An algorithm for evaluating the ECG signal quality in 12 lead ECG monitoring system

Zhang, Yue; Hou, Zhongjie

doi:10.1109/icsess.2015.7339095

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…Signal quality estimation of conventional contact ECG has been investigated more extensively than for ccECG. These ECG quality metrics include the use of features such as match between different beat detection algorithms [ 13 , 14 ], multi-lead signal comparison [ 13 , 14 , 15 ], higher order moments such as Kurtosis [ 13 , 14 , 16 ], amplitude characteristics [ 16 , 17 , 18 ], spectral characteristics [ 13 , 14 , 15 , 19 ], HR characteristics [ 15 , 20 ], QRST area [ 21 ], QRST morphology [ 20 ], electrode-tissue impedance (ETI), and motion [ 22 ]; in some cases combined with additional processing such as Kalman Filtering [ 13 ], multi-layer perceptron neural networks (MLP) [ 14 ], support vector machines (SVM) [ 14 ], Least-Mean-Squares (LMS) adaptive filters, and PCA [ 22 ]. The high amount of research on ECG SQIs compared to the one on ccECG SQIs demonstrates the need for additional efforts to further validate these metrics in the contactless signals, due to the special characteristics of ccECG artefacts [ 23 ], which are not always the same as in their contact counterpart, and present a higher challenge.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

Castro

Varon

Torfs

et al. 2018

Sensors

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Sleep-related conditions require high-cost and low-comfort diagnosis at the hospital during one night or longer. To overcome this situation, this work aims to evaluate an unobtrusive monitoring technique for sleep apnea. This paper presents, for the first time, the evaluation of contactless capacitively-coupled electrocardiography (ccECG) signals for the extraction of sleep apnea features, together with a comparison of different signal quality indicators. A multichannel ccECG system is used to collect signals from 15 subjects in a sleep environment from different positions. Reference quality labels were assigned for every 30-s segment. Quality indicators were calculated, and their signal classification performance was evaluated. Features for the detection of sleep apnea were extracted from capacitive and reference signals. Sleep apnea features related to heart rate and heart rate variability achieved high similarity to the reference values, with p-values of 0.94 and 0.98, which is in line with the more than 95% beat-matching obtained. Features related to signal morphology presented lower similarity with the reference, although signal similarity metrics of correlation and coherence were relatively high. Quality-based automatic classification of the signals had a maximum accuracy of 91%. Best-performing quality indicators were based on template correlation and beat-detection. Results suggest that using unobtrusive cardiac signals for the automatic detection of sleep apnea can achieve similar performance as contact signals, and indicates clinical value of ccECG. Moreover, signal segments can automatically be classified by the proposed quality metrics as a pre-processing step. Including contactless respiration signals is likely to improve the performance and provide a complete unobtrusive cardiorespiratory monitoring solution; this is a promising alternative that will allow the screening of more patients with higher comfort, for a longer time, and at a reduced cost.

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Section: Introductionmentioning

confidence: 99%

Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

Castro

Varon

Torfs

et al. 2018

Sensors

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“…The ECG signal informs us about the position of the heart, the origin and transmission of the bioelectrical impulses and the Heart Rate (HR). The measurement of the electrical activity of the heart by the ECG is an established and well-recognized standard [14,15,16,17]. Currently, ECG modules using MRI-compatible bioelectrodes are fixed to a patient’s body to monitor their ECG signals.…”

Section: State-of-the-artmentioning

confidence: 99%

Vital Sign Monitoring and Cardiac Triggering at 1.5 Tesla: A Practical Solution by an MR-Ballistocardiography Fiber-Optic Sensor

Nedoma

Fajkus

Martínek

et al. 2019

Sensors

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This article presents a solution for continuous monitoring of both respiratory rate (RR) and heart rate (HR) inside Magnetic Resonance Imaging (MRI) environments by a novel ballistocardiography (BCG) fiber-optic sensor. We designed and created a sensor based on the Fiber Bragg Grating (FBG) probe encapsulated inside fiberglass (fiberglass is a composite material made up of glass fiber, fabric, and cured synthetic resin). Due to this, the encapsulation sensor is characterized by very small dimensions (30 × 10 × 0.8 mm) and low weight (2 g). We present original results of real MRI measurements (conventionally most used 1.5 T MR scanner) involving ten volunteers (six men and four women) by performing conventional electrocardiography (ECG) to measure the HR and using a Pneumatic Respiratory Transducer (PRT) for RR monitoring. The acquired sensor data were compared against real measurements using the objective Bland–Altman method, and the functionality of the sensor was validated (95.36% of the sensed values were within the ±1.96 SD range for the RR determination and 95.13% of the values were within the ±1.96 SD range for the HR determination) by this means. The accuracy of this sensor was further characterized by a relative error below 5% (4.64% for RR and 4.87% for HR measurements). The tests carried out in an MRI environment demonstrated that the presence of the FBG sensor in the MRI scanner does not affect the quality of this imaging modality. The results also confirmed the possibility of using the sensor for cardiac triggering at 1.5 T (for synchronization and gating of cardiovascular magnetic resonance) and for cardiac triggering when a Diffusion Weighted Imaging (DWI) is used.

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“…The quality of the ECG trace is key in successful interpretation to ensure that patients receive the correct diagnosis (6). ECG quality is particularly important when using telehealth ECG devices, where the quality of ECG recordings can be lower than that of 12-lead ECGs due to: the use of dry electrodes instead of gel electrodes; recording at the hands rather than the chest; holding the device incorrectly; and recording without clinical supervision.…”

Section: Introductionmentioning

confidence: 99%

Telephone training to improve ECG quality in remote screening for atrial fibrillation

Bhayankaram,

Mant,

Brimicombe

et al. 2024

Preprint

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Background and Aims: Self-recorded, single-lead ECGs are increasingly used to diagnose arrhythmias. However, they can be of variable quality, which can affect the reliability of interpretation. In this analysis of ECGs collected in atrial fibrillation screening studies, our aims were to: (i) determine the quality of ECGs when recorded unsupervised (at home); and (ii) investigate whether telephone training improved ECG quality. Methods: Data was obtained from the Screening for Atrial Fibrillation to Reduce stroke (SAFER) programme, where participants recorded single-lead ECGs four times per day for three weeks using a handheld device. ECG quality was assessed using an automated algorithm, and participants who recorded >25% poor quality ECGs from days 4-10 of screening were identified for training. Telephone training to improve ECG recording technique was delivered when research team capacity permitted. Results: 14,727 participants recorded 1,206,972 ECGs, of which 43,513 (3.6%) were poor quality. Most participants (51.9%) did not record any poor-quality ECGs. 1,105 (7.5%) participants met the threshold for training. Of these, 165 participants received training and 896 did not. Comparing these groups, the mean (95% confidence interval) reduction in the proportion of poor-quality ECGs per participant from before training (days 1-3) to after training (days 11-21) was 21.1 (17.5-23.5) % with training and 15.7 (14.5-16.8) % without training (p<0.05). Conclusion: Most participants achieved adequate quality ECGs. For those that did not, ECG quality improved over time regardless, and training further improved ECG quality. Therefore, telephone training could be considered in atrial fibrillation screening programmes using single-lead ECG devices.

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An algorithm for evaluating the ECG signal quality in 12 lead ECG monitoring system

Cited by 6 publications

References 5 publications

Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

Evaluation of a Multichannel Non-Contact ECG System and Signal Quality Algorithms for Sleep Apnea Detection and Monitoring

Vital Sign Monitoring and Cardiac Triggering at 1.5 Tesla: A Practical Solution by an MR-Ballistocardiography Fiber-Optic Sensor

Telephone training to improve ECG quality in remote screening for atrial fibrillation

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