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

1. 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.

2. 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.

3. 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.

Overview

This project series was completed as part of the Statistical Learning for Data Science course at Southern University of Science and Technology. The work covered two major tasks: applying hybrid deep learning and traditional machine learning pipelines for medical image classification, focusing on fundus lesion diagnosis. The project explored how feature representations from pre-trained deep networks can be combined with classical classifiers to achieve high accuracy with reduced computational cost.

Results

• Task 1 — Pre-trained Feature Extraction: Used ResNet18 as a frozen feature extractor; downstream classifiers (Linear Regression, KNN, SVM) achieved 100% accuracy on the test set, demonstrating the quality of ResNet18’s learned representations.
• Task 2 — Fine-tuned ResNet18: Fine-tuned ResNet18 end-to-end on the 3-class fundus dataset, converging in ~3 epochs with 100% test accuracy and near-perfect AUC across all classes.
• Bonus — Custom CNN: Designed a lightweight CNN from scratch using PyTorch, achieving 99.53% accuracy in 155 s training time vs. 348 s for ResNet18, demonstrating favorable speed-accuracy trade-off.
• Extension — 7-class Classification: Extended the fine-tuned ResNet18 to a 7-class problem; all classes achieved AUC = 1.00, validating the method’s scalability.

Technical Details

• Dataset: Fundus lesion images categorized into 3 (and later 7) classes; standard preprocessing with resize, normalization, and contrast adjustment.
• Hybrid Pipeline:
  • ResNet18 (pre-trained on ImageNet) used as a backbone to extract 1000-dimensional feature vectors.
  • Traditional classifiers (LE, KNN, SVM, MLP) trained on extracted features using sklearn.
• Custom CNN Architecture:
  • Convolutional channels: [16, 32, 64], kernel size 3×3, max pooling with stride 2.
  • Grayscale edge-detected preprocessing (Canny, Gaussian blur) to reduce input redundancy.
  • Fully connected MLP head for multi-class output.
• Training Setup: SGD optimizer (lr=0.001, momentum=0.9), cross-entropy loss, 3–5 epochs.
• Evaluation: Accuracy, ROC curves, and AUC per class; all reported in the final report.

Challenges

• Speed vs. accuracy trade-off: The custom CNN was significantly faster (2.2×) but slightly less accurate than ResNet18. The gap was attributed to the simplicity of convolution layers and grayscale conversion that discards color information.
• Feature quality vs. training cost: Frozen ResNet18 features were so discriminative that even linear classifiers achieved perfect accuracy, raising the question of when fine-tuning is truly necessary.
• 7-class generalization: Extending to a harder 7-class scenario required careful dataset balancing and preprocessing to maintain generalization.

Reflection and Insights

This project reinforced a key principle in applied machine learning: strong pre-trained feature representations can often substitute for expensive end-to-end training, especially when labeled data is limited. The hybrid approach — deep features paired with classical classifiers — offers a practical and interpretable alternative to black-box deep models in medical contexts. Designing the custom CNN from scratch also deepened understanding of how architectural choices (depth, width, pooling strategy) affect both accuracy and training efficiency.

Team and Role

• Team: Collaborated with two teammates on methodology design, experiments, and report writing.
• My Role: Led the custom CNN design and preprocessing pipeline; contributed to the hybrid pipeline experiments and analysis of width/depth trade-offs.