Atrial fibrillation frequency tracking in ambulatory ECG signals: The significance of signal quality assessment

Abstract Purpose Electrocardiogram (ECG) signals collected from wearable devices are easily corrupted with surrounding noise and artefacts, where the signal-to-noise ratio (SNR) of wearable ECG signals is significantly lower than that from hospital ECG machines. To meet the requirements for monitoring heart disease via wearable devices, eliminating useless or poor-quality ECG signals (e.g., lead-falls and low SNRs) can be solved by signal quality assessment algorithms. Methods To compensate for the deficiency of the existing ECG quality assessment system, a wearable ECG signal dataset from heart disease patients collected by Lenovo H3 devices was constructed. Then, this paper compares the performance of three machine learning algorithms, i.e., the traditional support vector machine (SVM), least-squares SVM (LS-SVM) and long short-term memory (LSTM) algorithms. Different non-morphological signal quality indices (i.e., the approximate entropy (ApEn), sample entropy (SaEn), fuzzy measure entropy (FMEn), Hurst exponent (HE), kurtosis (K) and power spectral density (PSD) features) extracted from the original ECG signals are fed into the three algorithms as input. Results The true positive rate, true negative rate, sensitivity and accuracy are used to evaluate the performance of each method, and the LSTM algorithm achieves the best results on these metrics (97.14%, 86.8%, 97.46% and 95.47%, respectively). Conclusions Among the three algorithms, the LSTM-based quality assessment method is the most suitable for the signals collected by the Lenovo H3 devices. The results also show that the combination of statistical features can effectively evaluate the quality of ECG signals.

Download Full-text

Artefact detection and quality assessment of ambulatory ECG signals

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2019.105050 ◽

2019 ◽

Vol 182 ◽

pp. 105050 ◽

Cited By ~ 6

Author(s):

Jonathan Moeyersons ◽

Elena Smets ◽

John Morales ◽

Amalia Villa ◽

Walter De Raedt ◽

...

Keyword(s):

Quality Assessment ◽

Ecg Signals ◽

Ambulatory Ecg ◽

Artefact Detection

Download Full-text

Prediction of Atrial Fibrillation Termination by Catheter Ablation Using Adaptive Frequency Tracking of Atrial ECG Signals

2016 Computing in Cardiology Conference (CinC) ◽

10.22489/cinc.2016.282-343 ◽

2016 ◽

Author(s):

Adrian Luca ◽

Andrea Buttu ◽

Jean-Marc Vesin ◽

Patrizio Pascale ◽

Laurent Roten ◽

...

Keyword(s):

Atrial Fibrillation ◽

Catheter Ablation ◽

Frequency Tracking ◽

Ecg Signals

Download Full-text

Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App (Preprint)

10.2196/preprints.12284 ◽

2018 ◽

Author(s):

Tine Proesmans ◽

Christophe Mortelmans ◽

Ruth Van Haelst ◽

Frederik Verbrugge ◽

Pieter Vandervoort ◽

...

Keyword(s):

Atrial Fibrillation ◽

Primary Care ◽

Diagnostic Accuracy ◽

Mobile Phone ◽

Low Cost ◽

Attractive Alternative ◽

Signal Quality ◽

Convenience Sample ◽

Ecg Signals ◽

Accuracy Study

BACKGROUND Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered. OBJECTIVE This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals. METHODS A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF. RESULTS A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects. CONCLUSIONS The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.

Download Full-text

A Deep Learning Approach for Featureless Robust Quality Assessment of Intermittent Atrial Fibrillation Recordings from Portable and Wearable Devices

Entropy ◽

10.3390/e22070733 ◽

2020 ◽

Vol 22 (7) ◽

pp. 733

Author(s):

Álvaro Huerta Herraiz ◽

Arturo Martínez-Rodrigo ◽

Vicente Bertomeu-González ◽

Aurelio Quesada ◽

José J. Rieta ◽

...

Keyword(s):

Atrial Fibrillation ◽

Deep Learning ◽

Quality Assessment ◽

Heart Rhythm ◽

Wearable Devices ◽

Automatic Identification ◽

High Quality ◽

Learning Capability ◽

Ecg Signals ◽

Challenging Environment

Atrial fibrillation (AF) is the most common heart rhythm disturbance in clinical practice. It often starts with asymptomatic and very short episodes, which are extremely difficult to detect without long-term monitoring of the patient’s electrocardiogram (ECG). Although recent portable and wearable devices may become very useful in this context, they often record ECG signals strongly corrupted with noise and artifacts. This impairs automatized ulterior analyses that could only be conducted reliably through a previous stage of automatic identification of high-quality ECG intervals. So far, a variety of techniques for ECG quality assessment have been proposed, but poor performances have been reported on recordings from patients with AF. This work introduces a novel deep learning-based algorithm to robustly identify high-quality ECG segments within the challenging environment of single-lead recordings alternating sinus rhythm, AF episodes and other rhythms. The method is based on the high learning capability of a convolutional neural network, which has been trained with 2-D images obtained when turning ECG signals into wavelet scalograms. For its validation, almost 100,000 ECG segments from three different databases have been analyzed during 500 learning-testing iterations, thus involving more than 320,000 ECGs analyzed in total. The obtained results have revealed a discriminant ability to detect high-quality and discard low-quality ECG excerpts of about 93%, only misclassifying around 5% of clean AF segments as noisy ones. In addition, the method has also been able to deal with raw ECG recordings, without requiring signal preprocessing or feature extraction as previous stages. Consequently, it is particularly suitable for portable and wearable devices embedding, facilitating early detection of AF as well as other automatized diagnostic facilities by reliably providing high-quality ECG excerpts to further processing stages.

Download Full-text

Signal Quality Assessment of F-waves in Atrial Fibrillation

2017 Computing in Cardiology Conference (CinC) ◽

10.22489/cinc.2017.051-153 ◽

2017 ◽

Cited By ~ 2

Author(s):

Mikael Henriksson ◽

Andrius Petrenas ◽

Vaidotas Marozas ◽

Frida Sandberg ◽

Leif Sornmo

Keyword(s):

Atrial Fibrillation ◽

Quality Assessment ◽

Signal Quality

Download Full-text

The Effects of Compression on the Detection of Atrial Fibrillation in ECG Signals

Applied Sciences ◽

10.3390/app11135908 ◽

2021 ◽

Vol 11 (13) ◽

pp. 5908

Author(s):

Raquel Cervigón ◽

Brian McGinley ◽

Darren Craven ◽

Martin Glavin ◽

Edward Jones

Keyword(s):

Atrial Fibrillation ◽

Characteristic Curve ◽

Set Partitioning ◽

Constrained Systems ◽

Battery Life ◽

Detection Accuracy ◽

Ecg Signal ◽

Signal Compression ◽

Ecg Signals ◽

Cardiac Devices

Although Atrial Fibrillation (AF) is the most frequent cause of cardioembolic stroke, the arrhythmia remains underdiagnosed, as it is often asymptomatic or intermittent. Automated detection of AF in ECG signals is important for patients with implantable cardiac devices, pacemakers or Holter systems. Such resource-constrained systems often operate by transmitting signals to a central server where diagnostic decisions are made. In this context, ECG signal compression is being increasingly investigated and employed to increase battery life, and hence the storage and transmission efficiency of these devices. At the same time, the diagnostic accuracy of AF detection must be preserved. This paper investigates the effects of ECG signal compression on an entropy-based AF detection algorithm that monitors R-R interval regularity. The compression and AF detection algorithms were applied to signals from the MIT-BIH AF database. The accuracy of AF detection on reconstructed signals is evaluated under varying degrees of compression using the state-of-the-art Set Partitioning In Hierarchical Trees (SPIHT) compression algorithm. Results demonstrate that compression ratios (CR) of up to 90 can be obtained while maintaining a detection accuracy, expressed in terms of the area under the receiver operating characteristic curve, of at least 0.9. This highlights the potential for significant energy savings on devices that transmit/store ECG signals for AF detection applications, while preserving the diagnostic integrity of the signals, and hence the detection performance.

Download Full-text