R peak detection for wireless ECG using DWT and entropy of coefficients

Dave, Tejal; Pandya, Utpal

doi:10.1504/ijbet.2020.111472

Cited by 4 publications

(2 citation statements)

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“…Other methods perform peak detection by incorporating a larger context to describe the signature of each peak within the beat, which includes intensity weighted variance [ 22 ], filter-based techniques [ 23 , 24 ], histogram-based techniques [ 25 , 26 ], techniques using entropy [ 27 ], momentum [ 28 ], stochastic resonance [ 29 ], higher-order statistics [ 30 ], nonlinear energy operator [ 31 ], empirical mode decomposition [ 32 ]. More advanced techniques such as the wavelet transform and entropy of coefficients [ 33 , 34 ] have also achieved promising results.…”

Section: State-of-the-artmentioning

confidence: 99%

Peak detection in intracranial pressure signal waveforms: a comparative study

Wei,

Krakauskaite,

Subramanian

et al. 2024

BioMed Eng OnLine

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Background The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work quantitatively evaluates existing computational frameworks for automatically extracting peaks within ICP waveforms. Methods Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying noise levels. The evaluation was performed on a dataset of ICP signals assembled from 700 h of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13, 611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. Results The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that most techniques provide acceptable accuracy with a mean absolute error (MAE) $$\le 10$$ ≤ 10 ms without noise. In the presence of a higher noise level, however, only kernel spectral regression and random forest remain below that error threshold while the performance of other techniques deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and long short-term memory (LSTM) can be applied continuously and provide additional robustness in situations where single pulse analysis methods fail, such as missing data. Conclusion While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to noise and temporary artifacts that commonly arise as part of the monitoring setup in the clinical setting.

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Section: State-of-the-artmentioning

confidence: 99%

Peak detection in intracranial pressure signal waveforms: a comparative study

Wei,

Krakauskaite,

Subramanian

et al. 2024

BioMed Eng OnLine

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“…momentum [26], stochastic resonance [27], higher-order statistics [28], nonlinear energy operator [29], empirical mode decomposition [30]. More advanced techniques such as the wavelet transform and entropy of coefficients [31,32] have also achieved promising results.…”

Section: ) Signal-processing Techniquesmentioning

confidence: 99%

Peak Detection in Intracranial Pressure Signal Waveforms: A Comparative Study

Wei

Krakauskaite

Subramanian

et al. 2023

Preprint

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Background: The monitoring and analysis of quasi-periodic biological signals such as electrocardiography (ECG), intracranial pressure (ICP), and cerebral blood flow velocity (CBFV) waveforms plays an important role in the early detection of adverse patient events and contributes to improved care management in the intensive care unit (ICU). This work provides a quantitative evaluation of existing computational frameworks for the automatic extraction of peaks within ICP waveforms. Methods: Peak detection techniques based on state-of-the-art machine learning models were evaluated in terms of robustness to varying levels of noise. Evaluation was performed on a dataset of ICP signals assembled from 700 hours of monitoring from 64 neurosurgical patients. The groundtruth of the peak locations was established manually on a subset of 13,611 pulses. Additional evaluation was performed using a simulated dataset of ICP with controlled temporal dynamics and noise. Results: The quantitative analysis of peak detection algorithms applied to individual waveforms indicates that all techniques provide acceptable accuracy (RMSE <= 0.15) without noise. In presence of higher level of noise, however, only Kernel ridge regression and Random forest remains below that error threshold while the performance of other techniques significantly deteriorates. Our experiments also demonstrated that tracking methods such as Bayesian inference and LSTM can be applied in a continuous fashion and provide additional robustness in situations where single pulse analysis methods tend to fail such as in presence of missing data. Conclusion: While machine learning-based peak detection methods require manually labeled data for training, these models outperform conventional signal processing ones based on handcrafted rules and should be considered for peak detection in modern frameworks. In particular, peak tracking methods that incorporate temporal information between successive periods of the signals have demonstrated in our experiments to provide more robustness to temporary artifacts that commonly arise as part of the monitoring setup in the NICU.

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