Model-Based Assessment of f-Wave Signal Quality in Patients With Atrial Fibrillation

Henriksson, Mikael; Petrėnas, Andrius; Marozas, Vaidotas; Sandberg, Frida; Sörnmo, Leif

doi:10.1109/tbme.2018.2810508

Cited by 27 publications

(32 citation statements)

References 35 publications

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“…Sufficient signal quality is crucial for the analysis; our simulation results shows that respiratory modulation cannot be accurately estimated when the SQI above 0.45. Accurate estimation of respiratory modulation in the f-wave frequency trend requires a larger SQI than the threshold previously proposed in [7] when the aim was to improve AF detection. In this study, the respiration rate is assumed to be known and fixed.…”

Section: Discussionmentioning

confidence: 99%

“…1. After extracting f-waves x r (t) from ECG by applying spatiotemporal QRST cancellation [6], a model-based approach is employed to estimate the local f-wave frequency [7]. If the f-wave signal quality is sufficient for analysis, respiratory modulation is obtained from the fwave frequency trend using bandpass filtering and envelope estimation.…”

Section: Methodsmentioning

confidence: 99%

“…The local f-wave frequency is estimated using a modelbased approach [7]. In this method, the Hilbert transform is employed to construct a complex-valued analytical signal x c (t) from the real-valued x r (t), and a model signal s(t) is fitted to x c (t).…”

Section: Local F-wave Frequencymentioning

confidence: 99%

“…The corresponding local frequency estmatesF 0 provides an estimate of the f-wave frequency trendf (t). The normalized RMS of the model error x c (t) −ŝ(t), is used to compute a signal quality index (SQI) [7]. The SQI can take values between 0 and 1, where 1 indicates a perfect model fit.…”

Section: Local F-wave Frequencymentioning

confidence: 99%

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Respiratory Modulation in Permanent Atrial Fibrillation

Abdollahpur

Holmqvist

Platonov

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Several studies have shown that the autonomic nervous system (ANS) can induce changes during atrial fibrillation (AF). There is currently a lack of methods for quantifying ANS induced variations during AF. The purpose of this study is to quantify respiratory induced modulation in the f-wave frequency trend. Following qrst-cancellation, the local f-wave frequency is estimated by fitting a harmonic f-wave model signal and a quality index (SQI) is computed based on the model fit. The resulting frequency trend is filtered using a narrow bandpass filter with a center frequency corresponding to the local respiration rate. The magnitude of the respiratory induced f-wave frequency modulation is estimated by the envelope of the filtered frequency trend. The performance of the method is validated using simulations and the method is applied to analyze ECG data from eight patients with permanent AF recorded during 0.125 Hz frequency controlled respiration before and after the full vagal blockade, respectively. Results from simulated data show the magnitude of the respiratory induced f-wave frequency modulation can be estimated with an error of less than = 0.005Hz if the SQI is above 0.45. The signal quality was sufficient for analysis in 7 out of 8 patients. In 4 patients the magnitude decreased and in 3 patients there was no change.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Local F-wave Frequencymentioning

confidence: 99%

Section: Local F-wave Frequencymentioning

confidence: 99%

See 2 more Smart Citations

Respiratory Modulation in Permanent Atrial Fibrillation

Abdollahpur

Holmqvist

Platonov

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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View full text Add to dashboard Cite

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“…Atrial Fibrillation (AF) is a cardiac arrhythmia defined by abnormalities in the structure or conduction of the heart that promote abnormal atrial impulse formation and/or propagation [1]. It is detected by the absence of P waves and the presence of an irregular rate and rhythm on an electrocardiogram (EKG) [2]. The diagnosis is also classified by the duration of episodes.…”

Section: Introductionmentioning

confidence: 99%

New Onset Paroxysmal Atrial Fibrillation After Electroconvulsive Therapy

Hower

Yang

2020

Cureus

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Electroconvulsive therapy (ECT) is a safe treatment for various psychiatric disorders. During an ECT treatment, an electrical stimulus produces a transient sympathetic response leading to changes in the cardiac rate and rhythm. Rarely, ECT treatment may precipitate atrial fibrillation (AF). In this case report, we present a 70-year-old man with schizoaffective disorder who developed paroxysmal AF after his 38 th ECT treatment. We review his risk factors for AF and propose a possible mechanism of its development. We also discuss potential treatment options to safely resume ECT treatments.

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Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network

Koya

Deepthi

2021

J Ambient Intell Human Comput

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Smartphones that can support and assist the screening of various cardiovascular diseases are gaining popularity in recent years. The timely detection, diagnosis, and treatment of atrial fibrillation (AF) are critical, especially for those who are at risk of stroke. AF detection via screening with wearable devices should always be confirmed by a standard 12-lead electrocardiogram (ECG). However, the inability to perform on-site AF confirmatory testing results in increased patient anxiety, followed by unnecessary diagnostic procedures and treatments. Also, the delay in confirmation procedure may conclude the condition as non-AF while it was indeed present at the time of screening. To overcome these challenges, we propose an efficient on-site confirmatory testing for AF with 12-lead ECG derived from the reduced lead set (RLS) in a wireless body area network (WBAN) environment. The reduction in the number of leads enhances the comfort level of patients as well as minimizes the hurdles associated with continuous telemonitoring applications such as data transmission, storage, and bandwidth of the overall system. The proposed method is characterized by segment-wise regression and a lead selection algorithm, facilitating improved P-wave reconstruction. Further, an efficient AF detection algorithm is proposed by incorporating a novel three-level P-wave evidence score with an RR irregularity evidence score. The proposed on-site AF confirmation test reduces false positives and false negatives by 88% and 53% respectively, compared to single lead screening. In addition, the proposed lead derivation method improves accuracy, F 1 -score, and Matthews correlation coefficient (MCC) for the on-site AF detection compared to existing related methods.

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Model-Based Assessment of f-Wave Signal Quality in Patients With Atrial Fibrillation

Abstract: The use of improves the detection of AF and benefits the analysis of noisy ECGs.

Cited by 27 publications

References 35 publications

Respiratory Modulation in Permanent Atrial Fibrillation

Respiratory Modulation in Permanent Atrial Fibrillation

New Onset Paroxysmal Atrial Fibrillation After Electroconvulsive Therapy

Efficient on-site confirmatory testing for atrial fibrillation with derived 12-lead ECG in a wireless body area network

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