Add PPG signal processing and SpO2 calculation modules
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667bdc8213
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PPG2SpO2.py
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114
PPG2SpO2.py
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from pathlib import Path
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import numpy as np
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import pandas as pd
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from matplotlib import pyplot as plt
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import os
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os.environ["DISPLAY"] = "localhost:10.0"
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from func import calculate_hr_spo2_zhihu, ppg2spo2_pipeline, func_1
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from spo2_pipeline import spo2_pipeline
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def read_data(file_path):
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"""
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Read data from a file and return it as a list of floats.
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Args:
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file_path (str or Path): The path to the file to read.
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Returns:
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list of float: The data read from the file.
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"""
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df = pd.read_csv(file_path)
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# red = df['red'].values[300:]
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# ir = df['ied'].values[300:]
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red = df['red'].values[50:]
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ir = df['ied'].values[50:]
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return red, ir
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def read_labels(file_path):
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"""
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Read labels from a file and return them as a list of integers.
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Args:
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file_path (str or Path): The path to the file to read.
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Returns:
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list of int: The labels read from the file.
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"""
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df = pd.read_csv(file_path)
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timestamp = df['timestamp'].values
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hr_label = df['心率_值'].values
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spo2_label = df['血氧组_值'].values.astype(float)
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r_value = df['R组_均方根'].values
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np.place(spo2_label, spo2_label == 0, np.nan)
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return timestamp, hr_label, spo2_label, r_value
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def draw_compare(red_signal, ir_signal, spo2_values, hr_values, r_values, fs=25):
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"""
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Draw comparison plots for red and IR signals along with SpO2 values.
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Args:
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red_signal (array-like): The red light signal data.
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ir_signal (array-like): The infrared light signal data.
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spo2_values (array-like): The computed SpO2 values.
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fs (int): Sampling frequency in Hz. Default is 25.
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"""
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time_axis = [i / fs for i in range(len(red_signal))]
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plt.figure(figsize=(15, 10))
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plt.subplot(3, 1, 1)
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plt.plot(time_axis, red_signal, label='Red Signal', color='red')
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plt.plot(time_axis, ir_signal, label='IR Signal', color='blue')
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plt.title('Red and IR Signals')
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plt.xlabel('Time (s)')
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plt.ylabel('Amplitude')
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plt.legend()
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plt.subplot(3, 1, 2)
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plt.plot(spo2_values, label='Computed SpO2', color='green')
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plt.title('Computed SpO2 Values')
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plt.xlabel('Beats')
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plt.ylabel('SpO2 (%)')
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plt.legend()
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plt.subplot(3, 1, 3)
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plt.plot(hr_values, label='Computed Heart Rate', color='orange')
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plt.title('Computed Heart Rate Values')
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plt.xlabel('Beats')
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plt.ylabel('Heart Rate (bpm)')
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plt.legend()
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plt.tight_layout()
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plt.show()
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if __name__ == '__main__':
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file_path = Path("./data/spo2hr[42-A3-C5-2F-F7-32]2025.11.17_17.12.15.csv")
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# file_path = "./data/spo2hr[42-A3-C5-2F-F7-32]2025.11.17_17.01.13.csv"
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red_signal, ir_signal = read_data(file_path)
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label_path = file_path.parent / "spo2_alg_info[42_A3_C5_2F_F7_32]2025.11.17_17.12.16.csv"
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timestamp, hr_label, spo2_label, r_value = read_labels(label_path)
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# draw_compare(red_signal, ir_signal, spo2_label, hr_label, r_value, fs=25)
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# df, summary = spo2_pipeline(
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# red_signal,
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# ir_signal,
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# fs=25,
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# calibrate_with_ref=spo2_label,
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# A=104.0,
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# B=17.0
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# )
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# print(summary)
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# print(df)
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# HR_result, SpO2_result = ppg2spo2_pipeline(red_signal, ir_signal, fs=25)
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func_1(red_signal, ir_signal, fs=25)
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243
func.py
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func.py
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import numpy as np
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import pandas as pd
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from scipy import signal
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from matplotlib import pyplot as plt
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def calculate_hr_spo2_zhihu(X, fs, FFT_size=512):
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"""
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X : ndarray, shape (N, 2) # 第0列 RED，第1列 IR
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fs : 采样率 (Hz)
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FFT_size : FFT 点数（建议 2 的幂）
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返回值 : (Heart_Rate, SpO2_Level) 已经取整
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https://zhuanlan.zhihu.com/p/658858641
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https://github.com/thinkng/ppgprocessing/blob/master/HR_SpO2_Estimation.m
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"""
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X = np.asarray(X)
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if X.ndim != 2 or X.shape[1] != 2:
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raise ValueError("X 必须是 (样本数, 2) 的数组，列0=RED，列1=IR")
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# 计算能处理的完整窗口数量（每个窗口长度 = FFT_size）
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step = fs # MATLAB 中是 n*fs 开始，步长为 fs（1 秒）
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n_windows = int((len(X) / (2 * fs)) - 2) # 与原 MATLAB 一致
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HEART_RATE = np.zeros(n_windows)
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SpO2 = np.zeros(n_windows)
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for n in range(n_windows):
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start_idx = n * step
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end_idx = start_idx + FFT_size
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y1 = X[start_idx:end_idx, 0] # RED
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y2 = X[start_idx:end_idx, 1] # IR
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# ---------- FFT RED ----------
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Y1 = np.fft.fft(y1, n=FFT_size)
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Y1_abs = np.abs(Y1[:FFT_size//2 + 1])
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f1 = fs / 2 * np.linspace(0, 1, FFT_size//2 + 1)
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# ---------- FFT IR ----------
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Y2 = np.fft.fft(y2, n=FFT_size)
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Y2_abs = np.abs(Y2[:FFT_size//2 + 1])
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f2 = f1.copy() # 与 f1 完全相同
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# ---------- 在 0.5~2.5 Hz（对应心率 30~150 bpm）范围内找局部最大 ----------
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# MATLAB 中索引 6:12 对应频率大约 0.6~1.4 Hz（取决于 FFT_size/fs）
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# 这里统一取频率 0.5~2.5 Hz 对应的索引
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idx_range = np.where((f1 >= 0.5) & (f1 <= 2.5))[0]
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# RED 峰值索引
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segment_red = Y1_abs[idx_range]
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local_max_i = np.argmax(segment_red)
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pk_RED_i = idx_range[local_max_i]
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# IR 峰值索引
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segment_ir = Y2_abs[idx_range]
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local_max_i = np.argmax(segment_ir)
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pk_IR_i = idx_range[local_max_i]
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# ---------- 心率 ----------
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heart_rate_bpm = f2[pk_IR_i] * 60
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HEART_RATE[n] = heart_rate_bpm
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# ---------- SpO2 ----------
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R_RED = Y1_abs[pk_RED_i] / (Y1_abs[0] + 1e-12) # 防止除以 0
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R_IR = Y2_abs[pk_IR_i] / (Y2_abs[0] + 1e-12)
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R = R_RED / R_IR
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spo2 = 104 - 28 * R
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SpO2[n] = spo2
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# 去掉首尾（与原 MATLAB 相同）
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if len(HEART_RATE) > 2:
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HR_mean = np.mean(HEART_RATE[1:-1])
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SpO2_mean = np.mean(SpO2[1:-1])
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else:
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HR_mean = np.mean(HEART_RATE)
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SpO2_mean = np.mean(SpO2)
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Heart_Rate = round(HR_mean)
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SpO2_Level = round(SpO2_mean)
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return Heart_Rate, SpO2_Level
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def _culculate_spo2(ir_list_data, red_list_data):
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ir_dc = min(ir_list_data)
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red_dc = min(red_list_data)
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ir_ac = max(ir_list_data) - ir_dc
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red_ac = max(red_list_data) - red_dc
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temp1 = ir_ac * red_dc
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if temp1 < 1:
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temp1 = 1
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R2 = (red_ac * ir_dc) / temp1
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SPO2 = -45.060 * R2 * R2 + 30.354 * R2 + 94.845
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if SPO2 > 100 or SPO2 < 0:
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SPO2 = 0
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return SPO2
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def _culculate_HR(ir_list_data_filtered, data_list_time):
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HR_num = signal.find_peaks(ir_list_data_filtered, distance=10)[0]
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time = data_list_time[-1] -data_list_time[0]
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HR = len(HR_num) / (time / 1000) * 60
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return HR
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def process_signal(signal_segment, fs, highpass=True):
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if highpass:
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h_b, h_a = signal.butter(N=8, Wn=1/(fs/2), btype="highpass", output="ba")
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data = signal.filtfilt(h_b, h_a, signal_segment, axis=0)
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else:
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data = signal_segment
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data = signal.detrend(data,
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axis=0,
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type='linear',
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bp=0,
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overwrite_data=False)
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return data
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def ppg2spo2_pipeline(red, ir, fs=25):
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"""
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采用滑窗分析法计算心率和血氧饱和度
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每秒中输出结果
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red : ndarray, 红光信号
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ir : ndarray, 红外光信号
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fs : 采样率 (Hz)
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"""
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red = np.asarray(red).reshape(-1)
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ir = np.asarray(ir).reshape(-1)
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if len(red) != len(ir):
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raise ValueError("红光和红外光信号长度必须相同")
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red_filtered = process_signal(red, fs, highpass=False)
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ir_filtered = process_signal(ir, fs, highpass=True)
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bpm_list_data = []
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spo2_list_data = []
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temp_bpm_list_data = []
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temp_spo2_list_data = []
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for i in range(len(red)//fs - 1):
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red_segment = red_filtered[i*fs:(i+1)*fs]
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ir_segment = ir_filtered[i*fs:(i+1)*fs]
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spo2 = _culculate_spo2(ir_segment, red_segment)
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bpm = _culculate_HR(ir_segment, np.arange(i*fs, (i+1)*fs) * (1000/fs))
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temp_bpm_list_data.append(bpm)
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temp_spo2_list_data.append(spo2)
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# matlab
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# python
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plt.figure(figsize=(10, 5))
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timestamp = np.linspace(0, len(red_filtered)/fs, len(red_filtered))
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ax1 = plt.subplot(211)
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plt.plot( timestamp, red, label='Red Signal', color='red', alpha=0.5)
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plt.plot(timestamp,ir, label='IR Signal', color='blue', alpha=0.5)
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plt.title('Raw PPG Signals')
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plt.xlabel("seconds")
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plt.ylabel('Amplitude')
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plt.legend()
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plt.subplot(212, sharex=ax1)
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plt.plot(timestamp,red_filtered, label='Filtered Red Signal', color='red', alpha=0.5)
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plt.plot(timestamp,ir_filtered, label='Filtered IR Signal', color='blue', alpha=0.5)
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plt.title('Filtered PPG Signals')
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plt.xlabel("seconds")
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plt.ylabel('Amplitude')
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plt.legend()
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plt.show()
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def func_1(red, ir, fs=25):
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# Processing_PPG_Signal
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# make a move window find min and max of ArrayIR
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def movmin1(A, k):
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x = A.rolling(k, min_periods=1, center=True).min().to_numpy() #
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return x
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def movmax1(A, k):
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x = A.rolling(k, min_periods=1, center=True).max().to_numpy()
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return x
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ArrayIR = pd.DataFrame(ir)
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ArrayRed = pd.DataFrame(red)
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# calculate ac/dc ir
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max_ir = movmax1(ArrayIR, fs)
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# print(f"max_ir: {max_ir}")
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min_ir = movmin1(ArrayIR, fs)
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# print(f"min_ir: {min_ir}")
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baseline_data_ir = (max_ir + min_ir) / 2
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# print(f"baseline_data_ir: {baseline_data_ir}")
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acDivDcIr = (max_ir - min_ir) / baseline_data_ir
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# calculate ac/dc red
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max_red = movmax1(ArrayRed, fs)
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min_red = movmin1(ArrayRed, fs)
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baseline_data_red = (max_red + min_red) / 2
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acDivDcRed = (max_red - min_red) / baseline_data_red
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# Plot SPO2 = 110-25*(ac/dc_red)/(ac/dc_ir)
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SPO2 = 110 - 25 * (acDivDcRed / acDivDcIr)
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# plt.figure("SPO2")
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timestamp = np.linspace(0, len(red) / fs, len(red))
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plt.figure(figsize=(10, 5))
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ax1 = plt.subplot(311)
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plt.plot(timestamp, red, label='Red Signal', color='red', alpha=0.5)
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plt.plot(timestamp, ir, label='IR Signal', color='blue', alpha=0.5)
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plt.title('Raw PPG Signals')
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plt.xlabel("seconds")
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plt.ylabel('Amplitude')
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plt.legend()
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plt.subplot(312, sharex=ax1)
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plt.plot(timestamp, red - baseline_data_red, label='Detrended Red Signal', color='red', alpha=0.5)
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plt.plot(timestamp, ir - baseline_data_ir, label='Detrended IR Signal', color='blue', alpha=0.5)
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plt.title('Detrended PPG Signals')
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plt.xlabel("seconds")
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plt.ylabel('Amplitude')
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plt.legend()
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plt.subplot(313, sharex=ax1)
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plt.plot(timestamp,acDivDcRed, label='AC/DC Red', color='red', alpha=0.5)
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plt.plot(timestamp,acDivDcIr, label='AC/DC IR', color='blue', alpha=0.5)
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plt.title('AC/DC Ratios')
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plt.xlabel("seconds")
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plt.ylabel('Ratio')
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plt.legend()
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plt.show()
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plt.xlabel("Samples")
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plt.ylabel("SPO2")
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plt.title("SPO2")
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plt.plot(timestamp, SPO2)
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plt.show()
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100
process_spo2_alg_info.py
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100
process_spo2_alg_info.py
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import re
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import pandas as pd
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from pathlib import Path
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from typing import List, Dict
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def parse_line(line: str) -> Dict:
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"""
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解析一行数据，返回一个字典
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例如输入:
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[300]心率:78,83,83,83,;标准差1:43.0491;标准差2:2.16506;均方根:82.2787;值:82;信任级别:1 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:1 R组:;均方根:0; 仪器:60
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[325]心率:75,78,83,83,;标准差1:38.6641;标准差2:3.4187;均方根:80.3232;值:81;信任级别:1 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:2 R组:;均方根:0; 仪器:60
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[350]心率:78,83,83,83,;标准差1:46.0893;标准差2:2.16506;均方根:82.2787;值:82;信任级别:1 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:3 R组:;均方根:0; 仪器:60
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[375]心率:83,83,83,107,;标准差1:43.8422;标准差2:10.3923;均方根:90.1047;值:82;信任级别:0 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:3 R组:;均方根:0; 仪器:60
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[400]心率:83,83,107,107,;标准差1:47.7501;标准差2:12;均方根:96.2549;值:82;信任级别:0 血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:3 R组:;均方根:0; 仪器:60
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"""
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line = line.strip()
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if not line:
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return None
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# 提取时间戳 [300] 这样的
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time_match = re.match(r'\[(\d+)\]', line)
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if not time_match:
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return None
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timestamp = int(time_match.group(1))
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# 去掉时间戳部分，后面全部是键值对
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content = line[time_match.end():]
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content = content.split(" ")
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result = {"timestamp": timestamp}
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for block in content:
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||||
parsed_block = parse_block(block)
|
||||
result.update(parsed_block)
|
||||
return result
|
||||
|
||||
|
||||
def parse_block(block: str) -> Dict:
|
||||
"""
|
||||
解析一个数据块，返回一个字典
|
||||
例如输入:
|
||||
心率:78,83,83,83,;标准差1:43.0491;标准差2:2.16506;均方根:82.2787;值:82;信任级别:1
|
||||
血氧组:;标准差1:0;标准差:0;均方根:0;值:0;信任级别:0结果延迟:1
|
||||
R组:;均方根:0;
|
||||
仪器:60
|
||||
"""
|
||||
data = {}
|
||||
parts = block.split(';')
|
||||
if len(parts) == 1:
|
||||
key, value = parts[0].split(':', 1)
|
||||
data[key.strip()] = value.strip()
|
||||
else:
|
||||
key_0 = parts[0].split(':', 1)[0].strip()
|
||||
for part in parts:
|
||||
if len(part.strip()) == 0:
|
||||
continue
|
||||
if "结果延迟" in part:
|
||||
part0, key1, value1 = part.partition('结果延迟:')
|
||||
data[f"{key_0}_结果延迟"] = value1.strip()
|
||||
key2, _, value2 = part0.partition(':')
|
||||
if key2 and value2:
|
||||
data[f"{key_0}_{key2.strip()}"] = value2.strip()
|
||||
else:
|
||||
key, value = part.split(':', 1)
|
||||
data[f"{key_0}_{key.strip()}"] = value.strip()
|
||||
|
||||
return data
|
||||
|
||||
|
||||
|
||||
def parse_dat_file(file_path) -> pd.DataFrame:
|
||||
"""
|
||||
读取整个 .dat 文件并返回 pandas DataFrame
|
||||
"""
|
||||
data = []
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
for line_num, line in enumerate(f, 1):
|
||||
parsed = parse_line(line)
|
||||
if parsed:
|
||||
data.append(parsed)
|
||||
else:
|
||||
if line.strip():
|
||||
print(f"第 {line_num} 行解析失败，已跳过: {line.strip()[:80]}...")
|
||||
|
||||
df = pd.DataFrame(data)
|
||||
|
||||
# 按时间戳排序（一般已经是顺序，但保险起见）
|
||||
if not df.empty:
|
||||
df = df.sort_values("timestamp").reset_index(drop=True)
|
||||
|
||||
return df
|
||||
|
||||
if __name__ == '__main__':
|
||||
# file_path = "./data/spo2_alg_info[42-A3-C5-2F-F7-32]2025.11.17_17.01.14.dat"
|
||||
file_path = "./data/spo2_alg_info[42_A3_C5_2F_F7_32]2025.11.17_17.12.16.dat"
|
||||
file_path = Path(file_path)
|
||||
df = parse_dat_file(file_path)
|
||||
|
||||
df.to_csv(file_path.with_suffix('.csv'), index=False, encoding='utf-8-sig')
|
||||
52
process_spo2_hr.py
Normal file
52
process_spo2_hr.py
Normal file
@ -0,0 +1,52 @@
|
||||
'''样例数据
|
||||
red:9188561;ied:10745056;accX:209;accY:-438;accZ:-169
|
||||
red:9185136;ied:10744763;accX:209;accY:-438;accZ:-171
|
||||
red:9182875;ied:10744312;accX:209;accY:-438;accZ:-171
|
||||
red:9181788;ied:10744212;accX:209;accY:-441;accZ:-163
|
||||
red:9180729;ied:10744341;accX:209;accY:-441;accZ:-163
|
||||
red:9179405;ied:10742269;accX:212;accY:-440;accZ:-172
|
||||
red:9178563;ied:10741796;accX:212;accY:-440;accZ:-172
|
||||
red:9178557;ied:10742843;accX:209;accY:-440;accZ:-166
|
||||
red:9178185;ied:10742777;accX:209;accY:-440;accZ:-166
|
||||
red:9177053;ied:10737766;accX:210;accY:-440;accZ:-166
|
||||
'''
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pathlib import Path
|
||||
def parse_dat_line(line: str) -> dict:
|
||||
"""
|
||||
解析一行 .dat 文件内容，返回一个字典
|
||||
例如输入:
|
||||
red:9188561;ied:10745056;accX:209;accY:-438;accZ:-169
|
||||
"""
|
||||
data = {}
|
||||
parts = line.strip().split(';')
|
||||
for part in parts:
|
||||
if len(part.strip()) == 0:
|
||||
continue
|
||||
key, value = part.split(':', 1)
|
||||
data[key.strip()] = int(value.strip())
|
||||
return data
|
||||
def parse_dat_file(file_path) -> pd.DataFrame:
|
||||
"""
|
||||
读取整个 .dat 文件并返回 pandas DataFrame
|
||||
:param file_path:
|
||||
:return:
|
||||
"""
|
||||
records = []
|
||||
with open(file_path, 'r', encoding='utf-8') as f:
|
||||
for line in f:
|
||||
parsed_line = parse_dat_line(line)
|
||||
records.append(parsed_line)
|
||||
df = pd.DataFrame(records)
|
||||
return df
|
||||
|
||||
if __name__ == '__main__':
|
||||
# file_path = './data/spo2hr[42-A3-C5-2F-F7-32]2025.11.17_17.01.13.dat'
|
||||
file_path = './data/spo2hr[42-A3-C5-2F-F7-32]2025.11.17_17.12.15.dat'
|
||||
file_path = Path(file_path)
|
||||
df = parse_dat_file(file_path)
|
||||
|
||||
|
||||
df.to_csv(file_path.with_suffix('.csv'), index=False)
|
||||
114
spo2_pipeline.py
Normal file
114
spo2_pipeline.py
Normal file
@ -0,0 +1,114 @@
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from scipy.signal import butter, filtfilt, find_peaks
|
||||
|
||||
def bandpass_filter(x, fs, low=0.5, high=5.0, order=3):
|
||||
nyq = 0.5 * fs
|
||||
lown = low / nyq
|
||||
highn = high / nyq
|
||||
b, a = butter(order, [lown, highn], btype='band')
|
||||
y = filtfilt(b, a, x)
|
||||
return y
|
||||
|
||||
def detrend(x):
|
||||
return x - np.mean(x)
|
||||
|
||||
def detect_peaks(signal, fs, hr_min=40, hr_max=220):
|
||||
min_dist = int(np.floor(fs * 60.0 / hr_max))
|
||||
prominence = 0.3 * np.std(signal)
|
||||
peaks, props = find_peaks(signal, distance=min_dist, prominence=prominence)
|
||||
return peaks, props
|
||||
|
||||
def compute_ac_dc_per_beat(signal, peaks, fs):
|
||||
n = len(signal)
|
||||
AC, DC, times, beat_windows = [], [], [], []
|
||||
for i, p in enumerate(peaks):
|
||||
if i == 0:
|
||||
start = 0
|
||||
else:
|
||||
start = (peaks[i-1] + p) // 2
|
||||
if i == len(peaks)-1:
|
||||
end = n-1
|
||||
else:
|
||||
end = (p + peaks[i+1]) // 2
|
||||
segment = signal[start:end+1]
|
||||
if segment.size < 3:
|
||||
AC.append(np.nan); DC.append(np.nan)
|
||||
times.append(p / fs)
|
||||
beat_windows.append((start, end))
|
||||
continue
|
||||
seg_max = np.max(segment)
|
||||
seg_min = np.min(segment)
|
||||
ac = (seg_max - seg_min) / 2.0
|
||||
dc = np.mean(segment)
|
||||
AC.append(ac); DC.append(dc)
|
||||
times.append(p / fs)
|
||||
beat_windows.append((start, end))
|
||||
return np.array(AC), np.array(DC), np.array(times), beat_windows
|
||||
|
||||
def compute_spo2_from_R(R, A=104.0, B=17.0):
|
||||
return A - B * R
|
||||
|
||||
def calibrate_linear(R_vals, spo2_ref):
|
||||
mask = ~np.isnan(R_vals) & ~np.isnan(spo2_ref)
|
||||
if mask.sum() < 2:
|
||||
return None
|
||||
coeffs = np.polyfit(R_vals[mask], spo2_ref[mask], 1)
|
||||
c1, c0 = coeffs[0], coeffs[1]
|
||||
A = c0; B = -c1
|
||||
return float(A), float(B)
|
||||
|
||||
def spo2_pipeline(red, ir, fs=25, calibrate_with_ref=None, A=104.0, B=17.0):
|
||||
assert red.shape == ir.shape, "red and ir must have same shape"
|
||||
red_f = bandpass_filter(red, fs)
|
||||
ir_f = bandpass_filter(ir, fs)
|
||||
red_d = detrend(red_f)
|
||||
ir_d = detrend(ir_f)
|
||||
peaks, props = detect_peaks(ir_d, fs)
|
||||
ac_red, dc_red, times, _ = compute_ac_dc_per_beat(red_d, peaks, fs)
|
||||
ac_ir, dc_ir, _, _ = compute_ac_dc_per_beat(ir_d, peaks, fs)
|
||||
|
||||
with np.errstate(divide='ignore', invalid='ignore'):
|
||||
ratio_red = ac_red / dc_red
|
||||
ratio_ir = ac_ir / dc_ir
|
||||
R = ratio_red / ratio_ir
|
||||
|
||||
spo2_vals = compute_spo2_from_R(R, A=A, B=B)
|
||||
used_A, used_B = A, B
|
||||
|
||||
# if calibrate_with_ref is not None:
|
||||
# if len(calibrate_with_ref) != len(R):
|
||||
# raise ValueError("Reference SpO2 length mismatch with beats.")
|
||||
# ref = np.array(calibrate_with_ref)
|
||||
# fit = calibrate_linear(R, ref)
|
||||
# if fit is not None:
|
||||
# used_A, used_B = fit
|
||||
# spo2_vals = compute_spo2_from_R(R, A=used_A, B=used_B)
|
||||
|
||||
df = pd.DataFrame({
|
||||
"time_s": times,
|
||||
"AC_red": ac_red,
|
||||
"DC_red": dc_red,
|
||||
"AC_ir": ac_ir,
|
||||
"DC_ir": dc_ir,
|
||||
"R": R,
|
||||
"SpO2": spo2_vals
|
||||
})
|
||||
|
||||
df['valid'] = (
|
||||
(~np.isnan(df['R'])) &
|
||||
(df['AC_red']>0) & (df['AC_ir']>0) &
|
||||
(df['DC_red']>0) & (df['DC_ir']>0) &
|
||||
(df['SpO2']>50) & (df['SpO2']<100)
|
||||
)
|
||||
|
||||
summary = {
|
||||
"n_beats": len(df),
|
||||
"n_valid_beats": int(df['valid'].sum()),
|
||||
"mean_spo2": float(np.nanmean(df['SpO2'][df['valid']])) if df['valid'].any() else float('nan'),
|
||||
"median_spo2": float(np.nanmedian(df['SpO2'][df['valid']])) if df['valid'].any() else float('nan'),
|
||||
"used_A": used_A,
|
||||
"used_B": used_B
|
||||
}
|
||||
return df, summary
|
||||
Loading…
Reference in New Issue
Block a user