Detection and identification of ECG waves by histogram approach

Halder, Basudev; Mitra, S.; Mitra, Madhuchhanda

doi:10.1109/ciec.2016.7513749

Cited by 13 publications

(2 citation statements)

References 17 publications

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“…Among them, we identify threshold-based processes [14,15], derivative-based techniques [16], and transform domain techniques [17][18][19]. 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 [20], filterbased techniques [21,22], histogram-based techniques [23,24], techniques using entropy [25],…”

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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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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“…It is necessary to carry out filtering of the signal to delete power line interference at 50 or 60 Hz, baseline wander or baseline drift around 0.5 Hz, electromyogram noises at frequencies higher 50 Hz, and low and high-frequency noise components, which interfere with the signal analysis [28]. The principal components of electrocardiogram signals are P wave, QRS complex, and T Wave, where their frequencies are from 5 to 40 Hz [29].…”

Section: Denoisingmentioning

confidence: 99%

A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data

Carrillo-Alarcón

Morales-Rosales

Rangel

et al. 2020

Sensors

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The electrocardiogram records the heart’s electrical activity and generates a significant amount of data. The analysis of these data helps us to detect diseases and disorders via heart bio-signal abnormality classification. In unbalanced-data contexts, where the classes are not equally represented, the optimization and configuration of the classification models are highly complex, reflecting on the use of computational resources. Moreover, the performance of electrocardiogram classification depends on the approach and parameter estimation to generate the model with high accuracy, sensitivity, and precision. Previous works have proposed hybrid approaches and only a few implemented parameter optimization. Instead, they generally applied an empirical tuning of parameters at a data level or an algorithm level. Hence, a scheme, including metrics of sensitivity in a higher precision and accuracy scale, deserves special attention. In this article, a metaheuristic optimization approach for parameter estimations in arrhythmia classification from unbalanced data is presented. We selected an unbalanced subset of those databases to classify eight types of arrhythmia. It is important to highlight that we combined undersampling based on the clustering method (data level) and feature selection method (algorithmic level) to tackle the unbalanced class problem. To explore parameter estimation and improve the classification for our model, we compared two metaheuristic approaches based on differential evolution and particle swarm optimization. The final results showed an accuracy of 99.95%, a F1 score of 99.88%, a sensitivity of 99.87%, a precision of 99.89%, and a specificity of 99.99%, which are high, even in the presence of unbalanced data.

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Development of Cardiac Disease Classifier Using Rough Set Decision System

Halder

Mitra

2018

Advances in Intelligent Systems and Computing

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Detection and identification of ECG waves by histogram approach

Cited by 13 publications

References 17 publications

Peak Detection in Intracranial Pressure Signal Waveforms: A Comparative Study

Peak Detection in Intracranial Pressure Signal Waveforms: A Comparative Study

A Metaheuristic Optimization Approach for Parameter Estimation in Arrhythmia Classification from Unbalanced Data

Development of Cardiac Disease Classifier Using Rough Set Decision System

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