Adaptive Model Selection for Real-Time Heart Disease Detection
Overview
As a Research Assistant at North Carolina State University (under Prof. Zhishan Guo), I contributed to building and evaluating an Adaptive Model Selection (AMS) framework for real-time cardiovascular disease detection on wearable embedded hardware — targeting deployment on a Raspberry Pi 4.
The core problem: ECG inference latency is bounded by the patient’s instantaneous heart rate (higher HR = shorter beat deadline), but accuracy increases with a heavier model. A fixed-complexity model either misses deadlines at high heart rate or wastes capacity at low heart rate. Our AMS framework solves this by dynamically selecting from three model tiers at every beat window based on real-time HR.
The work was published as a research paper at an IEEE conference.
Publication: “Adaptive Model Selection for Real-Time Heart Disease Detection on Embedded Systems” (2nd author)
IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2025)
Task Definition
The system performs 5-class cardiac severity classification (severity levels 0–4) on single-lead ECG data in real-time. Rather than classifying individual disease types (the full dataset covers 72 disease categories), the system assigns a severity score to each heartbeat cycle — enabling timely alerts and continuous risk monitoring on a wearable without requiring a full diagnostic workup.
Dataset: PhysioNet 2021 Challenge — filtered to 22,359 single-label ECG recordings, split 64% training / 16% validation / 20% test.
Model Architecture
Each input segment contains β consecutive R–R cycles, each resampled to 256 samples. The model has two parallel branches fused via global attention:
ECG branch: A stem convolution → three Residual Blocks (channel widths 8β → 16β → 32β → 64β) each containing a Squeeze-and-Excitation (SE) unit for channel recalibration → adaptive average pooling to length α.
Period branch: The inter-beat period vector passes through a FC block (Linear → BatchNorm → ELU), mapping to the same 64β feature space.
Global Attention fusion: The flattened ECG features and period embedding are concatenated, fed to a two-layer attention module that produces a sigmoid mask modulating the ECG features — allowing the network to weight cycle regions by their rhythm context.
Output: The attended features pass through two FC layers to produce 5 logits (severity 0–4).
This architecture couples morphological feature extraction (ResBlocks + SE) with rhythm-aware re-weighting (period branch + global attention), kept compact for embedded deployment.
AMS Framework and Anytime CNN
Three model tiers share a common parameter-shared Anytime CNN backbone with early-exit heads:
- High HR (≥ 90 bpm): Lightweight exit — fastest path, 0.57 ms, handles tight deadlines.
- Moderate HR (70–90 bpm): Moderate exit — adds one ResBlock+SE, 1.79 ms.
- Low HR (< 70 bpm): Advanced exit — full depth with global attention, 1.94 ms, highest accuracy.
At every shifted window, the AMS controller reads instantaneous HR and routes to the shallowest model that can meet the beat’s timing deadline. All three exits are jointly trained with deep supervision (equal-weight loss summing), so each head remains independently accurate while sharing the backbone weights — keeping the total checkpoint under 5 MB in the two-cycle configuration.
Results
| Model | Cycles | Accuracy | F1 | Inference (ms) | Deadline Misses |
|---|---|---|---|---|---|
| AMS + Anytime | 2 | 91.5% | 90.6% | 1.33 | 0 |
| Advanced (standalone) | 2 | 92.6% | 91.1% | 1.94 | 431/1000 |
| Moderate | 2 | 87.8% | 87.7% | 1.79 | 259/1000 |
| Lightweight | 2 | 86.5% | 86.6% | 1.05 | 0 |
| CNN-LSTM (baseline) | 2 | 87.3% | 87.6% | 3.33 | 1000/1000 |
Key finding: Two cardiac cycles is the optimal input length — one extra beat provides enough temporal context to improve accuracy meaningfully, while three or four cycles push latency past the real-time budget. The AMS+Anytime configuration achieves the accuracy sweet spot (91.5%) with zero deadline misses across all heart-rate regimes.
Technical Details
Preprocessing:
- R-peaks detected using Hamilton’s algorithm (BioSPPy library); heartbeat cycles extracted as R–R intervals and resampled to 256 points.
- Labels assigned per-cycle based on the recording’s severity score; multi-label recordings excluded to eliminate annotation ambiguity.
- Fixed preprocessing/label-alignment issues in the PhysioNet dataset that caused unstable cross-fold metrics.
Scheduling:
- EDF (Earliest-Deadline-First) schedulability analysis verified the system can co-exist with other concurrent tasks (UI, Bluetooth, sensor fusion) on a uniprocessor without deadline violations.
- A microsecond-resolution watchdog can pre-empt inference at a configurable fraction of the beat budget and fall back to a shallower exit if needed.
Training:
- Adam optimizer, lr 0.001, batch size 128, early stopping on validation loss.
- Multi-exit deep supervision: losses from all three exit heads summed with equal weights.
- Evaluated on Raspberry Pi 4 (quad-core ARM Cortex-A72) as a proxy for commercial wearable SoCs.
Challenges
Latency–accuracy trade-off at the per-beat level: No single fixed model can meet deadlines at high HR while maximizing accuracy at low HR. The AMS+Anytime design resolves this by making depth selection a runtime policy rather than a design-time choice.
Label-alignment bugs in PhysioNet preprocessing: Early experiments showed high cross-fold metric variance. Root cause was windowing misalignment causing future-label leakage. Fixing alignment via Hamilton R-peak anchoring eliminated the variance.
Memory budget on embedded SoC: Three independent checkpoints would exceed wearable SRAM. Parameter sharing via early-exit architecture brings the two-cycle AMS model to under 5 MB — feasible for a smartwatch.
Reflection and Insights
The most important insight from this project: adaptive depth selection is not an optimization — it is a prerequisite for correctness in real-time embedded ML. A model that achieves 92.6% accuracy in batch evaluation but misses 431 out of 1000 deadlines on-device is not a working real-time system. Framing the problem through the lens of schedulability analysis (EDF, utilization bounds) made this explicit and led directly to the AMS design. The secondary insight is that multi-exit parameter sharing is the right architectural response to memory-constrained deployment: all complexity levels coexist in one checkpoint, switchable with zero weight reload overhead.
Team and Role
Research at NCSU under Prof. Zhishan Guo. My responsibilities: co-designing the CNN architecture (ResBlocks + SE + Global Attention), debugging the PhysioNet preprocessing pipeline, benchmarking model tiers on Raspberry Pi, contributing to AMS framework design, and co-authoring the RTCSA 2025 paper.
