ECG artefact identification and removal in mHealth systems for continuous patient monitoring

Imtiaz, Syed Anas; Mardell, James; Saremi-Yarahmadi, Siavash; Rodriguez‐Villegas, Esther

doi:10.1049/htl.2016.0020

Cited by 19 publications

(12 citation statements)

References 20 publications

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“…All devices are sensitive to poor skin contact, sensor placement, and motion artefact (muscle tremor and arm movement). 25,26 Although Withings was strapped around the wrist, suggesting less movement, we frequently observed a noise in baseline, which was reflected by the overall lower ECG quality. Around 80% of patients were willing to use a smart device at home, similar to previous studies.…”

Section: Discussionmentioning

confidence: 91%

A comparison of ECG-based home monitoring devices in adults with CHD

Pengel

Robbers‐Visser²,

Groenink³

et al. 2022

Cardiol Young

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Background: Various electrocardiogram (ECG)-based devices are available for home monitoring, but the reliability in adults with CHD is unknown. Therefore, we determined the accuracy of different ECG-based devices compared to the standard 12-lead ECG in adult CHD. Methods and results: This is a single-centre, prospective, cross-sectional study in 176 consecutive adults with CHD (54% male, age 40 ± 16.6 years, 24% severe CHD, 84% previous surgery, 3% atrial fibrillation (AF), 24% right bundle branch block). Diagnostic accuracy of the Withings Scanwatch (lead I), Eko DUO (precordial lead), and Kardia 6L (six leads) was determined in comparison to the standard 12-lead ECG on several tasks: 1) AF classification (percentage correct), 2) QRS-morphology classification (percentage correct), and 3) ECG intervals calculation (QTc time ≤ 40 ms difference). Both tested AF algorithms had high accuracy (Withings: 100%, Kardia 6L: 97%) in ECGs that were classified. However, the Withings algorithm classified fewer ECGs as inconclusive (5%) compared to 31% of Kardia (p < 0.001). Physician evaluation of Kardia correctly classified QRS morphology more frequently (90% accuracy) compared to Eko DUO (84% accuracy) (p = 0.03). QTc was underestimated on all ECG-based devices (p < 0.01). QTc duration accuracy was acceptable in only 51% of Withings versus 70% Eko and 74% Kardia (p < 0.001 for both comparisons). Conclusions: Although all devices demonstrated high accuracy in AF detection, the Withings automatic algorithm had fewest uninterpretable results. Kardia 6L was most accurate in overall evaluation such as QRS morphology and QTc duration. These findings can inform both patients and caregivers for optimal choice of home monitoring.

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Section: Discussionmentioning

confidence: 91%

A comparison of ECG-based home monitoring devices in adults with CHD

Pengel

Robbers‐Visser²,

Groenink³

et al. 2022

Cardiol Young

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“…stated that: ”Rigorous quality control is essential for accurate diagnosis, since alterations during the actual recording might result in inappropriate treatment decisions” [3]. To mitigate this problem, automated algorithms are needed to detect artefacts and to quantify the quality of the recorded signal [4]. Two closely related approaches can be distinguished to assess the acceptability of the recorded ECG: detection and quantification.…”

Section: Introductionmentioning

confidence: 99%

Artefact detection and quality assessment of ambulatory ECG signals

Moeyersons

Smets

Morales

et al. 2019

Computer Methods and Programs in Biomedicine

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HighlightsA novel way for ECG quality assessment is proposed, based on the posterior probability of an artefact detection classifier.A good performance was obtained when testing the classifier on two independent (re)labelled datasets, thereby showing its robustness. The performance was better, compared to a heuristic method and comparable to another machine learning algorithm.A significant correlation was observed between the proposed quality assessment and the annotators level of agreement.Significant decreases in quality level were observed for different noise levels.

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“…The patient movement, local muscle contraction and electrical interference are some causes of noise and artefact in the recording process (Rodrigues, Belo, and Gamboa 2017). In automatic ECG signal labeling depends mainly on the algorithm and the quality of the recording (Imtiaz et al 2016). ECG recording artefact can be misinterpreted with fast heart rate (due to narrow peak R to R interval) and even arrhythmia (such as VT, VF and AF).…”

Section: B Discussionmentioning

confidence: 99%

Neural Network based - Arrhythmia Monitoring Device: A Pivotal Clinical Trial

2020

IJITEE

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Arrhythmia or irregular heart beat had wide range of clinical manifestations, from benign arrhythmia that not need any medication to life threatening condition. It can occur permanently or intermittently. Intermittent arrhythmia needs specific diagnostic tools that can record the electrocardiogram continuously. This research was sought to analysed the sensitivity, specificity, Positive Predictive Value, and Negative Predictive Value of arrhythmia monitoring device that based on neural network based artificial intelligent. The pivotal clinical trial was involved a total 103 people (health and stable arrhythmia patients). This research used a diagnostic test by comparing the electrocardiography (ECG) from prototype with standard ECG for diagnose arrhythmia. The Arrhythmia Monitoring System that we developed has three hardware components; smartphones, server for arrhythmia detection and patchable ECG recorder. All three components are connected with internet of things (IoT) technology. The architecture of Arrhythmia software monitoring included ECG signals pre-processing, beats detection, features extraction for detecting VT/VF, and classification for detecting VT/VF. Features extraction such as heart rate variability (HRV) and T wave alternans. We compared the ECG of arrhythmia prototype monitoring device with standard Holter monitoring. We enrolled 103 patients. There was no significant difference of heart rate between arrhythmia prototype monitoring device and standard Holter (87.26 ± 11.2 vs 86.07±9.15, P=0.43) . There was significant different of maximum and minimum heart rate between arrhythmia prototype monitoring device and standard holter monitoring (121.3±31.7 vs 131.0±10.8, p= 0.000, and 65.1±13.5 vs 73.07±10.02, p=0.000) . This device has low sensitivity 80% (95% CI 75% -82%) and high specificity 91.8% (95% CI 85% -92%) for detecting the abnormal ECG. The Positive Predictive Value (PPV) was 63.2% (95% CI 58.8% -67.52%) and Negative Predictive Value (NVP) was 96.3% (95% CI 94.7% -98.3%) 71.4% (95% CI 66.4% -76.4%) and 75% (95% CI 72%-78%), Specificity 97.8% (95% CI 95.8-99.8%) and 91.7% (95% CI 83.4%-99.7%, respectively). The PPV of this device to detect PVC and PAC was 71.4% (95% CI 66.4%-76.4%) and 72.7%. (95% CI 68.7%-76.7%) The NPV of this device to detect PVC and PAC was 97. 8% (95% CI 95.8%-99.85) and 98.9% (95% CI 98.1%-99.7%), respectively. This study found that the device to be a valuable diagnostic tool that has relatively low sensitivity but high specificity for diagnosing Abnormal ECG, PVC and PAC. According to the results of our study, we found that the device to be a valuable diagnostic tool that has relatively high sensitivity and specificity for diagnosing Abnormal ECG, PVC and PAC. . This device demonstrates an ability to detect PVC and PAC (Sensitivity

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ECG artefact identification and removal in mHealth systems for continuous patient monitoring

Cited by 19 publications

References 20 publications

A comparison of ECG-based home monitoring devices in adults with CHD

A comparison of ECG-based home monitoring devices in adults with CHD

Artefact detection and quality assessment of ambulatory ECG signals

Neural Network based - Arrhythmia Monitoring Device: A Pivotal Clinical Trial

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