基礎(chǔ)知識之血氧傳感器
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1.什么是血氧傳感器？
血氧傳感器是一種用于測量人體血液中氧氣飽和度的設(shè)備。它通過非侵入性或微創(chuàng)性的方式獲取血氧水平的相關(guān)數(shù)據(jù)。血氧傳感器通常使用光學(xué)原理來工作。
本文引用地址：http://butianyuan.cn/article/202403/456426.htm血氧傳感器中最常見的類型是脈搏血氧飽和度（SpO2）傳感器，也被稱為脈搏血氧傳感器。SpO2傳感器利用光的吸收特性來測量血紅蛋白的氧合程度。它通過發(fā)射兩種不同波長的光（通常是紅光和紅外光）經(jīng)過皮膚照射到血液中，然后通過相應(yīng)的光電傳感器測量經(jīng)過皮膚反射回來的光的強(qiáng)度。根據(jù)紅光和紅外光的吸收差異，可以計算出血液中氧氣的飽和度。
人體需要并調(diào)節(jié)血液中氧氣的非常精確和特定的平衡。人體的正常動脈血氧飽和度（SpO2）為97-100％，或96-99%。如果該水平低于90％，則被認(rèn)為是低氧血癥。動脈血氧水平低于80％可能會損害器官功能，例如大腦和心臟，應(yīng)及時解決。持續(xù)的低氧水平可能導(dǎo)致呼吸或心臟驟停。
血氧傳感器常見于醫(yī)療領(lǐng)域，特別是在監(jiān)護(hù)設(shè)備、手持式脈搏氧飽和度儀和睡眠呼吸監(jiān)測等應(yīng)用中。此外，它們也逐漸應(yīng)用于個人健康監(jiān)測設(shè)備，如智能手環(huán)、智能手表等。
圖1：max30102光電式心率血氧傳感器
2. 血氧傳感器是如何工作的？
血氧傳感器通常使用光學(xué)原理來測量血紅蛋白的氧合程度，其中最常見的類型是脈搏氧血飽和度（SpO2）傳感器。
圖2:光學(xué)原理
以下是血氧傳感器的工作原理：
血氧傳感器只能提供間接的血氧飽和度測量結(jié)果，并且有一定的誤差范圍。其他因素如溫度、燈光干擾、運(yùn)動等也可能對測量結(jié)果產(chǎn)生影響。
3. 如何應(yīng)用血氧傳感器？
1 臨床監(jiān)護(hù)：血氧傳感器常用于臨床監(jiān)護(hù)中，例如在手術(shù)室、急診室和重癥監(jiān)護(hù)病房。通過監(jiān)測患者的血氧飽和度（SPO2）水平，醫(yī)護(hù)人員可以實(shí)時了解患者的氧氣供應(yīng)情況，及時發(fā)現(xiàn)并處理低氧血癥或窒息等問題，確保患者的安全。
2 睡眠呼吸監(jiān)測：血氧傳感器被廣泛應(yīng)用于睡眠呼吸監(jiān)測領(lǐng)域。睡眠時，佩戴血氧傳感器的設(shè)備（如脈搏氧飽和度儀）能夠監(jiān)測睡眠者的血氧水平。通過分析血氧飽和度數(shù)據(jù)，醫(yī)生或睡眠專家可以評估睡眠質(zhì)量、檢測睡眠呼吸暫停等呼吸障礙，并為患者提供相應(yīng)的治療建議。
3 慢性阻塞性肺疾?。–OPD）管理：COPD患者常使用血氧傳感器來管理他們的疾病。他們可以在家中使用脈搏氧飽和度儀來測量自己的血氧水平，并追蹤數(shù)據(jù)的變化。這有助于監(jiān)測疾病的進(jìn)展、評估治療效果，并及時采取相應(yīng)的措施，如調(diào)整藥物劑量或進(jìn)行氧療。
4 家庭健康監(jiān)測：現(xiàn)代的脈搏氧飽和度儀（Pulse Oximeter）通常集成了血氧傳感器，被廣泛應(yīng)用于家庭健康監(jiān)測場景。人們可以通過在家中使用這種設(shè)備來監(jiān)測自己或家人的血氧水平，以及心率等信息。這對于早期發(fā)現(xiàn)可能存在的呼吸系統(tǒng)問題、心血管疾病和睡眠呼吸暫停等狀況非常有幫助。此外，一些健康追蹤設(shè)備和智能手表也集成了血氧傳感器，能夠提供用戶的血氧飽和度數(shù)據(jù)，幫助用戶更好地了解自己的健康狀況。
5 高海拔登山：登山者在攀登高海拔地區(qū)時通常會面臨低氧環(huán)境。血氧傳感器被用于監(jiān)測登山者的血氧水平，幫助他們了解身體在高海拔環(huán)境下的氧氣供應(yīng)情況。這樣的信息可以幫助他們判斷是否需要停止攀登或采取其他適當(dāng)?shù)男袆觼肀苊飧呱讲〉葷撛陲L(fēng)險。
6 運(yùn)動訓(xùn)練和健身監(jiān)測：血氧傳感器在運(yùn)動訓(xùn)練和健身監(jiān)測中起著重要作用。運(yùn)動員可以使用集成了血氧傳感器的可穿戴設(shè)備，如智能手表或運(yùn)動耳機(jī)，來監(jiān)測他們的血氧水平和心率等數(shù)據(jù)。這些數(shù)據(jù)可以幫助運(yùn)動員和教練員了解身體在運(yùn)動過程中的氧氣攝取能力和運(yùn)動耐力水平，并進(jìn)行相應(yīng)的訓(xùn)練調(diào)整。此外，血氧傳感器還可以幫助跑步愛好者監(jiān)測自己的運(yùn)動表現(xiàn)，提供健身指導(dǎo)和優(yōu)化跑步計劃。
4. 主要的血氧傳感器供應(yīng)商
Maxim Integrated ：Maxim Integrated是一家知名的集成電路設(shè)計和生產(chǎn)公司，提供多種心率傳感器芯片和模塊。其中，MAX30102是一款常見的心率傳感器模塊，集成了紅外LED、紅光LED和光電傳感器，適用于便攜設(shè)備和健康監(jiān)測設(shè)備等應(yīng)用。
Texas Instruments（TI）：TI是一家全球領(lǐng)先的半導(dǎo)體公司，提供多款生物傳感器芯片和模塊。AFE4404是TI的一款心率監(jiān)測芯片，集成了紅外LED、綠光LED、光電傳感器和ADC等功能，可實(shí)現(xiàn)高精度的心率和血氧濃度測量。
Analog Devices(ADI)：ADI是一家知名的模擬與數(shù)字混合信號處理技術(shù)供應(yīng)商，提供多種生物傳感器芯片和模塊。AD8232是ADI的一款心率傳感器芯片，專為心電圖（ECG）采集設(shè)計，具備高性能和低功耗特點(diǎn)。
Nellcor (Medtronic)：Nellcor是Medtronic旗下的品牌，專注于提供高質(zhì)量的血氧傳感器芯片和模塊。他們的SpO2傳感器采用專有的信號處理算法，具有高靈敏度和抗干擾能力。Nellcor的血氧傳感器在醫(yī)療領(lǐng)域廣泛應(yīng)用，包括重癥監(jiān)護(hù)、手術(shù)室和急診等環(huán)境。
NXP Semiconductors：NXP Semiconductors是一家全球領(lǐng)先的半導(dǎo)體解決方案提供商，他們提供適用于醫(yī)療應(yīng)用的心率、血氧和心電圖傳感器芯片。
Silicon Labs：Silicon Labs是一家專注于集成電路解決方案的公司，他們提供用于生物傳感器應(yīng)用的芯片產(chǎn)品，包括心率、血氧和心電圖傳感器芯片。
5. 參考案例
在Thonny使用Mircopython編寫程序控制RP2040控制Max30102讀取心率數(shù)據(jù)
Max30102芯片介紹
MAX30102是一個集成的脈搏血氧計和心率監(jiān)測器模塊。它包括內(nèi)部LED、光電探測器、光學(xué)元件和具有環(huán)境光抑制功能的低噪聲電子器件。
MAX30102使用一個1.8V電源和一個用于內(nèi)部LED的獨(dú)立3.3V電源。通過標(biāo)準(zhǔn)I2C兼容接口進(jìn)行通信。該模塊可以通過零待機(jī)電流的軟件關(guān)閉，使電源導(dǎo)軌始終保持通電狀態(tài)。
電路連接
程序代碼
程序文件需要文件
前面兩個文件可在github上面下載https://github.com/n-elia/MAX30102-MicroPython-driver/tree/main/max30102
spo2cal.py程序如下
# -*-coding:utf-8 # 25 samples per second (in algorithm.h)
SAMPLE_FREQ = 25
# taking moving average of 4 samples when calculating HR # in algorithm.h, "DONOT CHANGE" comment is attached
MA_SIZE = 4
# sampling frequency * 4 (in algorithm.h)
BUFFER_SIZE = 100
# this assumes ir_data and red_data as np.array
def calc_hr_and_spo2(ir_data, red_data):
"""
By detecting peaks of PPG cycle and corresponding AC/DC
of red/infra-red signal, the an_ratio for the SPO2 is computed.
"""
# get dc mean
ir_mean = int(sum(ir_data) / len(ir_data)) # remove DC mean and inver signal # this lets peak detecter detect valley
x = [ir_mean - x for x in ir_data] # 4 point moving average # x is np.array with int values, so automatically casted to int
for i in range(len(x) - MA_SIZE):
x[i] = sum(x[i:i + MA_SIZE]) / MA_SIZE
# calculate threshold
n_th = int(sum(x) / len(x))
n_th = 30 if n_th < 30 else n_th # min allowed
n_th = 60 if n_th > 60 else n_th # max allowed
ir_valley_locs, n_peaks = find_peaks(x, BUFFER_SIZE, n_th, 4, 15)
# print(ir_valley_locs[:n_peaks], ",", end="")
peak_interval_sum = 0
if n_peaks >= 2:
for i in range(1, n_peaks):
peak_interval_sum += (ir_valley_locs[i] - ir_valley_locs[i - 1])
peak_interval_sum = int(peak_interval_sum / (n_peaks - 1))
hr = int(SAMPLE_FREQ * 60 / peak_interval_sum)
hr_valid = True else:
hr = -999 # unable to calculate because # of peaks are too small
hr_valid = False
# ---------spo2--------- # find precise min near ir_valley_locs (???)
exact_ir_valley_locs_count = n_peaks
# find ir-red DC and ir-red AC for SPO2 calibration ratio # find AC/DC maximum of raw
# FIXME: needed??
for i in range(exact_ir_valley_locs_count):
if ir_valley_locs[i] > BUFFER_SIZE:
spo2 = -999 # do not use SPO2 since valley loc is out of range
spo2_valid = False return hr, hr_valid, spo2, spo2_valid
i_ratio_count = 0
ratio = [] # find max between two valley locations # and use ratio between AC component of Ir and Red DC component of Ir and Red for SpO2
red_dc_max_index = -1
ir_dc_max_index = -1
for k in range(exact_ir_valley_locs_count - 1):
red_dc_max = -16777216
ir_dc_max = -16777216
if ir_valley_locs[k + 1] - ir_valley_locs[k] > 3:
for i in range(ir_valley_locs[k], ir_valley_locs[k + 1]):
if ir_data[i] > ir_dc_max:
ir_dc_max = ir_data[i]
ir_dc_max_index = i if red_data[i] > red_dc_max:
red_dc_max = red_data[i]
red_dc_max_index = i
red_ac = int((red_data[ir_valley_locs[k + 1]] - red_data[ir_valley_locs[k]]) * (red_dc_max_index - ir_valley_locs[k]))
red_ac = red_data[ir_valley_locs[k]] + int(red_ac / (ir_valley_locs[k + 1] - ir_valley_locs[k]))
red_ac = red_data[red_dc_max_index] - red_ac # subtract linear DC components from raw
ir_ac = int((ir_data[ir_valley_locs[k + 1]] - ir_data[ir_valley_locs[k]]) * (ir_dc_max_index - ir_valley_locs[k]))
ir_ac = ir_data[ir_valley_locs[k]] + int(ir_ac / (ir_valley_locs[k + 1] - ir_valley_locs[k]))
ir_ac = ir_data[ir_dc_max_index] - ir_ac # subtract linear DC components from raw
nume = red_ac * ir_dc_max
denom = ir_ac * red_dc_max if (denom > 0 and i_ratio_count < 5) and nume != 0:
# original cpp implementation uses overflow intentionally. # but at 64-bit OS, Pyhthon 3.X uses 64-bit int and nume*100/denom does not trigger overflow # so using bit operation ( &0xffffffff ) is needed
ratio.append(int(((nume * 100) & 0xffffffff) / denom))
i_ratio_count += 1 # choose median value since PPG signal may vary from beat to beat
ratio = sorted(ratio) # sort to ascending order
mid_index = int(i_ratio_count / 2)
ratio_ave = 0
if mid_index > 1:
ratio_ave = int((ratio[mid_index - 1] + ratio[mid_index]) / 2)
else:
if len(ratio) != 0:
ratio_ave = ratio[mid_index] # why 184?
# print("ratio average: ", ratio_ave)
if ratio_ave > 2 and ratio_ave < 184:
# -45.060 * ratioAverage * ratioAverage / 10000 + 30.354 * ratioAverage / 100 + 94.845
spo2 = -45.060 * (ratio_ave ** 2) / 10000.0 + 30.054 * ratio_ave / 100.0 + 94.845
spo2_valid = True else:
spo2 = -999
spo2_valid = False
return hr - 20, hr_valid, spo2, spo2_valid
def find_peaks(x, size, min_height, min_dist, max_num):
"""
Find at most MAX_NUM peaks above MIN_HEIGHT separated by at least MIN_DISTANCE
"""
ir_valley_locs, n_peaks = find_peaks_above_min_height(x, size, min_height, max_num)
ir_valley_locs, n_peaks = remove_close_peaks(n_peaks, ir_valley_locs, x, min_dist)
n_peaks = min([n_peaks, max_num]) return ir_valley_locs, n_peaks
def find_peaks_above_min_height(x, size, min_height, max_num):
"""
Find all peaks above MIN_HEIGHT
"""
i = 0
n_peaks = 0
ir_valley_locs = [] # [0 for i in range(max_num)]
while i < size - 1:
if x[i] > min_height and x[i] > x[i - 1]: # find the left edge of potential peaks
n_width = 1
# original condition i+n_width < size may cause IndexError # so I changed the condition to i+n_width < size - 1
while i + n_width < size - 1 and x[i] == x[i + n_width]: # find flat peaks
n_width += 1
if x[i] > x[i + n_width] and n_peaks < max_num: # find the right edge of peaks # ir_valley_locs[n_peaks] = i
ir_valley_locs.append(i)
n_peaks += 1 # original uses post increment
i += n_width + 1
else:
i += n_width else:
i += 1 return ir_valley_locs, n_peaks
def remove_close_peaks(n_peaks, ir_valley_locs, x, min_dist):
"""
Remove peaks separated by less than MIN_DISTANCE
""" # should be equal to maxim_sort_indices_descend # order peaks from large to small
# should ignore index:0
sorted_indices = sorted(ir_valley_locs, key=lambda i: x[i])
sorted_indices.reverse() # this "for" loop expression does not check finish condition # for i in range(-1, n_peaks):
i = -1
while i < n_peaks:
old_n_peaks = n_peaks
n_peaks = i + 1
# this "for" loop expression does not check finish condition # for j in (i + 1, old_n_peaks):
j = i + 1
while j < old_n_peaks:
n_dist = (sorted_indices[j] - sorted_indices[i]) if i != -1 else (sorted_indices[j] + 1) # lag-zero peak of autocorr is at index -1
if n_dist > min_dist or n_dist < -1 * min_dist:
sorted_indices[n_peaks] = sorted_indices[j]
n_peaks += 1 # original uses post increment
j += 1
i += 1
sorted_indices[:n_peaks] = sorted(sorted_indices[:n_peaks]) return sorted_indices, n_peaks
if __name__ == "__main__":
hr, hrb, sp, spb = calc_hr_and_spo2([12853, 15573, 15580, 15586, 15587, 15567, 15520, 15480, 15464, 15460, 15462, 15466, 15473, 15479, 15485, 15490, 15495, 15503, 15512, 15518, 15521, 15521, 15518, 15517, 15522, 15527, 15536, 15547, 15558, 15568, 15577, 15587, 15594, 15604, 15610, 15616, 15620, 15624, 15625, 15615, 15576, 15531, 15508, 15500, 15502, 15509, 15516, 15523, 15528, 15533, 15538, 15547, 15556, 15564, 15564, 15560, 15556, 15556, 15559, 15564, 15570, 15579, 15588, 15599, 15610, 15619, 15628, 15635, 15642, 15649, 15655, 15662, 15669, 15672, 15661, 15621, 15571, 15546, 15537, 15538, 15545, 15553, 15560, 15565, 15570, 15577, 15585, 15593, 15600, 15601, 15597, 15592, 15591, 15594, 15600, 15608, 15617, 15626, 15633, 15640], [12258, 14318, 14322, 14324, 14326, 14317, 14299, 14284, 14280, 14279, 14280, 14283, 14285, 14288, 14292, 14294, 14297, 14299, 14302, 14304, 14305, 14305, 14304, 14304, 14306, 14308, 14311, 14316, 14321, 14325, 14329, 14333, 14329, 14329, 14332, 14335, 14336, 14338, 14338, 14333, 14315, 14295, 14286, 14283, 14285, 14288, 14292, 14295, 14297, 14298, 14301, 14305, 14309, 14312, 14312, 14310, 14308, 14308, 14309, 14312, 14315, 14318, 14322, 14327, 14332, 14336, 14341, 14344, 14347, 14350, 14351, 14354, 14357, 14359, 14353, 14335, 14313, 14304, 14300, 14302, 14305, 14309, 14312, 14314, 14316, 14319, 14323, 14326, 14329, 14329, 14326, 14325, 14324, 14326, 14328, 14332, 14336, 14341, 14345, 14349])
print("hr detected:", hrb)
print("sp detected:", spb) if (hrb == True and hr != -999):
hr2 = int(hr)
print("Heart Rate : ", hr2)
if (spb == True and sp != -999):
sp2 = int(sp)
print("SPO2 : ", sp2)
HR_SpO2.py程序如下：
from machine import SoftI2C, Pin, Timer
from utime import ticks_diff, ticks_us
from max30102 import MAX30102, MAX30105_PULSE_AMP_MEDIUM
from spo2cal import calc_hr_and_spo2
BEATS = 0 # 存儲心率
FINGER_FLAG = False # 默認(rèn)表示未檢測到手指
SPO2 = 0 # 存儲血氧
TEMPERATURE = 0 # 存儲溫度
def display_info(t):
# 如果沒有檢測到手指，那么就不顯示 if FINGER_FLAG is False:
return
print('Heart Rate: ', BEATS, " SpO2：", SPO2, " Temperture：", TEMPERATURE)
def main():
global BEATS, FINGER_FLAG, SPO2, TEMPERATURE # 如果需要對全局變量修改，則需要global聲明
# 創(chuàng)建I2C對象(檢測MAX30102)
i2c = SoftI2C(sda=Pin(16), scl=Pin(17), freq=400000) # Fast: 400kHz, slow: 100kHz
# 創(chuàng)建傳感器對象
sensor = MAX30102(i2c=i2c) # 檢測是否有傳感器 if sensor.i2c_address not in i2c.scan():
print("沒有找到傳感器")
return
elif not (sensor.check_part_id()):
# 檢查傳感器是否兼容
print("檢測到的I2C設(shè)備不是MAX30102或者M(jìn)AX30105")
return
else:
print("傳感器已識別到") # 配置
sensor.setup_sensor()
sensor.set_sample_rate(400)
sensor.set_fifo_average(8)
sensor.set_active_leds_amplitude(MAX30105_PULSE_AMP_MEDIUM)
t_start = ticks_us() # Starting time of the acquisition
MAX_HISTORY = 32
history = []
beats_history = []
beat = False
red_list = []
ir_list = [] while True:
sensor.check()
if sensor.available():
# FIFO 先進(jìn)先出，從隊(duì)列中取數(shù)據(jù)。都是整形int
red_reading = sensor.pop_red_from_storage()
ir_reading = sensor.pop_ir_from_storage() if red_reading < 1000:
print('No finger')
FINGER_FLAG = False # 表示沒有放手指 continue
else:
FINGER_FLAG = True # 表示手指已放
# 計算心率
history.append(red_reading) # 為了防止列表過大，這里取列表的后32個元素
history = history[-MAX_HISTORY:] # 提取必要數(shù)據(jù)
minima, maxima = min(history), max(history)
threshold_on = (minima + maxima * 3) // 4 # 3/4
threshold_off = (minima + maxima) // 2 # 1/2 if not beat and red_reading > threshold_on:
beat = True
t_us = ticks_diff(ticks_us(), t_start)
t_s = t_us/1000000
f = 1/t_s
bpm = f * 60
if bpm < 500:
t_start = ticks_us()
beats_history.append(bpm)
beats_history = beats_history[-MAX_HISTORY:] # 只保留最大30個元素數(shù)據(jù)
BEATS = round(sum(beats_history)/len(beats_history), 2) # 四舍五入 if beat and red_reading < threshold_off:
beat = False
# 計算血氧
red_list.append(red_reading)
ir_list.append(ir_reading)
# 最多 只保留最新的100個
red_list = red_list[-100:]
ir_list = ir_list[-100:]
# 計算血氧值 if len(red_list) == 100 and len(ir_list) == 100:
hr, hrb, sp, spb = calc_hr_and_spo2(red_list, ir_list)
if hrb is True and spb is True:
if sp != -999:
SPO2 = int(sp) # 計算溫度
TEMPERATURE = sensor.read_temperature()
if __name__ == '__main__':
tim = Timer(period=1000, mode=Timer.PERIODIC, callback=display_info)
main()
運(yùn)行效果
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