September 26, 2018IEEE Transactions on Circuits and Systems I Regular Papers

Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices

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Population

ECG signals from Physionet ECG database and MIT-BIH database

Authors

TTTemesghen TekesteKhalifa University of Science and Technology HSHani SalehKhalifa University of Science and Technology BMBaker MohammadKhalifa University of Science and Technology

Discussion

Member takes

Implication

Enables low-power wearable ECG monitoring; leaves open clinical validation before adoption.

Structured PICO

Population

ECG signals from Physionet ECG database and MIT-BIH database

Intervention

Absolute-value curve length transform (A-CLT) based QRS detector and ECG compression architecture

Outcome

QRS detection sensitivity and predictivity, compression ratio, and power consumption

A novel ultra-low power QRS detection and ECG compression architecture demonstrates high accuracy and efficiency, making it highly suitable for IoT medical wearable devices.

Cite This Study

Tekeste et al. (2018) studied this question.

synapsesocial.com/papers/6a894b1abc51a5b47adef19b https://doi.org/10.1109/tcsi.2018.2867746

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Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Ultra-Low Power QRS Detection and ECG Compression Architecture for IoT Healthcare Devices

Discussion

Member takes

Implication

Structured PICO

Cite This Study

Also Consider

Also Consider