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| """
座舱纺织传感器生理信号质量评估系统
基于: Frontiers in Physiology, 2026
适用场景:
- 航空座舱: 飞行员缺氧/G力监测
- 汽车座舱: 驾驶员生理监测（借鉴纺织传感器方案）
依赖:
pip install numpy scipy scikit-learn matplotlib
"""
import numpy as np
from scipy.signal import butter, filtfilt, find_peaks
from scipy.stats import kurtosis, skew
from typing import Tuple, Dict, List
from dataclasses import dataclass
from enum import Enum
class SignalQuality(Enum):
EXCELLENT = 4
GOOD = 3
MODERATE = 2
POOR = 1
INVALID = 0
@dataclass
class SignalMetrics:
"""信号质量指标"""
snr_db: float
artifact_ratio: float
coverage: float
quality: SignalQuality
notes: str = ""
class TextileECGProcessor:
"""纺织 ECG 信号处理器"""
def __init__(self, sample_rate: int = 256):
self.fs = sample_rate
def assess_quality(self, ecg: np.ndarray,
window_sec: float = 10.0) -> SignalMetrics:
"""
评估 ECG 信号质量（滑动窗口）
Args:
ecg: ECG 信号
window_sec: 窗口大小（秒）
Returns:
metrics: 信号质量指标
"""
window = int(window_sec * self.fs)
n_windows = len(ecg) // window
snr_list = []
artifact_list = []
coverage_list = []
for w in range(n_windows):
segment = ecg[w*window:(w+1)*window]
filtered = self._bandpass(segment, 0.5, 40)
peaks, props = find_peaks(
np.abs(filtered),
height=0.3*np.max(np.abs(filtered)),
distance=0.6*self.fs
)
if len(peaks) > 0:
rr = np.diff(peaks) / self.fs
hr = 60 / np.mean(rr) if len(rr) > 0 else 0
if 30 <= hr <= 200:
coverage_list.append(1.0)
else:
coverage_list.append(0.0)
else:
coverage_list.append(0.0)
hr = 0
if len(peaks) > 2:
signal_power = np.mean(filtered[peaks] ** 2)
mask = np.zeros(len(filtered), dtype=bool)
for p in peaks:
lo = max(0, p - int(0.1*self.fs))
hi = min(len(filtered), p + int(0.1*self.fs))
mask[lo:hi] = True
noise_power = np.mean(filtered[~mask] ** 2) + 1e-8
snr = 10 * np.log10(signal_power / noise_power)
else:
snr = -10
snr_list.append(snr)
fft = np.abs(np.fft.rfft(segment))
freqs = np.fft.rfftfreq(len(segment), d=1/self.fs)
hf_mask = freqs > 40
lf_mask = freqs <= 40
hf_energy = np.sum(fft[hf_mask]**2)
lf_energy = np.sum(fft[lf_mask]**2)
artifact_ratio = hf_energy / (hf_energy + lf_energy + 1e-8)
artifact_list.append(artifact_ratio)
avg_snr = np.mean(snr_list)
avg_artifact = np.mean(artifact_list)
avg_coverage = np.mean(coverage_list)
if avg_snr > 10 and avg_coverage > 0.9:
quality = SignalQuality.EXCELLENT
notes = "信号质量优秀，适合分析"
elif avg_snr > 6 and avg_coverage > 0.8:
quality = SignalQuality.GOOD
notes = "信号质量良好"
elif avg_snr > 3 and avg_coverage > 0.6:
quality = SignalQuality.MODERATE
notes = "信号质量中等，需滤波处理"
elif avg_snr > 0:
quality = SignalQuality.POOR
notes = "信号质量差，谨慎使用"
else:
quality = SignalQuality.INVALID
notes = "信号无效"
return SignalMetrics(
snr_db=float(avg_snr),
artifact_ratio=float(avg_artifact),
coverage=float(avg_coverage),
quality=quality,
notes=notes
)
def _bandpass(self, signal, low, high):
nyq = self.fs / 2
b, a = butter(4, [low/nyq, high/nyq], btype='band')
return filtfilt(b, a, signal)
class TextileRSPProcessor:
"""纺织呼吸信号处理器"""
def __init__(self, sample_rate: int = 128):
self.fs = sample_rate
def assess_quality(self, rsp: np.ndarray,
window_sec: float = 10.0) -> SignalMetrics:
"""评估呼吸信号质量"""
window = int(window_sec * self.fs)
n_windows = len(rsp) // window
snr_list = []
coverage_list = []
artifact_list = []
for w in range(n_windows):
segment = rsp[w*window:(w+1)*window]
filtered = self._bandpass(segment, 0.1, 0.5)
peaks, _ = find_peaks(
filtered,
distance=int(1.5 * self.fs),
height=0.2*np.max(np.abs(filtered)) if np.max(np.abs(filtered)) > 0 else 0
)
if len(peaks) > 1:
rr = np.diff(peaks) / self.fs
breath_rate = 60 / np.mean(rr)
if 6 <= breath_rate <= 40:
coverage_list.append(1.0)
else:
coverage_list.append(0.0)
else:
coverage_list.append(0.0)
if len(peaks) > 1:
signal_power = np.mean(filtered[peaks] ** 2)
mask = np.zeros(len(filtered), dtype=bool)
for p in peaks:
lo = max(0, p - int(0.5*self.fs))
hi = min(len(filtered), p + int(0.5*self.fs))
mask[lo:hi] = True
noise_power = np.mean(filtered[~mask] ** 2) + 1e-8
snr = 10 * np.log10(signal_power / noise_power)
else:
snr = -5
snr_list.append(snr)
diff = np.abs(np.diff(segment))
artifact_ratio = np.sum(diff > 3*np.std(diff)) / len(diff)
artifact_list.append(artifact_ratio)
avg_snr = np.mean(snr_list)
avg_coverage = np.mean(coverage_list)
avg_artifact = np.mean(artifact_list)
if avg_snr > 6 and avg_coverage > 0.8:
quality = SignalQuality.EXCELLENT
elif avg_snr > 3 and avg_coverage > 0.7:
quality = SignalQuality.GOOD
elif avg_snr > 1:
quality = SignalQuality.MODERATE
elif avg_snr > -2:
quality = SignalQuality.POOR
else:
quality = SignalQuality.INVALID
return SignalMetrics(
snr_db=float(avg_snr),
artifact_ratio=float(avg_artifact),
coverage=float(avg_coverage),
quality=quality,
notes=f"呼吸 SNR={avg_snr:.1f}dB, 覆盖={avg_coverage:.0%}"
)
def _bandpass(self, signal, low, high):
nyq = self.fs / 2
b, a = butter(4, [low/nyq, high/nyq], btype='band')
return filtfilt(b, a, signal)
class ExtremConditionSimulator:
"""极端条件生理信号仿真"""
def __init__(self, ecg_fs=256, rsp_fs=128):
self.ecg_fs = ecg_fs
self.rsp_fs = rsp_fs
def generate_hypoxia_ecg(self, duration_sec=60, severity='moderate'):
"""
模拟缺氧 ECG
缺氧: 心率↑, HRV↓, ST段可能压低
"""
t = np.arange(0, duration_sec, 1/self.ecg_fs)
if severity == 'mild':
hr = 90
hrv_std = 0.05
elif severity == 'moderate':
hr = 110
hrv_std = 0.03
else:
hr = 140
hrv_std = 0.015
rr_mean = 60 / hr
rr_intervals = np.random.normal(rr_mean, hrv_std, int(duration_sec / rr_mean))
ecg = np.zeros(len(t))
r_times = np.cumsum(rr_intervals)
for r_time in r_times:
idx = int(r_time * self.ecg_fs)
if 0 <= idx < len(ecg):
ecg[idx] = 1.5
if idx > 5:
ecg[idx-5] = -0.1
if idx + 8 < len(ecg):
ecg[idx+8] = -0.3
t_idx = idx + int(0.3 * self.ecg_fs)
if t_idx < len(ecg):
ecg[t_idx] = 0.3
noise = np.random.normal(0, 0.05, len(ecg))
artifact = 0.1 * np.sin(2*np.pi*2*t) * (np.random.random(len(t)) > 0.7)
return ecg + noise + artifact
def generate_gforce_ecg(self, duration_sec=60, max_g=7):
"""
模拟高 G 力 ECG
G力: 心率↑↑, HRV↓↓, T波可能改变
"""
t = np.arange(0, duration_sec, 1/self.ecg_fs)
g_profile = np.where(t < 10, 1, 1 + (max_g-1) * np.minimum((t-10)/20, 1))
hr = 70 + (g_profile - 1) * 12
ecg = np.zeros(len(t))
prev_r = 0
for i in range(1, len(t)):
if t[i] - prev_r >= 60 / hr[i]:
idx = i
ecg[idx] = 1.5 + 0.05 * g_profile[idx]
if idx > 5:
ecg[idx-5] = -0.1
if idx + 8 < len(ecg):
ecg[idx+8] = -0.3
t_idx = idx + int(0.3 * self.ecg_fs)
if t_idx < len(ecg):
ecg[t_idx] = 0.3 + 0.02 * g_profile[idx]
prev_r = t[i]
noise = np.random.normal(0, 0.08, len(ecg))
artifact = 0.15 * np.random.normal(0, 1, len(ecg)) * (g_profile > 3)
return ecg + noise + artifact
if __name__ == "__main__":
print("=" * 70)
print("座舱纺织传感器生理信号质量评估")
print("论文: Frontiers in Physiology, 2026")
print("=" * 70)
sim = ExtremConditionSimulator()
ecg_proc = TextileECGProcessor(sample_rate=256)
rsp_proc = TextileRSPProcessor(sample_rate=128)
print("\n=== 低压缺氧条件 ===")
for severity in ['mild', 'moderate', 'severe']:
ecg = sim.generate_hypoxia_ecg(duration_sec=30, severity=severity)
metrics = ecg_proc.assess_quality(ecg, window_sec=5)
print(f" {severity:8s}: SNR={metrics.snr_db:.1f}dB, "
f"覆盖={metrics.coverage:.0%}, "
f"伪迹={metrics.artifact_ratio:.1%}, "
f"质量={metrics.quality.name}")
print("\n=== 高 G 力条件 ===")
for g in [3, 5, 7, 9]:
ecg = sim.generate_gforce_ecg(duration_sec=30, max_g=g)
metrics = ecg_proc.assess_quality(ecg, window_sec=5)
print(f" {g}G: SNR={metrics.snr_db:.1f}dB, "
f"覆盖={metrics.coverage:.0%}, "
f"伪迹={metrics.artifact_ratio:.1%}, "
f"质量={metrics.quality.name}")
print(f"\n=== 论文报告数据 ===")
print(f"{'条件':<15s} {'ECG有效率':>10s} {'呼吸有效率':>12s}")
print(f"{'低压缺氧':<15s} {'93.4%':>10s} {'70.7%':>12s}")
print(f"{'高 G 力':<15s} {'90.4%':>10s} {'81.3%':>12s}")
print(f"\n=== 汽车座舱类比 ===")
print(f"{'场景':<20s} {'等效航空条件':>15s} {'ECG预期':>10s}")
print(f"{'正常驾驶':<20s} {'巡航':>15s} {'95%+':>10s}")
print(f"{'激烈驾驶':<20s} {'中等G力':>15s} {'88%+':>10s}")
print(f"{'高原驾驶(3000m)':<20s} {'轻度缺氧':>15s} {'90%+':>10s}")
print(f"{'疲劳驾驶':<20s} {'缺氧+低G':>15s} {'85%+':>10s}")
|