| 1. |
- Bachi, Lorenzo, et al.
(författare)
-
ECG Modeling for Simulation of Arrhythmias in Time-Varying Conditions
- 2023
-
Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 70:12, s. 3449-3460
-
Tidskriftsartikel (refereegranskat)abstract
- The present paper proposes an ECG simulator that advances modeling of arrhythmias and noise by introducing time-varying signal characteristics. The simulator is built around a discrete-time Markov chain model for simulating atrial and ventricular arrhythmias of particular relevance when analyzing atrial fibrillation (AF). Each state is associated with statistical information on episode duration and heartbeat characteristics. Statistical, time-varying modeling of muscle noise, motion artifacts, and the influence of respiration is introduced to increase the complexity of simulated ECGs, making the simulator well suited for data augmentation in machine learning. Modeling of how the PQ and QT intervals depend on heart rate is also introduced. The realism of simulated ECGs is assessed by three experienced doctors, showing that simulated ECGs are difficult to distinguish from real ECGs. Simulator usefulness is illustrated in terms of AF detection performance when either simulated or real ECGs are used to train a neural network for signal quality control. The results show that both types of training lead to similar performance.
|
|
| 2. |
- Ben-Moshe, Noam, et al.
(författare)
-
RawECGNet : Deep Learning Generalization for Atrial Fibrillation Detection From the Raw ECG
- 2024
-
Ingår i: IEEE Journal of Biomedical and Health Informatics. - 2168-2194. ; , s. 1-10
-
Tidskriftsartikel (refereegranskat)abstract
- Introduction Deep learning models for detecting episodes of atrial fibrillation (AF) using rhythm information in long-term ambulatory ECG recordings have shown high performance. However, the rhythm-based approach does not take advantage of the morphological information conveyed by the different ECG waveforms, particularly the f-waves. As a result, the performance of such models may be inherently limited. Methods: To address this limitation, we have developed a deep learning model, named RawECGNet, to detect episodes of AF and atrial flutter (AFl) using the raw, single-lead ECG. We compare the generalization performance of RawECGNet on two external data sets that account for distribution shifts in geography, ethnicity, and lead position. RawECGNet is further benchmarked against a state-of-the-art deep learning model, named ArNet2, which utilizes rhythm information as input. Results: Using RawECGNet, the results for the different leads in the external test sets in terms of the F1 score were 0.91–0.94 in RBDB and 0.93 in SHDB, compared to 0.89–0.91 in RBDB and 0.91 in SHDB for ArNet2. The results highlight RawECGNet as a high-performance, generalizable algorithm for detection of AF and AFl episodes, exploiting information on both rhythm and morphology.
|
|
| 3. |
- Biton, Shany, et al.
(författare)
-
Estimation of f-wave Dominant Frequency Using a Voting Scheme
- 2022
-
Ingår i: Computing in Cardiology, CinC 2022. - 9798350300970
-
Konferensbidrag (refereegranskat)abstract
- Atrial fibrillation (AF) is the most common heart arrhythmia, characterized by the presence of fibrillatory waves (f-waves) in the ECG. We introduce a voting scheme to estimate the dominant atrial frequency (DAF) of f-waves. Methods: We analysed a subset of Holter recordings obtained from the University of Virginia AF Database. 100 Holter recordings with manually annotated AF events, resulting in a total 363 AF events lasting more than 1 min. The f-waves were extracted using four different template subtraction (TS) algorithms and the DAF was estimated from the first 1-min window of each AF event. A random forest classifier was used. We hypothesized that better extraction of the f-wave meant better AF/non-AF classification using the DAF as the single input feature of the RF model. Results: Performance on the test set, expressed in terms of AF/non-AF classification, was the best when the DAF was computed computed the three best-performing extraction methods. Using these three algorithms in a voting scheme, the classifier obtained AUC=0.60 and the DAFs were mostly spread around 6 Hz, 5.66 (4.83-7.47). Conclusions: This study has two novel contributions: (1) a method for assessing the performance of f-wave extraction algorithms, and (2) a voting scheme for improved DAF estimation.
|
|
| 4. |
- Butkuviene, Monika, et al.
(författare)
-
Atrial Fibrillation Episode Patterns and Their Influence on Detection Performance
- 2021
-
Ingår i: 2021 Computing in Cardiology, CinC 2021. - 2325-8861 .- 2325-887X. - 9781665479165 ; 2021-September
-
Konferensbidrag (refereegranskat)abstract
- Existing studies offer little insight on how atrial fibrillation (AF) detection performance is influenced by the properties of AF episode patterns. The aim of this study is to investigate the influence of AF burden and median AF episode length on detection performance. For this purpose, three types of AF detectors, using either information on rhythm, rhythm and morphology, or ECG segments, were investigated on 1-h simulated ECGs. Comparing AF burdens of 20% and 80% for a median episode length of 167 beats, the sensitivity of the rhythm- and morphology-based detector increases only slightly whereas the specificity drops from 99.5% to 93.3%. The corresponding figures of specificity are 99.0% and 90.6% for the rhythm-based detector; 88.1% and 70.7% for the segment-based detector. The influence of AF burden on specificity becomes even more pronounced for AF patterns with brief episodes (median episode length set to 30 beats). Therefore, patterns with briefepisodes and high AF burden imply higher demands on detection performance. Future research should focus on how well episode patterns are captured.
|
|
| 5. |
- Butkuviene, Monika, et al.
(författare)
-
Characterization of Atrial Fibrillation Episode Patterns : A Comparative Study
- 2024
-
Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 71:1, s. 106-113
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: The episode patterns of paroxysmal atrial fibrillation (AF) may carry important information on disease progression and complication risk. However, existing studies offer very little insight into to what extent a quantitative characterization of AF patterns can be trusted given the errors in AF detection and various types of shutdown, i.e., poor signal quality and non-wear. This study explores the performance of AF pattern characterizing parameters in the presence of such errors. Methods: To evaluate the performance of the parameters AF aggregation and AF density, both previously proposed to characterize AF patterns, the two measures mean normalized difference and the intraclass correlation coefficient are used to describe agreement and reliability, respectively. The parameters are studied on two PhysioNet databases with annotated AF episodes, also accounting for shutdowns due to poor signal quality. Results: The agreement is similar for both parameters when computed for detector-based and annotated patterns, which is 0.80 for AF aggregation and 0.85 for AF density. On the other hand, the reliability differs substantially, with 0.96 for AF aggregation but only 0.29 for AF density. This finding suggests that AF aggregation is considerably less sensitive to detection errors. The results from comparing three strategies to handle shutdowns vary considerably, with the strategy that disregards the shutdown from the annotated pattern showing the best agreement and reliability. Conclusions: Due to its better robustness to detection errors, AF aggregation should be preferred. To further improve performance, future research should put more emphasis on AF pattern characterization.
|
|
| 6. |
- Butkuviene, Monika, et al.
(författare)
-
Considerations on Performance Evaluation of Atrial Fibrillation Detectors
- 2021
-
Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 68:11, s. 3250-3260
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: A large number of atrial fibrillation (AF) detectors have been published in recent years, signifying that the comparison of detector performance plays a central role, though not always consistent. The aim of this study is to shed needed light on aspects crucial to the evaluation of detection performance. Methods: Three types of AF detector, using either information on rhythm, rhythm and morphology, or segments of ECG samples, are implemented and studied on both real and simulated ECG signals. The properties of different performance measures are investigated, for example, in relation to dataset imbalance. Results: The results show that performance can differ considerably depending on the way detector output is compared to database annotations, i.e., beat-to-beat, segment-to-segment, or episode-to-episode comparison. Moreover, depending on the type of detector, the results substantiate that physiological and technical factors, e.g., changes in ECG morphology, rate of atrial premature beats, and noise level, can have a considerable influence on performance. Conclusion: The present study demonstrates overall strengths and weaknesses of different types of detector, highlights challenges in AF detection, and proposes five recommendations on how to handle data and characterize performance.
|
|
| 7. |
- Halvaei, Hesam, et al.
(författare)
-
Detection of Short Supraventricular Tachycardias in Single-lead ECGs Recorded Using a Handheld Device
- 2022
-
Ingår i: Computing in Cardiology, CinC 2022. - 9798350300970
-
Konferensbidrag (refereegranskat)abstract
- Short supraventricular tachycardias (S-SVTs) have been associated with a higher risk of developing atrial fibrillation (AF). Hence, identification of participants with such arrhythmias may increase the yield of AF screening. However, the lower signal quality of ECGs recorded using handheld screening devices challenges the detection of S-SVT. In the present work, a new method for detection of S-SVT is presented, which is based on the requirement on morphologic similarity between the detected beats. Specifically, any episode with a sequence of beats of similar morphology is considered as an S-SVT candidate while any episode with detections of different morphology, either due to signal disturbances or aberrant ectopic beats, is excluded. For this purpose, a support vector machine (SVM) was trained and validated, using a simulated ECG database, to classify an episode as either consisting of beats of similar or non-similar morphologies. Episodes identified as S-SVT candidates are subject to two further rhythm criteria in order to confirm the presence of an S-SVT. The performance of the S-SVT detector is evaluated using a subset of the StrokeStop I database (305 S-SVT out of 8258), resulting in a sensitivity, specificity, and positive predictive value of 88.8%, 92.0%, and 29.9%, respectively. In conclusion, the results suggest that the detection of S-SVT in AF screening can be done at an acceptable balance between sensitivity and positive predictive value.
|
|
| 8. |
- Henriksson, Mikael, et al.
(författare)
-
Modeling and Estimation of Temporal Episode Patterns in Paroxysmal Atrial Fibrillation
- 2021
-
Ingår i: IEEE Transactions on Biomedical Engineering. - 0018-9294. ; 68:1, s. 319-329
-
Tidskriftsartikel (refereegranskat)abstract
- Objective: The present study proposes a model-based, statistical approach to characterizing episode patterns in paroxysmal atrial fibrillation (AF). Thanks to the rapid advancement of noninvasive monitoring technology, the proposed approach should become increasingly relevant in clinical practice. Methods: History-dependent point process modeling is employed to characterize AF episode patterns, using a novel alternating, bivariate Hawkes self-exciting model. In addition, a modified version of a recently proposed statistical model to simulate AF progression throughout a lifetime is considered, involving non-Markovian rhythm switching and survival functions. For each model, the maximum likelihood estimator is derived and used to find the model parameters from observed data. Results: Using three databases with a total of 59 long-term ECG recordings, the goodness-of-fit analysis demonstrates that the proposed alternating, bivariate Hawkes model fits SR-to-AF transitions in 40 recordings and AF-to-SR transitions in 51; the corresponding numbers for the AF model with non-Markovian rhythm switching are 40 and 11, respectively. Moreover, the results indicate that the model parameters related to AF episode clustering, i.e., aggregation of temporal AF episodes, provide information complementary to the well-known clinical parameter AF burden. Conclusion: Point process modeling provides a detailed characterization of the occurrence pattern of AF episodes that may improve the understanding of arrhythmia progression.
|
|
| 9. |
- Kontaxis, Spyridon, et al.
(författare)
-
Investigating Respiratory Rate Estimation during Paroxysmal Atrial Fibrillation Using an Improved ECG Simulation Model
- 2020
-
Ingår i: 2020 Computing in Cardiology, CinC 2020. - 2325-887X .- 2325-8861. - 9781728173825 ; 2020-September
-
Konferensbidrag (refereegranskat)abstract
- The present study addresses the problem of respiratory rate estimation from ECG-derived respiration (EDR) signals during paroxysmal atrial fibrillation (AF). Novel signal-to-noise ratios between various components of the ECG including the influence of respiration, measured by QRS ensemble variance, the amplitude of fibrillatory waves (f-waves), and the QRS amplitude are introduced to characterize EDR performance. Using an improved ECG simulation model accounting for morphological variation induced by respiration, the results show that 1. the error in estimating the respiratory rate increases as a function of the time spent in AF, 2. the leads farthest away from the atria, i.e., V_{4}, V_{5}, V_{6}, exhibit the best performance due to lower f-wave amplitudes, 3. lower errors in leads with similar f-wave amplitude are due to a more pronounced respiratory influence, and 4. the respiratory influence is higher in V_{2}, V_{3}, and V_{4} compared to other precordial leads.
|
|
| 10. |
- Martin-Yebra, Alba, et al.
(författare)
-
Characterization of Atrial Fibrillation Episodes Using a Point Process Model
- 2020
-
Ingår i: 2020 11th Conference of the European Study Group on Cardiovascular Oscillations : Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020 - Computation and Modelling in Physiology: New Challenges and Opportunities, ESGCO 2020. - 9781728157511
-
Konferensbidrag (refereegranskat)abstract
- The purpose of the present study is to introduce a point process model for characterizing the pattern of atrial fibrillation (AF) episodes. A variant of the bivariate Hawkes process is proposed, accounting for clustered episodes. The model parameters are inferred by the maximum likelihood method. The goodness-of-fit analysis show that model fits the data in most of the recordings (27 out of 32). The information provided by this approach is complementary to AF burden.
|
|
