement(
+            signal_data=reverse_resp_data,
+            sampling_rate=resp_fs,
+            **resp_movement_conf
+        )
+        print(f"resp_movement_mask_reverse_shape: {resp_movement_mask_reverse.shape}, num_movement_reverse: {np.sum(resp_movement_mask_reverse == 1)}, count_movement_positions_reverse: {len(resp_movement_position_list_reverse)}")
+        # 将resp_movement_mask_reverse翻转回来
+        resp_movement_mask_reverse = resp_movement_mask_reverse[::-1]
+    else:
+        resp_movement_mask_reverse = None
+        print("resp_movement_mask_reverse is None")
+
+
+    # 取交集
+    if resp_movement_mask is not None and resp_movement_mask_reverse is not None:
+        combined_resp_movement_mask = np.logical_and(resp_movement_mask, resp_movement_mask_reverse).astype(int)
+        resp_movement_mask = combined_resp_movement_mask
+        print(f"combined_resp_movement_mask_shape: {combined_resp_movement_mask.shape}, num_combined_movement: {np.sum(combined_resp_movement_mask == 1)}")
+    else:
+        print("combined_resp_movement_mask is None")
+
 
     # 分析Resp的幅值突变区间
     if resp_movement_mask is not None:
@@ -143,6 +167,7 @@ def process_one_signal(samp_id):
         print("amp_change_mask is None")
 
 
+
     # 分析Bcg的低幅值区间
     bcg_low_amp_conf = conf.get("bcg_low_amp", None)
     if bcg_low_amp_conf is not None:
@@ -182,6 +207,7 @@ def process_one_signal(samp_id):
     # 如果signal_data采样率过，进行降采样
     if signal_fs == 1000:
         signal_data = utils.downsample_signal_fast(original_signal=signal_data, original_fs=signal_fs, target_fs=100)
+        signal_data_raw = utils.downsample_signal_fast(original_signal=signal_data_raw, original_fs=signal_fs, target_fs=100)
         signal_fs = 100
 
     draw_tools.draw_signal_with_mask(samp_id=samp_id,
@@ -220,4 +246,4 @@ if __name__ == '__main__':
 
     all_samp_disable_df = utils.read_disable_excel(disable_df_path)
 
-    process_one_signal(select_ids[2])
+    process_one_signal(select_ids[5])

dataset_config/HYS_config.yaml

@@ -26,19 +26,19 @@ resp_low_amp:
   window_size_sec: 30
   stride_sec:
   amplitude_threshold: 3
-  merge_gap_sec: 180
-  min_duration_sec: 30
+  merge_gap_sec: 60
+  min_duration_sec: 60
 
 resp_movement:
   window_size_sec: 20
-  stride_sec: 5
-  std_median_multiplier: 5
+  stride_sec: 1
+  std_median_multiplier: 3.5
   compare_intervals_sec:
     - 60
     - 90
   interval_multiplier: 3.5
   merge_gap_sec: 30
-  min_duration_sec: 5
+  min_duration_sec: 2
 
 bcg:
     downsample_fs: 100
@@ -52,7 +52,7 @@ bcg_filter:
 bcg_low_amp:
   window_size_sec: 1
   stride_sec:
-  amplitude_threshold: 5
+  amplitude_threshold: 8
   merge_gap_sec: 20
   min_duration_sec: 3
 

signal_method/rule_base_event.py

@@ -168,6 +168,24 @@ def detect_movement(signal_data, sampling_rate, window_size_sec=2, stride_sec=No
 
 
 
+
+def movement_revise(signal_data, sampling_rate, movement_mask, std_median_multiplier=4.5):
+    """
+    基于标准差对已有体动掩码进行修正。 用于大尺度的体动检测后的位置修正
+
+    参数：
+    - signal_data: numpy array，输入的信号数据
+    - sampling_rate: int，信号的采样率（Hz）
+    - movement_mask: numpy array，已有的体动掩码（1表示体动，0表示睡眠）
+    - std_median_multiplier: float，标准差中位数的乘数阈值，默认值为 4.5
+
+    返回：
+    - revised_movement_mask: numpy array，修正后的体动掩码
+    """
+    pass
+
+
+
 @timing_decorator()
 def detect_low_amplitude_signal(signal_data, sampling_rate, window_size_sec=1, stride_sec=None,
                                 amplitude_threshold=50, merge_gap_sec=10, min_duration_sec=5):
@@ -394,6 +412,15 @@ def position_based_sleep_recognition_v2(signal_data, movement_mask, sampling_rat
     segment_average_amplitude = []
     segment_average_energy = []
 
+    signal_data_no_movement = signal_data.copy()
+    for start, end in zip(movement_start, movement_end):
+        signal_data_no_movement[start * sampling_rate:end * sampling_rate] = np.nan
+
+    # from matplotlib import pyplot as plt
+    # plt.plot(signal_data, alpha=0.3, color='gray')
+    # plt.plot(signal_data_no_movement, color='blue', linewidth=1)
+    # plt.show()
+
     for start, end in zip(valid_starts, valid_ends):
         start *= sampling_rate
         end *= sampling_rate
@@ -407,12 +434,12 @@ def position_based_sleep_recognition_v2(signal_data, movement_mask, sampling_rat
                         mav_calc_window_sec * sampling_rate)
 
         # 计算每个片段的幅值指标
-        mav = np.mean(
-            np.max(signal_data[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0)) - np.mean(
-            np.min(signal_data[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
+        mav = np.nanmean(
+            np.nanmax(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0)) - np.nanmean(
+            np.nanmin(signal_data_no_movement[start:end].reshape(-1, mav_calc_window_sec * sampling_rate), axis=0))
         segment_average_amplitude.append(mav)
 
-        energy = np.sum(np.abs(signal_data[start:end] ** 2))
+        energy = np.nansum(np.abs(signal_data_no_movement[start:end] ** 2))
         segment_average_energy.append(energy)
 
     position_changes = np.zeros(len(signal_data) // sampling_rate, dtype=int)