Editing AI in Wearables (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''AFib detection from PPG signal using 1D CNN:'''
<syntaxhighlight lang="python">
import torch
import torch.nn as nn
import numpy as np
from scipy.signal import butter, filtfilt

def preprocess_ppg(signal: np.ndarray, fs: int = 50) -> np.ndarray:
    """Bandpass filter PPG signal and normalize."""
    b, a = butter(4, [0.5/(fs/2), 8/(fs/2)], btype='band')
    filtered = filtfilt(b, a, signal)
    return (filtered - filtered.mean()) / (filtered.std() + 1e-8)

class PPG_AFibDetector(nn.Module):
    """1D CNN for AFib detection from PPG signal windows."""
    def __init__(self, window_seconds=30, fs=50):
        super().__init__()
        self.conv_layers = nn.Sequential(
            # Multi-scale temporal convolutions
            nn.Conv1d(1, 32, kernel_size=5, padding=2), nn.BatchNorm1d(32), nn.ReLU(),
            nn.Conv1d(32, 64, kernel_size=5, padding=2), nn.BatchNorm1d(64), nn.ReLU(),
            nn.MaxPool1d(4),
            nn.Conv1d(64, 128, kernel_size=3, padding=1), nn.BatchNorm1d(128), nn.ReLU(),
            nn.Conv1d(128, 128, kernel_size=3, padding=1), nn.BatchNorm1d(128), nn.ReLU(),
            nn.MaxPool1d(4),
            nn.Conv1d(128, 256, kernel_size=3, padding=1), nn.BatchNorm1d(256), nn.ReLU(),
            nn.AdaptiveAvgPool1d(16)
        )
        self.classifier = nn.Sequential(
            nn.Linear(256 * 16, 256), nn.ReLU(), nn.Dropout(0.5),
            nn.Linear(256, 64), nn.ReLU(), nn.Dropout(0.3),
            nn.Linear(64, 1)  # Binary: AFib vs. normal sinus rhythm
        )

    def forward(self, x):  # x: (B, 1, T)
        feat = self.conv_layers(x).flatten(1)
        return self.classifier(feat)

# Additional HRV features (model inputs alongside raw PPG)
def extract_hrv_features(rr_intervals: np.ndarray) -> dict:
    """Extract heart rate variability features from RR intervals (ms)."""
    return {
        'rmssd': np.sqrt(np.mean(np.diff(rr_intervals)**2)),  # HRV
        'sdnn': np.std(rr_intervals),
        'pnn50': np.mean(np.abs(np.diff(rr_intervals)) > 50),  # AFib indicator
        'irregularity': np.std(np.diff(rr_intervals)),         # Key AFib feature
        'mean_rr': np.mean(rr_intervals)
    }

model = PPG_AFibDetector()
# Train on PhysioNet/CinC Challenge 2017: 8,528 short ECG/PPG recordings
# Labels: Normal, AFib, Other rhythm, Noise
</syntaxhighlight>

; Wearable health AI applications
: '''AFib detection''' → Apple Watch ECG (FDA cleared), Fitbit ECG, AliveCor KardiaMobile
: '''CGM + AI''' → Dexcom Clarity, Abbott LibreView; closed loop: Tandem Control-IQ
: '''Sleep''' → Oura Ring, WHOOP, Fitbit sleep staging
: '''Fall detection''' → Apple Watch, Samsung Galaxy Watch, Fall detection algos (FDA cleared)
: '''Seizure''' → Empatica Embrace2 (FDA De Novo cleared)
: '''General health''' → Apple HealthKit ML, Google Fitbit Health Studies
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