Journal: Scientific Reports

Article Title: Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT

doi: 10.1038/s41598-026-43578-6

Figure Lengend Snippet: CLARITY-AI 2.0 overview and deployment context. (a) End-to-end hybrid architecture showing on-device ECG feature extraction, cloud inference, and the integrated explainability + security layers (SHAP-LLM explanations and intrusion detection) designed for security-aware edge cardiac monitoring. (b) Deployment scenario for continuous ECG streaming in a medical IoT setting, highlighting how predictions, explanations, and trust/security flags are delivered to enable interpretable and trustworthy decision support at the network edge.

Article Snippet: In contrast, recent advances in MIoT and wearable sensing technologies have enabled continuous, real-time electrocardiogram (ECG) monitoring, facilitating early disease detection and personalized health management , .

Techniques: Extraction

Journal: Scientific Reports

Article Title: Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT

doi: 10.1038/s41598-026-43578-6

Figure Lengend Snippet: Multi-source data segmentation and input representation. Visual overview of the data preparation pipeline and beat-level segmentation. The figure illustrates how 12-lead ECG signals are segmented and aligned into a single beat representation; the example shown is a beat from the PTB-XL dataset rendered consistently across all 12 leads for downstream feature extraction and modeling.

Article Snippet: In contrast, recent advances in MIoT and wearable sensing technologies have enabled continuous, real-time electrocardiogram (ECG) monitoring, facilitating early disease detection and personalized health management , .

Techniques: Extraction

Journal: Scientific Reports

Article Title: Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT

doi: 10.1038/s41598-026-43578-6

Figure Lengend Snippet: Local explanation case study (ANOMALY/PVC): SHAP + LLM report. Example of a model-specific explanation for an anomalous beat. (a) The ECG segment used for inference. (b) SHAP waterfall plot showing the dominant positive/negative feature contributions driving the anomaly decision. (c) The corresponding LLM-generated clinician-readable explanation produced from the SHAP evidence.

Article Snippet: In contrast, recent advances in MIoT and wearable sensing technologies have enabled continuous, real-time electrocardiogram (ECG) monitoring, facilitating early disease detection and personalized health management , .

Techniques: Generated, Produced

Journal: Scientific Reports

Article Title: Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT

doi: 10.1038/s41598-026-43578-6

Figure Lengend Snippet: Local explanation case study (NORMAL): SHAP + LLM report. Example explanation for a normal beat. (a) The ECG segment used for inference. (b) SHAP waterfall plot showing which features support the normal classification versus counter-evidence. (c) The final clinician-oriented LLM explanation grounded in the SHAP attribution list.

Article Snippet: In contrast, recent advances in MIoT and wearable sensing technologies have enabled continuous, real-time electrocardiogram (ECG) monitoring, facilitating early disease detection and personalized health management , .

Techniques:

Journal: Scientific Reports

Article Title: Lightweight and interpretable edge intelligence AI with intrusion detection for trustworthy cardiac arrhythmia in medical IoT

doi: 10.1038/s41598-026-43578-6

Figure Lengend Snippet: On-device efficiency on ESP32 (latency + footprint). On-device benchmark comparing CLARITY-AI 2.0 to a 1D-CNN baseline deployed on the same ESP32. The figure summarizes runtime feasibility and resource usage, showing that CLARITY-AI 2.0 is 11.7× faster and remains well below a 100 ms real-time constraint for beat-level inference, while also substantially reducing model/storage demands (energy results are detailed in Fig. ).

Article Snippet: In contrast, recent advances in MIoT and wearable sensing technologies have enabled continuous, real-time electrocardiogram (ECG) monitoring, facilitating early disease detection and personalized health management , .

Techniques: