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:

Journal: Cell Reports Methods

Article Title: Non-invasive real-time pulsed Doppler assessment of blood flow in mouse ophthalmic artery

doi: 10.1016/j.crmeth.2025.100983

Figure Lengend Snippet: Setup of non-invasive Doppler OphA blood flow velocity measurements at mouse orbital opening An anesthetized mouse is placed in a prone position on the ECG board. The Doppler probe sensor tip is at the temporal canthus (left and center). Doppler in-phase (I), quadrature (Q), and ECG signals are acquired, processed, displayed, and recorded in real-time (middle). Doppler velocity signal waveforms with major parameters are acquired for analysis (right). DVFS, Doppler flow velocity system; US, ultrasound; ECG, electrocardiogram; FV, flow velocity.

Article Snippet: The principle of operation of the DFVS is described in great detail elsewhere., , Mice were anesthetized with 2.0% isoflurane in the induction chamber and then transferred to a heated electrocardiogram (ECG) board (Mouse Monitor S, Indus Instruments, Webster, TX).

Techniques:

Journal: Cell Reports Methods

Article Title: Non-invasive real-time pulsed Doppler assessment of blood flow in mouse ophthalmic artery

doi: 10.1016/j.crmeth.2025.100983

Figure Lengend Snippet: Targeted artery (OphA) originates as a branch off the ICA (A) Experimental setup for sequential ECA and ICA branch occlusion. A surgically prepared mouse on the heated board with continuous ECG monitoring, the Doppler probe, and the dissected area are shown. (B) Magnified view of the neck region prepared for occlusion. CCA, common carotid artery; ICA, internal carotid artery; ECA, external carotid artery. (C) Schematic representation of anatomical features in close proximity to CCA and ICA/ECA branching site (adapted from Liu et al. ). (D) Changes of the Doppler signal during sequential occlusion of the blood flow in left ECA and ICA. A representative of 6 independent sequential occlusions from 3 individual mice is shown.

Article Snippet: The principle of operation of the DFVS is described in great detail elsewhere., , Mice were anesthetized with 2.0% isoflurane in the induction chamber and then transferred to a heated electrocardiogram (ECG) board (Mouse Monitor S, Indus Instruments, Webster, TX).

Techniques: