A Noise-Adaptive Method for Detection of Brief Episodes of Paroxysmal Atrial Fibrillation

Abstract The aim of this work is to develop a method for detection of brief episode paroxysmal atrial fibrillation (PAF Introduction The detection of brief episode paroxysmal atrial fibrillation (PAF) is an important problem to solve since atrial fibrillation (AF) is a progressive disorder. If not treated, PAF usually becomes more frequent and longer until it becomes permanent Automatic AF detection can be done in different waysone is based on identification of P-wave absence and another on the analysis of RR interval irregularity. Since P-waves are not apparent during AF such knowledge can be combined with RR irregularity information in order to improve the performance of AF detection Recently, there has been a growing interest in developing algorithms for detection of brief AF episodes. A sample entropy based method was proposed that is capable of detecting AF using only 12 consecutive RR intervals A novel detector architecture was recently proposed, where information on P wave presence/absence, heart rate irregularity, and atrial activity analysis was combined, using an artificial neural network as classifier In this study, the proposed method is based on atrial activity extraction using an echo state network (ESN) recently introduced as a unified solution to the problem of QRST cancellation in the presence of substantial variation in beat morphology and/or occasional ectopic beats Methods The main processing steps of the proposed AF detector are illustrated i

Low-complexity detection of atrial fibrillation in continuous long-term monitoring.

This study describes an atrial fibrillation (AF) detector whose structure is well-adapted both for detection of subclinical AF episodes and for implementation in a battery-powered device for use in continuous long-term monitoring applications. A key aspect for achieving these two properties is the use of an 8-beat sliding window, which thus is much shorter than the 128-beat window used in most existing AF detectors. The building blocks of the proposed detector include ectopic beat filtering, bigeminal suppression, characterization of RR interval irregularity, and signal fusion. With one design parameter, the performance can be tuned to put more emphasis on avoiding false alarms due to non-AF arrhythmias or more emphasis on detecting brief AF episodes. Despite its very simple structure, the results show that the detector performs better on the MIT-BIH Atrial Fibrillation database than do existing detectors, with high sensitivity and specificity (97.1% and 98.3%, respectively). The detector can be implemented with just a few arithmetical operations and does not require a large memory buffer thanks to the short window

Modeling of the photoplethysmogram during atrial fibrillation

A phenomenological model for simulating the photoplethysmogram (PPG) during atrial fibrillation (AF) is proposed. The simulated PPG is solely based on RR interval information, and, therefore, any annotated ECG database can be used to model sinus rhythm, AF, or rhythms with premature beats. A PPG pulse is modeled by a linear combination of a log-normal and two Gaussian waveforms. The model PPG is obtained by placing individual pulses according to the RR intervals so that a connected signal is created. The model is evaluated on synchronously recorded ECG and PPG signals from the MIMIC and the University of Queensland Vital Signs Dataset databases. The results show that the model PPG signals closely resemble real signal for sinus rhythm, premature beats, as well as for AF. The model is used to study the performance of a low-complexity RR interval-based AF detector on simulated PPG signals with five different pulse types generated using the MIT–BIH AF database at signal-to-noise ratios (SNRs) from 0 to 30 dB. PPGs composed of pulses with a dicrotic notch tend to increase the rate of false alarms, especially at lower SNRs. The model is capable of generating simulated PPG signals from RR interval series with sinus rhythm, AF, and premature beats. Considering the lack of annotated, public PPG databases with arrhythmias, the simulation of realistic PPG signals based on annotated ECG signals is expected to facilitate the development and testing of PPG-specific AF detectors

Estimation of otoacoustic emision signals by using synchroneous averaging method

The study presents the investigation results of synchro­nous averaging method and its application in estimation of impulse evoked otoacoustic emission signals (IEOAE). The method was analyzed using synthetic and real signals. Synthetic signals were modeled as the mixtures of deterministic compo­nent with noise realizations. Two types of noise were used: normal (Gaussian) and transient impulses dominated (Lapla­cian). Signal to noise ratio was used as the signal quality measure after processing. In order to account varying amplitude of deterministic component in the realizations weighted aver­aging method was investigated. Results show that the perfor­mance of synchronous averaging method is very similar in case of both types of noise Gaussian and Laplacian. Weighted aver­aging method helps to cope with varying deterministic component or noise level in case of nonhomogenous ensembles as is the case in IEOAE signal. Article in Lithuanian. Otoakustinės emisijos signalų įvertinimas panaudojant sinchroninį vidurkinimą Santrauka. Nagrinėjamos sinchroninio vidurkinimo metodo galimybės išskirti otoakustinės emisijos (OAE) signalus iš triukšmo. Metodas ištirtas naudojant sintetinius ir realius signalus. Sumodeliuotu sintetinių signalų ansambliu įvertinta triukšmo standartinės deviacijos ir vidurkio įtaka signalo ir triukšmo santykiui (STS). Realių OAE signalų atveju įgyvendinti ir palyginti sinchroninio ir pasverto vidurkinimo metodai. Tyrimas rodo, kad signalų įvertinimo rezultatams didelę įtaką turi ansamblio realizacijų skaičius, realizacijų vidurkis, ansamblio nehomogeniškumas dėl determinuotos dedamosios amplitudės varijavimo, o triukšmo pobūdžio (Gauso ar Laplaso) didelės įtakos rezultatams nepastebėta. Raktiniai žodžiai: otoakustinė emisija; sinchroninis vidurkinimas; signalo ir triukšmo santykis

Estimation of Heart Rate Recovery after Stair Climbing Using a Wrist-Worn Device

Heart rate recovery (HRR) after physical exercise is a convenient method to assess cardiovascular autonomic function. Since stair climbing is a common daily activity, usually followed by a slow walking or rest, this type of activity can be considered as an alternative HRR test. The present study explores the feasibility to estimate HRR parameters after stair climbing using a wrist-worn device with embedded photoplethysmography and barometric pressure sensors. A custom-made wrist-worn device, capable of acquiring heart rate and altitude, was used to estimate the time-constant of exponential decay τ , the short-term time constant S , and the decay of heart rate in 1 min D . Fifty-four healthy volunteers were instructed to climb the stairs at three different climbing rates. When compared to the reference electrocardiogram, the absolute and percentage errors were found to be ≤ 21.0 s (≤ 52.7%) for τ , ≤ 0.14 (≤ 19.2%) for S , and ≤ 7.16 bpm (≤ 20.7%) for D in 75% of recovery phases available for analysis. The proposed approach to monitoring HRR parameters in an unobtrusive way may complement information provided by personal health monitoring devices (e.g., weight loss, physical activity), as well as have clinical relevance when evaluating the efficiency of cardiac rehabilitation program outside the clinical setting

Quantitative evaluation of temporal episode patterns in paroxysmal atrial fibrillation

Flow velocity in left atrial appendage decreases when paroxysmal atrial fibrillation (PAF) progresses to longer episodes, suggesting that the temporal PAF episode pattern may be related to risk of thrombus formation. This study investigates the feasibility of discriminating episode patterns based on two descriptors: the aggregation characterizes the temporal distribution of PAF episodes, whereas the Gini coefficient characterizes differences in episode duration. The descriptors were studied on three PhysioNet databases with annotated PAF episodes, resulting in a total of 102 recordings. Three types of patterns were defined: congregation of several episodes in a single and multiple clusters, and episodes dispersed over the entire monitoring period. The results show that the aggregation descriptor achieves large values for patterns with a single and multiple clusters (0.76± 0.07 and 0.60± 0.08, respectively). In contrast, much lower values are obtained for dispersed episode patterns (0.10± 0.05). The Gini coefficient is better suited for discriminating among the patterns with high PAF burden and, therefore, represents a descriptor which is complementary to aggregation. Both descriptors may have relevance when studying the relationship between episode pattern and the risk of thrombus formation

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

An approach to atrial fibrillation (AF) frequency tracking in long-term ambulatory ECG recordings is presented, comprising f-wave extraction, dominant atrial frequency (DAF) tracking, and signal quality assessment. Since poor signal quality is commonly encountered in ambulatory monitoring, a recently proposed index is employed to assess f-wave signal quality in a database containing 38 patients with permanent AF. The index ensures that DAF outliers, typically associated with poor-quality segments, are excluded from further analysis. 40% of all 5-s signal segments were excluded from the database due to poor quality. The exclusion of DAF outliers significantly reduces the standard deviation of the frequency estimates (p≤0.01), allowing more reliable evaluation of the difference between day- and night-time DAF. The results show that signal quality assessment plays a central role in DAF tracking, and therefore should be employed in ambulatory monitoring

ECG-based monitoring of electrolyte fluctuations during the long interdialytic interval

Hemodialysis (HD) patients have a higher risk of sudden death due to cardiac arrhythmias, which commonly occur during the long interdialytic interval (LII) as a result of electrolyte fluctuations (EFs). Noninvasive monitoring of EFs would enable restoring normal serum electrolyte levels (SELs) by performing early HD before the onset of arrhythmias. In this study, we propose an ECGderived descriptor, […], that is noise robust and capable of capturing EFs during HD and the LII. To investigate the variation of […], ECG and blood samples of 3 patients were acquired continuously, starting at Friday’s HD and ending at Monday’s HD. Results show that the increase of […] during Friday’s HD is correlated with the decrease of SELs. Moreover, […] tends to decrease during the LII (no blood samples were obtained) and further increases during Monday’s HD. If results in larger databases are confirmed, […] might be suitable for noninvasive monitoring of EFs during the LII