Coefficient Of Sample Entropy Research Articles

A novel single-lead handheld atrial fibrillation detection system

Objective. The single-lead handheld atrial fibrillation (AF) detection device is suitable for daily monitoring or early screening of AF in the hospital. However, the signal quality and the reliability of AF detection algorithm still need to be improved. This study proposed a novel AF detection system with a user-friendly interaction and a lightweight and accurate AF detection algorithm. Approach. The system consisted of a single-lead handheld electrocardiogram device with a novel appearance like a gaming handle and a smartphone terminal embedded with AF detection. After feature optimization, the rule-based multi-feature AF detection algorithm had relatively good AF detection ability. Three types of experiments were designed to test the performance of the system. (1) Test the accuracy and time/memory cost of the AF detection algorithm. (2) Compare the proposed device with the standard device Shimmer. (3) Use the simulator to test the effectiveness of the system. Main results. The percentage of differences of successive RR intervals larger than 50 ms (PNN50), minimum value of RR intervals (minRR), and coefficient of sample entropy (COSEn) were features chosen for AF detection. (1) The sensitivity, specificity, and accuracy were 96.00%, 99.75%, 97.88% on the MIT-BIH AF database, and 98.50%, 94.50%, 96.50% on the clinical database we founded. The time/memory cost of the proposed algorithm was much smaller than that of support vector machine. (2) The mean correlation coefficient of RR was 0.9950, indicating a high degree of consistency. (3) This system showed the effectiveness of AF detection. Significance. The proposed single-lead handheld AF detection system is demonstrated to be accurate, lightweight, consistent with the standard device, and efficient for AF detection.

A New Entropy-Based Atrial Fibrillation Detection Method for Scanning Wearable ECG Recordings.

Entropy-based atrial fibrillation (AF) detectors have been applied for short-term electrocardiogram (ECG) analysis. However, existing methods suffer from several limitations. To enhance the performance of entropy-based AF detectors, we have developed a new entropy measure, named EntropyAF, which includes the following improvements: (1) use of a ranged function rather than the Chebyshev function to define vector distance, (2) use of a fuzzy function to determine vector similarity, (3) replacement of the probability estimation with density estimation for entropy calculation, (4) use of a flexible distance threshold parameter, and (5) use of adjusted entropy results for the heart rate effect. EntropyAF was trained using the MIT-BIH Atrial Fibrillation (AF) database, and tested on the clinical wearable long-term AF recordings. Three previous entropy-based AF detectors were used for comparison: sample entropy (SampEn), fuzzy measure entropy (FuzzyMEn) and coefficient of sample entropy (COSEn). For classifying AF and non-AF rhythms in the MIT-BIH AF database, EntropyAF achieved the highest area under receiver operating characteristic curve (AUC) values of 98.15% when using a 30-beat time window, which was higher than COSEn with AUC of 91.86%. SampEn and FuzzyMEn resulted in much lower AUCs of 74.68% and 79.24% respectively. For classifying AF and non-AF rhythms in the clinical wearable AF database, EntropyAF also generated the largest values of Youden index (77.94%), sensitivity (92.77%), specificity (85.17%), accuracy (87.10%), positive predictivity (68.09%) and negative predictivity (97.18%). COSEn had the second-best accuracy of 78.63%, followed by an accuracy of 65.08% in FuzzyMEn and an accuracy of 59.91% in SampEn. The new proposed EntropyAF also generated highest classification accuracy when using a 12-beat time window. In addition, the results from time cost analysis verified the efficiency of the new EntropyAF. This study showed the better discrimination ability for identifying AF when using EntropyAF method, indicating that it would be useful for the practical clinical wearable AF scanning.

Comparison between electrocardiogram- and photoplethysmogram-derived features for atrial fibrillation detection in free-living conditions

Objective: Atrial fibrillation (AF) is the most commonly experienced arrhythmia and it increases the risk of stroke and heart failure. The challenge in detecting the presence of AF is the occasional and asymptomatic manifestation of the condition. Long-term monitoring can increase the sensitivity of detecting intermittent AF episodes, however it is either cumbersome or invasive and costly with electrocardiography (ECG). Photoplethysmography (PPG) is an unobtrusive measuring modality enabling heart rate monitoring, and promising results have been presented in detecting AF. However, there is still limited knowledge about the applicability of the PPG solutions in free-living conditions. The aim of this study was to compare the inter-beat interval derived features for AF detection between ECG and wrist-worn PPG in daily life. Approach: The data consisted of 24 h ECG, PPG, and accelerometer measurements from 27 patients (eight AF, 19 non-AF). In total, seven features (Shannon entropy, root mean square of successive differences (RMSSD), normalized RMSSD, pNN40, pNN70, sample entropy, and coefficient of sample entropy (CosEn)) were compared. Body movement was measured with the accelerometer and used with three different thresholds to exclude PPG segments affected by movement. Main results: CosEn resulted as the best performing feature from ECG with Cohens kappa 0.95. When the strictest movement threshold was applied, the same performance was obtained with PPG (kappa = 0.96). In addition, pNN40 and pNN70 reached similar results with the same threshold (kappa = 0.95 and 0.94), but were more robust with respect to movement artefacts. The coverage of PPG was 24.0%–57.6% depending on the movement threshold compared to 92.1% of ECG. Significance: The inter-beat interval features derived from PPG are equivalent to the ones from ECG for AF detection. Movement artefacts substantially worsen PPG-based AF monitoring in free-living conditions, therefore monitoring coverage needs to be carefully selected. Wrist-worn PPG still provides a promising technology for long-term AF monitoring.

A comparison of entropy approaches for AF discrimination

Objective: This study focuses on the comparison of single entropy measures for ventricular response analysis-based AF detection. Approach: To enhance the performance of entropy-based AF detectors, we developed a normalized fuzzy entropy, , a novel metric that (1) uses a fuzzy function to determine vector similarity, (2) replaces probability estimation with density estimation for entropy approximation, (3) utilizes a flexible distance threshold parameter, and (4) adjusts for heart rate by subtracting the natural log value of the mean RR interval. An AF detector based on was trained using the MIT-BIH atrial fibrillation (AF) database, and tested on the MIT-BIH normal sinus rhythm (NSR) and MIT-BIH arrhythmia databases. The -based AF detector was compared to AF detectors based on three other entropy measures: sample entropy (), fuzzy measure entropy () and coefficient of sample entropy (), over three standard window sizes. Main results: To classify AF and non-AF rhythms, achieved the highest area under receiver operating characteristic curve (AUC) values of 92.72%, 95.27% and 96.76% for 12-, 30- and 60-beat window lengths respectively. This was higher than the performance of the next best technique, , over all windows sizes, which provided respective AUCs of 91.12%, 91.86% and 90.55%. and resulted in lower AUCs (below 90%) over all window sizes. also provided superior performance for all other tested statistics, including the Youden index, sensitivity, specificity, accuracy, positive predictivity and negative predictivity. In conclusion, we show that can be used to accurately identify AF from RR interval time series. Furthermore, longer window lengths (up to one minute) increase the performance of all entropy-based AF detectors under evaluation except the -based method. Significance: Our results demonstrate that the new developed normalized fuzzy entropy is an accurate measure for detecting AF.

Characterization of rate and regularity of ventricular response during atrial tachyarrhythmias. Insight on atrial and nodal determinants

Objective: Ventricular rate and regularity affect hemodynamic stability and are targets of rate control therapy in patients with atrial flutter (AFL) and atrial fibrillation (AF). Nevertheless, the characteristics and determinants of ventricular response during these atrial arrhythmias are poorly understood. This work aims to investigate the effects of changes in atrial activity and atrioventricular (AV) conduction patterns on ventricular response in patients with atrial tachyarrhythmias by a combined quantitative analysis of atrial and ventricular time series. Approach: Time series were determined in patients during 24 episodes of AFL and 13 episodes of AF. Ventricular response was characterized in terms of mean ventricular interval, time-domain variability parameters, and entropy-based irregularity measures (quadratic entropy rate, QSE, and coefficient of sample entropy, COSEn). Atrial activity was characterized in terms of mean atrial rate and variability. AV conduction was analyzed by AV synchrogram method, quantifying the stability of AV coupling and the average AV conduction ratio. The dependence of ventricular features on atrial/AV determinants was investigated by correlation analysis. Main results: The analysis showed ventricular response to display a wide spectrum of variability and irregularity features at changing atrial and AV properties. The ventricular interval was significantly correlated with the AV conduction ratio (Spearman correlation coefficient ρ = −0.76, p < 0.005), while it did not correlate with atrial rate. The variability and irregularity of ventricular response were influenced by both atrial and AV parameters. QSE and COSEn significantly increased at shorter atrial cycle lengths (ρ = −0.67 and −0.71, p < 0.005), at higher atrial variability (ρ = 0.80 and 0.77, p < 0.005), at reduced AV coupling (ρ = −0.91, p < 0.005) and at smaller AV conduction ratios (ρ = −0.65 and −0.52, p < 0.005). Significance: The identification of the determinants of ventricular rate and regularity may have significant implications in the development of efficacious rate control strategies in patients with atrial tachyarrhythmias.

Automated detection of atrial fibrillation using R-R intervals and multivariate-based classification

Automated detection of AF from the electrocardiogram (ECG) still remains a challenge. In this study, we investigated two multivariate-based classification techniques, Random Forests (RF) and k-nearest neighbor (k-nn), for improved automated detection of AF from the ECG. We have compiled a new database from ECG data taken from existing sources. R-R intervals were then analyzed using four previously described R-R irregularity measurements: (1) the coefficient of sample entropy (CoSEn), (2) the coefficient of variance (CV), (3) root mean square of the successive differences (RMSSD), and (4) median absolute deviation (MAD). Using outputs from all four R-R irregularity measurements, RF and k-nn models were trained. RF classification improved AF detection over CoSEn with overall specificity of 80.1% vs. 98.3% and positive predictive value of 51.8% vs. 92.1% with a reduction in sensitivity, 97.6% vs. 92.8%. k-nn also improved specificity and PPV over CoSEn; however, the sensitivity of this approach was considerably reduced (68.0%).

Detection of occult paroxysmal atrial fibrillation.

This work introduces a novel approach to the detection of brief episodes of paroxysmal atrial fibrillation (PAF). The proposed detector is based on four parameters which characterize RR interval irregularity, P-wave absence, f-wave presence, and noise level, of which the latter three are determined from a signal produced by an echo state network. The parameters are used for fuzzy logic classification where the decisions involve information on prevailing signal quality; no training is required. The performance is evaluated on a large set of test signals with brief episodes of PAF. The results show that episodes with as few as five beats can be reliably detected with an accuracy of 0.88, compared to 0.82 for a detector based on rhythm information only (the coefficient of sample entropy); this difference in accuracy increases when atrial premature beats are present. The results also show that the performance remains essentially unchanged at noise levels up to [Formula: see text] RMS. It is concluded that the combination of information on ventricular activity, atrial activity, and noise leads to substantial improvement when detecting brief episodes of PAF.

Accuracy of algorithms for detection of atrial fibrillation from short duration beat interval recordings

Atrial fibrillation (AF) is characterised by highly variable beat intervals. The aims of the study were to assess the accuracy of AF detection algorithms from short analysis durations and to validate prospectively the accuracy on a large community-based cohort of elderly subjects. Three algorithms for AF detection were evaluated: coefficient of variation (CV), mean successive difference (Δ) and coefficient of sample entropy (COSEn), using two databases of beat interval recordings: 167 recordings of 300s duration for a range of rhythms acquired in a hospital setting and 2130 recordings of 10s duration acquired in the community. Using the longer recordings receiver operating characteristic (ROC) analysis was used to identify optimal algorithm thresholds and to evaluate analysis durations ranging from 5s to 60s. An ROC area of 93% was obtained at recording duration of 60s but remained above 90% for durations as low as 5s. Prospective analysis on the 2130 recordings gave AF detector sensitivities from 90.5% (CV and Δ) to 95.2% (COSEn), specificities from 89.3% (Δ) to 93.4% (COSEn) and accuracy from 89.3% (Δ) to 93.4% (COSEn), not significantly different to those obtained on the initial database. AF detection algorithms are effective for short analysis durations, offering the prospect of a simple and rapid diagnostic test based on beat intervals alone.

Accurate estimation of entropy in very short physiological time series: the problem of atrial fibrillation detection in implanted ventricular devices

Entropy estimation is useful but difficult in short time series. For example, automated detection of atrial fibrillation (AF) in very short heart beat interval time series would be useful in patients with cardiac implantable electronic devices that record only from the ventricle. Such devices require efficient algorithms, and the clinical situation demands accuracy. Toward these ends, we optimized the sample entropy measure, which reports the probability that short templates will match with others within the series. We developed general methods for the rational selection of the template length m and the tolerance matching r. The major innovation was to allow r to vary so that sufficient matches are found for confident entropy estimation, with conversion of the final probability to a density by dividing by the matching region volume, 2r(m). The optimized sample entropy estimate and the mean heart beat interval each contributed to accurate detection of AF in as few as 12 heartbeats. The final algorithm, called the coefficient of sample entropy (COSEn), was developed using the canonical MIT-BIH database and validated in a new and much larger set of consecutive Holter monitor recordings from the University of Virginia. In patients over the age of 40 yr old, COSEn has high degrees of accuracy in distinguishing AF from normal sinus rhythm in 12-beat calculations performed hourly. The most common errors are atrial or ventricular ectopy, which increase entropy despite sinus rhythm, and atrial flutter, which can have low or high entropy states depending on dynamics of atrioventricular conduction.

Abstract 2168: Efficient Detection of Atrial Fibrillation and Congestive Heart Failure in Single-Lead Devices

Patients receiving primary prevention single lead ICDs are at risk for atrial fibrillation (AF) and congestive heart failure (CHF). No such device reports AF burden, and only a single CHF measure, trans-thoracic impedance, is available. Entropy measures that count the number of matching RR intervals have promise, as AF is random (high entropy) and CHF is often marked by reduced heart rate variability (RR intervals with many matches) and ectopic beats (few matches). We designed entropy-based measures to detect AF (high entropy) and CHF (mixture of RR intervals with many and with few matches). For real-world implementation, we used only 12 RR intervals, and calculated the result every 30 minutes in 24-hour Holter monitor records from the MIT-BIH databases. The Figure shows distinction among AF, NSR and CHF records using HR and S.D. (panel A) or the new entropy-based measures. Panel A shows poor diagnostic performance of conventional measures. In Panel B, the y-axis, COSEn, is the coefficient of sample entropy. The AF records all have higher values, and the ROC area is 1.00. The x-axis is a measure of template match counts. It distinguishes between normals and CHF patients with ROC area 0.92. With only 12 RR intervals every 30 minutes, entropy calculations allow for efficient detection of AF and CHF. We propose that single lead devices can be employed as monitors in the primary prevention population, where risk of AF and CHF is high.