About This Architecture

Hybrid CNN-BiLSTM-Attention architecture processes multi-channel physiological time series for binary sleep apnea detection across three sequential phases. Phase 1 extracts intra-epoch spatial features using TimeDistributed 1D CNN with two convolutional blocks (32 and 64 filters) followed by global average pooling. Phase 2 models inter-epoch temporal dependencies via bidirectional LSTM with 192 units and 0.3 dropout, while Phase 3 applies attention mechanism and dense classification layers with sigmoid output. The pipeline employs focal loss with class imbalance handling (γ=2.0, α=0.65), L2 regularization (1e-4), Adam optimizer with cosine decay learning rate, and gradient clipping for stable training. Fork this architecture on Diagrams.so to customize layer dimensions, experiment with attention variants, or adapt the pipeline for other biomedical sequence classification tasks like arrhythmia detection or seizure prediction.

People also ask

How do you combine CNN and BiLSTM with attention mechanism for sleep apnea classification from physiological signals?

Use a three-phase pipeline: TimeDistributed 1D CNN extracts intra-epoch spatial features from multi-channel input, bidirectional LSTM models inter-epoch temporal dependencies, and attention mechanism weights relevant time steps before dense classification layers with focal loss for class imbalance.