real time electrocardiogram ecg monitoring Search Results


real time electrocardiogram ecg monitoring  (Wearable Sensing)
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Wearable Sensing real time electrocardiogram ecg monitoring
CLARITY-AI 2.0 overview and deployment context. (a) End-to-end hybrid architecture showing on-device <t>ECG</t> 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.
Real Time Electrocardiogram Ecg Monitoring, supplied by Wearable Sensing, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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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.

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

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.

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

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.

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

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.

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:

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. ).

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: