Implement oximetry FFT function for heart rate and SpO2 calculation

func.py

@@ -172,70 +172,67 @@ def ppg2spo2_pipeline(red, ir, fs=25):
 
 
 
+def oximetry_fft(red, ir, fs=25):
 
-def func_1(red, ir, fs=25):
-    # Processing_PPG_Signal
-    # make a move window find min and max of ArrayIR
-    def movmin1(A, k):
-        x = A.rolling(k, min_periods=1, center=True).min().to_numpy()  #
-        return x
+    fs = fs
+    n_fft = 128
+    window_size = fs // 2
 
-    def movmax1(A, k):
-        x = A.rolling(k, min_periods=1, center=True).max().to_numpy()
-        return x
+    ir_filtered = np.convolve(ir, np.ones(window_size)/window_size, mode='valid')
 
-    ArrayIR = pd.DataFrame(ir)
-    ArrayRed = pd.DataFrame(red)
+    segment_len = fs
+    peaks_idx = []
 
-    # calculate ac/dc ir
-    max_ir = movmax1(ArrayIR, fs)
-    # print(f"max_ir: {max_ir}")
-    min_ir = movmin1(ArrayIR, fs)
-    # print(f"min_ir: {min_ir}")
-    baseline_data_ir = (max_ir + min_ir) / 2
-    # print(f"baseline_data_ir: {baseline_data_ir}")
-    acDivDcIr = (max_ir - min_ir) / baseline_data_ir
+    for start in range(0, len(ir_filtered) - segment_len + 1, segment_len):
+        segment = ir_filtered[start:start + segment_len]
+        # 用 scipy.find_peaks 找局部最大值（这里只取最高峰）
+        pk, _ = signal.find_peaks(segment, height=None)
+        if len(pk) > 0:
+            # 取幅度最大的那个峰
+            best_pk = pk[np.argmax(segment[pk])]
+            peaks_idx.append(start + best_pk)
+    peaks_idx = np.array(peaks_idx)
 
-    # calculate ac/dc red
-    max_red = movmax1(ArrayRed, fs)
-    min_red = movmin1(ArrayRed, fs)
-    baseline_data_red = (max_red + min_red) / 2
-    acDivDcRed = (max_red - min_red) / baseline_data_red
+    peaks_idx = np.array(peaks_idx)
 
-    # Plot SPO2 = 110-25*(ac/dc_red)/(ac/dc_ir)
-    SPO2 = 110 - 25 * (acDivDcRed / acDivDcIr)
-    # plt.figure("SPO2")
-    timestamp = np.linspace(0, len(red) / fs, len(red))
-    plt.figure(figsize=(10, 5))
-    ax1 = plt.subplot(311)
-    plt.plot(timestamp, red, label='Red Signal', color='red', alpha=0.5)
-    plt.plot(timestamp, ir, label='IR Signal', color='blue', alpha=0.5)
-    plt.title('Raw PPG Signals')
-    plt.xlabel("seconds")
-    plt.ylabel('Amplitude')
-    plt.legend()
+    # 1.3 计算心率（跳过明显错误的相邻峰值间隔）
+    if len(peaks_idx) >= 2:
+        diffs = np.diff(peaks_idx)  # 相邻峰之间的采样点数
+        valid = diffs >= 10  # 过滤掉过于靠近的误检（原代码的 -10 条件）
+        beat_intervals_sec = diffs[valid] / fs  # 转为秒
+        HEART_RATE = 60.0 / beat_intervals_sec.mean()
+    else:
+        HEART_RATE = np.nan
+
+    print(f"Heart Rate: {HEART_RATE:.1f} bpm")
+
+    freqs = np.fft.rfftfreq(n_fft, d=1 / fs)
+    f_min = 0.7
+    f_max = 2.0
+    idx_range = np.where((freqs >= f_min) & (freqs <= f_max))[0]
+
+    Y1 = np.fft.rfft(red - red.mean(), n=n_fft)  # 去直流后再 FFT
+    mag1 = np.abs(Y1)
+
+    Y2 = np.fft.rfft(ir - ir.mean(), n=n_fft)
+    mag2 = np.abs(Y2)
+
+    peak_idx_red = idx_range[np.argmax(mag1[idx_range])]
+    peak_idx_ir = idx_range[np.argmax(mag2[idx_range])]
+
+    AC_red = mag1[peak_idx_red]
+    DC_red = mag1[0]
+    AC_ir = mag2[peak_idx_ir]
+    DC_ir = mag2[0]
+
+    R = (AC_red / DC_red) / (AC_ir / DC_ir)
+    SpO2 = 104 - 28 * R
+
+    SpO2 = np.clip(SpO2, 0, 100)
+
+    print(f"SpO2: {SpO2:.1f} %")
 
-    plt.subplot(312, sharex=ax1)
-    plt.plot(timestamp, red - baseline_data_red, label='Detrended Red Signal', color='red', alpha=0.5)
-    plt.plot(timestamp, ir - baseline_data_ir, label='Detrended IR Signal', color='blue', alpha=0.5)
-    plt.title('Detrended PPG Signals')
-    plt.xlabel("seconds")
-    plt.ylabel('Amplitude')
-    plt.legend()
 
-    plt.subplot(313, sharex=ax1)
-    plt.plot(timestamp,acDivDcRed, label='AC/DC Red', color='red', alpha=0.5)
-    plt.plot(timestamp,acDivDcIr, label='AC/DC IR', color='blue', alpha=0.5)
-    plt.title('AC/DC Ratios')
-    plt.xlabel("seconds")
-    plt.ylabel('Ratio')
-    plt.legend()
-    plt.show()
-    plt.xlabel("Samples")
-    plt.ylabel("SPO2")
-    plt.title("SPO2")
-    plt.plot(timestamp, SPO2)
-    plt.show()