Hardware Efficient Low-Frequency Artifact Reduction Technique for Wearable ECG Device

Prajapati, Priyank H.; Darji, Anand D.

doi:10.1109/tim.2022.3208262

Cited by 8 publications

(4 citation statements)

References 35 publications

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“…The main objective of this study is to employ CWT with CNN deep learning model to classify N/S/V/F/Q based on QRS complex morphology in ECG signals. The main QRS complex has been proven to contain frequencies predominantly in the range of 5 to 20 Hz [31], while also minimizing interference from baseline and motion artifacts, which are mainly present at frequencies lower than 3.58 Hz [32]. This study focuses on selecting characteristics within the slightly wider range of 4 to 40 Hz in spectrogram, as it has been demonstrated to effectively differentiate between different arrhythmia classes.…”

Section: ) Segmentationmentioning

confidence: 99%

Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN

Xie,

Lin,

Lee

et al. 2024

IEEE Sensors J.

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This paper presents the development and testing of lightweight and power-efficient CNN models for ectopic beat classification, tailored for a compact-sized (25x45 mm) ARM-based (STM32H7) AI module. Methods: Two custom lightweight architectures (LMUEBCNet and SEmbedNet) were introduced, and their performances benchmarked against conventional models (AlexNet and VGG19). A structural pruning method, filter pruning via the Taylor score, was employed for pre-trained models' parameter optimizations. Further resizing the input image size to pixel-56/112 for the efficiency of the embedded system. Validation was conducted utilizing a combined ECG simulator and the PhysioNet MIT-BIH arrhythmia ECG dataset, adhering to the ANSI/AAMI EC57 standard. Results: SEmbedNet with parameter size of 0.04M and LMUEBCNet with 0.15M parameters reported accuracy of 99.9%/97.6%. In comparison, the pruned models of VGG19 and AlexNet, which had parameter counts of 0.10M and 0.07M respectively, achieved accuracy of 96.7%/94.4%. Post-structural pruning, VGG19 and AlexNet models saw reductions in parameters by thousands of times, with accuracy decreases of 2% to 5%. Notably, the proposed LMUEBCNet and SEmbedNet were vastly leaner by 930 and 320 times than VGG19 and AlexNet. Conclusion: The proposed ARM-based AI module integrated with custom lightweight CNN models offers superior accuracy-memory trade-offs with 0.04M parameters, which is less than 1% the size of conventional models. The AI module with compact size and a power consumption of only 0.4 watts achieves a classification rate of 4.2 segments per second and 99.9% accuracy. The AI module shows the potential to transform ECG monitoring devices into ECG analyzers.

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Section: ) Segmentationmentioning

confidence: 99%

Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN

Xie,

Lin,

Lee

et al. 2024

IEEE Sensors J.

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“…It saves ECG data analysis time by categorizing the data as useful or unreliable. Next, authors in 13 discuss efficient hardware of ECG devices with a low-frequency artifact (LFA) reduction approach. LFA present in ECG signals increases the ECG interpretation complexity.…”

Section: Related Workmentioning

confidence: 99%

“…LFA present in ECG signals increases the ECG interpretation complexity. The presented LFA reduction approach for ECG detector hardware devices in 13 provides a significant improvement over traditional reduction techniques with lower power consumption and generation of high-quality signals. References 12 and 13 focus on designing low-power ECG detectors with increased accuracy.…”

Section: Related Workmentioning

confidence: 99%

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Secure hardware IP of GLRT cascade using color interval graph based embedded fingerprint for ECG detector

Sengupta,

Anshul

2024

Sci Rep

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This paper presents a security aware design methodology to design secure generalized likelihood ratio test (GLRT) hardware intellectual property (IP) core for electrocardiogram (ECG) detector against IP piracy and fraudulent claim of IP ownership threats. Integrating authentic (secure version) GLRT hardware IP core in the system-on-chip (SoC) of ECG detectors is paramount for reliable operation and estimation of ECG parametric data, such as Q wave, R wave and S wave (QRS) complex detection. A pirated GLRT hardware IP integrated into an ECG detector may result in an unreliable/erratic estimation of ECG parametric data that can be hazardous and fatal for the end patient. The proposed methodology presents an integrated design flow to secure micro GLRT and GLRT cascade hardware IP cores for the ECG detector, using the colored interval graph (CIG) framework based fingerprint biometric, during high level synthesis (HLS). The proposed approach integrates a fingerprint biometric based security constraint generation process for securing the GLRT hardware IP core. This paper also presents a secure register transfer level (RTL) datapath design corresponding to micro GLRT and GLRT cascade hardware IP cores with embedded IP vendor's fingerprint. The proposed secure GLRT hardware IP core embedded with fingerprint biometric achieves superior results in terms of probability of coincidence and tamper tolerance than other security approaches. More explicitly, the proposed approach reports a significantly lower value of probability of coincidence and stronger value for tamper tolerance. Further, the proposed approach incurs zero design cost overhead.

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Hardware implementation of 1D-CNN architecture for ECG arrhythmia classification

Prajapati

Darji

2023

Biomedical Signal Processing and Control

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Hardware Efficient Low-Frequency Artifact Reduction Technique for Wearable ECG Device

Cited by 8 publications

References 35 publications

Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN

Design and Implementation of an ARM-Based AI Module for Ectopic Beat Classification Using Custom and Structural Pruned Lightweight CNN

Secure hardware IP of GLRT cascade using color interval graph based embedded fingerprint for ECG detector

Hardware implementation of 1D-CNN architecture for ECG arrhythmia classification

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