Real-time electrocardiogram P-QRS-T detection–delineation algorithm based on quality-supported analysis of characteristic templates

Karimipour, Atiyeh; Homaeinezhad, Mohammad Reza

doi:10.1016/j.compbiomed.2014.07.002

Cited by 105 publications

(55 citation statements)

References 26 publications

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“…The signal noise ratio method means based on the value of signal noise ratio to evaluate the quality. Threshold method [2] means based on the training focusing on the quality of the electrocardiosignal, selecting a value as the threshold, if the signal meeting the requirements of each threshold is judged as acceptable quality; otherwise signal quality is not acceptable. The method with learning machine [4] outlines a five-level ECG signal quality classification algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…The algorithms are mainly classified into four types, including the signal noise ratio theory method, threshold value method based on the signal shape [2,3], the method based on learning machine [4] and the method based on support vector machine [5]. The signal noise ratio method means based on the value of signal noise ratio to evaluate the quality.…”

Section: Related Workmentioning

confidence: 99%

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An algorithm for evaluating the quality of the electrocardiosignal

Fang

Zhao

et al. 2015

2015 IEEE International Conference on Information and Automation

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The main objective of this study is to introduce a simple, low-complexity and effective algorithm to evaluate the quality of the real-time electro-cardiosignal, especially the electro-cardiosignal coming from dry electrode, or the signal collecting during periods of muscular activity or excessive motion. In the proposed method, the real-time signal is preprocessed by five main steps:(1) down sampling to reduce the computation complexity; (2) three-order auto-correlation operation to compute the degree of correlation of the electrocardiogram; (3) finding two maximal values in the definite window size; (4) recording every maximal value's position and amplitude; (5) analysing the relationship between every maximum's position and amplitude. Finally, according to the judgment rules, the quality of electro-cardiosignal is obtained. The theory of the proposed algorithm is proved based on the mathematics theory, and testified by the three real-time electrocardiosignal coming from the dry electrodes, which are the signal approximately with least noise, the signal with partial noise, and the signal with full bandwidth noise. The whole computation complexity is 2 ( ) O n . The result shows this method can provide a better discrimination between noise and other signal.Index Terms -quality of electrocardiosignal, auto-correlation, dry electrode.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

An algorithm for evaluating the quality of the electrocardiosignal

Fang

Zhao

et al. 2015

2015 IEEE International Conference on Information and Automation

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“…(13) In addition, radio waves should not be lost when they pass through the material. Packaging design should be considered to prevent the risk of tissue damage due to movement after sensor insertion.…”

Section: Packagingmentioning

confidence: 99%

The Computer Aided Cardiovascular Disease Detection Sensor and Algorithm Using Dry Electrode Based ECG Sensor Integrated Clothes

2018

Sensors and Materials

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The incidence of heart disease increases with age. The typical method of monitoring arrhythmia is to use a body-patch-type sensor with a wet electrode. Even though this sensor is easy to use, it has several disadvantages such as problems caused by wet electrodes in tissues when they are monitored for long periods. Thus, a monitoring sensor integrated into clothes with a dry electrode is proposed. In this study, we develop a smart outdoor shirt equipped with a dry electrode electrocardiogram (ECG) sensor for a cardiac arrhythmia computer-aided diagnosis (CAD) system. The sensor can be inserted into a console close to the chest, charged, used to communicate wirelessly, and connected with a smartphone application. According to experiments, the ECG signals measured by the smart shirt indicated that 97.5 ± 1% of the signals could be measured in an immobile state and at least 85.2 ± 2% of the signals could be measured during movement. In addition, we propose a CAD system for detecting cardiac arrhythmia. It was determined through experiments that the system can detect arrhythmia with an accuracy of 98.2 ± 2%.

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“…Low pass differentiation (LPD) [9], hidden Markov models (HMM) [10,11,12,13,14], partially collapsed Gibbs sampler (PCGS) [15], wavelet transform [16,17,18], correlation analysis [19,20] and extended…”

Section: Introductionmentioning

confidence: 99%

“…In the SKF, at each time instance, the states are estimated by several Kalman filters 20 (KFs). Furthermore, a hidden discrete state variable called switch is considered whose status changes over the time according to a Markov model.…”

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confidence: 99%

ECG fiducial point extraction using switching Kalman filter

Akhbari

Ghahjaverestan

Shamsollahi

et al. 2018

Computer Methods and Programs in Biomedicine

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In this paper, we propose a novel method for extracting fiducial points (FPs) of the beats in electrocardiogram (ECG) signals using switching Kalman filter (SKF). In this method, according to McSharry's model, ECG waveforms (P-wave, QRS complex and T-wave) are modeled with Gaussian functions and ECG baselines are modeled with first order auto regressive models. In the proposed method, a discrete state variable called "switch" is considered that affects only the observation equations. We denote a mode as a specific observation equation and switch changes between 7 modes and corresponds to different segments of an ECG beat. At each time instant, the probability of each mode is calculated and compared among two consecutive modes and a path is estimated, which shows the relation of each part of the ECG signal to the mode with the maximum probability. ECG FPs are found from the estimated path. For performance evaluation, the Physionet QT database is used and the proposed method is compared with methods based on wavelet transform, partially collapsed Gibbs sampler (PCGS) and extended Kalman filter. For our proposed method, the mean error and the root mean square error across all FPs are 2 ms (i.e. less than one sample) and 14 ms, respectively. These errors are significantly smaller than those obtained using other methods. The proposed method achieves lesser RMSE and smaller variability with respect to others.

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Real-time electrocardiogram P-QRS-T detection–delineation algorithm based on quality-supported analysis of characteristic templates

Cited by 105 publications

References 26 publications

An algorithm for evaluating the quality of the electrocardiosignal

An algorithm for evaluating the quality of the electrocardiosignal

The Computer Aided Cardiovascular Disease Detection Sensor and Algorithm Using Dry Electrode Based ECG Sensor Integrated Clothes

ECG fiducial point extraction using switching Kalman filter

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