scholarly journals A New Physically Meaningful Threshold of Sample Entropy for Detecting Cardiovascular Diseases

Entropy ◽  
2019 ◽  
Vol 21 (9) ◽  
pp. 830 ◽  
Author(s):  
Xiong ◽  
Liang ◽  
Zhu ◽  
Zhao ◽  
Li ◽  
...  
Keyword(s):  
Cardiovascular Diseases ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Sample Entropy ◽  
Physiological Signals ◽  
Series Length ◽  
Popular Method ◽  
Rr Interval ◽  
Normal Sinus

Sample Entropy (SampEn) is a popular method for assessing the regularity of physiological signals. Prior to the entropy calculation, certain common parameters need to be initialized: Embedding dimension m, tolerance threshold r and time series length N. Nevertheless, the determination of these parameters is usually based on expert experience. Improper assignments of these parameters tend to bring invalid values, inconsistency and low statistical significance in entropy calculation. In this study, we proposed a new tolerance threshold with physical meaning (rp), which was based on the sampling resolution of physiological signals. Statistical significance, percentage of invalid entropy values and ROC curve were used to evaluate the proposed rp against the traditional threshold (rt). Normal sinus rhythm (NSR), congestive heart failure (CHF) as well as atrial fibrillation (AF) RR interval recordings from Physionet were used as the test data. The results demonstrated that the proposed rp had better stability than rt, hence more adaptive to detect cardiovascular diseases of CHF and AF.

scholarly journals Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects

Entropy ◽  
2015 ◽  
Vol 17 (12) ◽  
pp. 6270-6288 ◽  
Author(s):  
Lina Zhao ◽  
Shoushui Wei ◽  
Chengqiu Zhang ◽  
Yatao Zhang ◽  
Xinge Jiang ◽  
...  
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Sample Entropy ◽  
Fuzzy Measure ◽  
Normal Sinus ◽  
Measure Entropy

THE HILBERT MARGINAL SPECTRUM ANALYSIS AND FEATURE EXTRACTION OF SHORT-TERM HEART RATE SIGNAL OF SPONTANEOUS VENTRICULAR TACHYARRHYTHMIA

2011 ◽  
Vol 23 (06) ◽  
pp. 467-478 ◽  
Author(s):  
Hong-Sheng Dong ◽  
Ai-Hua Zhang ◽  
Xiao-Hong Hao
Keyword(s):  
High Frequency ◽  
Amplitude Ratio ◽  
Normal Sinus Rhythm ◽  
Ventricular Tachyarrhythmia ◽  
Very High Frequency ◽  
Total Amplitude ◽  
Rr Interval ◽  
Normal Sinus ◽  
Marginal Spectrum ◽  
Very High

The malignant ventricular tachyarrhythmia including ventricular tachycardia (VT) and ventricular fibrillation (VF) is the major cause of triggering sudden cardiac death (SCD) and it is seriously harmful to human. There is a great significance to predict the VT/VF. In this study, the RR interval series preceding the onset of VT/VF events are used as the study objects, and the 135 RR interval series are recorded by implantable cardioverter defibrillators (ICD) from 78 patients. Instantaneous heart rate (IHR) series are obtained after preprocessing the RR interval series. The Hilbert spectrum and the frequency marginal spectrum of IHR series are analyzed based on the traditional Hilbert-Huang transform (HHT) and the improved HHT. Some signal spectrum features are extracted from the marginal spectrum of IHR series: low frequency amplitude, high frequency amplitude, very high frequency amplitude, total amplitude and the low-to-high frequency amplitude ratio. The statistical analysis shows that the performance of improved HHT is better than that of traditional HHT. High frequency amplitude, very high frequency amplitude and total amplitude of IHR series preceding the onset of VT/VF are significantly higher than that of normal sinus rhythm (p < 0.003), and low-to-high frequency amplitude ratio is significantly lower than that of normal sinus rhythm (p < 0.0002).

scholarly journals Prediction of atrial fibrillation from normal ECG using artificial intelligence in patients with unexplained stroke

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
Y.S Baek ◽  
S.C Lee ◽  
W.I Choi ◽  
D.H Kim
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Atrial Fibrillation ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Validation Dataset ◽  
Internal Validation ◽  
Normal Sinus ◽  
Group 2

Abstract Background Stroke related to embolic and of undetermined source constitute 20 to 30% of ischemic strokes. Many of these strokes are related to atrial fibrillation (AF), which might be underdetected due to its paroxysmal and silent nature. Purpose The aim of our study was to predict AF during normal sinus rhythm in a standard 12-lead ECG to train an artificial intelligence to train deep neural network in patients with unexplained stroke (embolic stroke of undetermined source; ESUS). Methods We analyzed digital raw data of 12-lead ECGs using artificial intelligence (AI) recurrent neural network (RNN) to detect the electrocardiographic signature of atrial fibrillation present during normal sinus rhythm using standard 12-lead ECGs. We included 2,585 cases aged 18 years or older with multiple ECGs at our university hospital between 2005 and 2017 validated by crossover analysis of two electrophysiologists. We defined the first recorded AF ECG as the index ECG and the first day of the window of interest as 14 days before the date of the index ECG. We allocated ECGs to the training, internal validation, and testing datasets in a 7:1:2 ratio. We calculated recall, F1 score, and the area under the curve (AUC) of the receiver operatoring characteristic curve (ROC) for the internal validation dataset to select a probability threshold. We applied this developed AI program to 169 ESUS patients who has been diagnosed and had standard 12-lead ECGs in our hospital. Results We acquired 1,266 NSR ECSs from real normal subjects and 1,319 NSR ECGs form paroxysmal AF patients. RNN AI-enabled ECG identified atrial fibrillation with an AUC of 0.79, recall of 82%, specificity of 78%, F1 score of 75% and overall accuracy of 72.8% (Figure). ESUS patients were divided into three groups according to calculated probabilities of AF using AI guided RNN program: group 1 (35 patients with probability of 0–25% of paroxysmal AF), group 2 (86 patients with probability of 25–75% of paroxysmal AF) and group 3 (48 patients with probability of 75–100% of paroxysmal AF). In Kaplan-Meier estimates, Group 2 and 3 (more than 25% of PAF probabilities) tended to have higher AF incidence although it did not reach statistical significance (log-rank p 0.678) (Figure). Conclusion AI may discriminate subtle changes between real and paroxysmal NSR and can also be helpful in patients with ESUS to identify if AF is the underlying cause of the stroke. Further studies are needed in order to evaluate their possible use in future prognostic models. Funding Acknowledgement Type of funding source: None

scholarly journals Detection of Congestive Heart Failure Based on LSTM-Based Deep Network via Short-Term RR Intervals

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1502 ◽  
Author(s):  
Ludi Wang ◽  
Xiaoguang Zhou
Keyword(s):  
Heart Failure ◽  
Congestive Heart Failure ◽  
Short Term Memory ◽  
Normal Sinus Rhythm ◽  
Medical Center ◽  
Segment Length ◽  
Short Term ◽  
Healthcare Applications ◽  
Rr Interval ◽  
Normal Sinus

Congestive heart failure (CHF) refers to the inadequate blood filling function of the ventricular pump and it may cause an insufficient heart discharge volume that fails to meet the needs of body metabolism. Heart rate variability (HRV) based on the RR interval is a proven effective predictor of CHF. Short-term HRV has been used widely in many healthcare applications to monitor patients’ health, especially in combination with mobile phones and smart watches. Inspired by the inception module from GoogLeNet, we combined long short-term memory (LSTM) and an Inception module for CHF detection. Five open-source databases were used for training and testing, and three RR segment length types (N = 500, 1000 and 2000) were used for the comparison with other studies. With blindfold validation, the proposed method achieved 99.22%, 98.85% and 98.92% accuracy using the Beth Israel Deaconess Medical Center (BIDMC) CHF, normal sinus rhythm (NSR) and the Fantasia database (FD) databases and 82.51%, 86.68% and 87.55% accuracy using the NSR-RR and CHF-RR databases, with N = 500, 1000 and 2000 length RR interval segments, respectively. Our end-to-end system can help clinicians to detect CHF using short-term assessment of the heartbeat. It can be installed in healthcare applications to monitor the status of human heart.

scholarly journals THE REFRACTORY PERIOD OF THE NORMALLY-BEATING DOG'S AURICLE; WITH A NOTE ON THE OCCURRENCE OF AURICULAR FIBRILLATION FOLLOWING A SINGLE STIMULUS

1930 ◽  
Vol 51 (3) ◽  
pp. 357-367 ◽  
Author(s):  
E. Cowles Andrus ◽  
Edward P. Carter ◽  
Keyword(s):  
Sinus Rhythm ◽  
Refractory Period ◽  
Vagal Stimulation ◽  
Normal Sinus Rhythm ◽  
Accurate Determination ◽  
Single Stimulus ◽  
Action Current ◽  
Normal Sinus ◽  
Stimulation Of

1. A method is described for determining the refractory period of the dog's auricle during the normal sinus rhythm. The advantages of the method are: (a) The total stimulating effects of repeated induction shocks are avoided. (b) The action current is recorded from a point one millimeter or less from the point of stimulation. (c) Alterations in the spontaneous rate of the auricle do not interfere with the accurate determination of the refractory period. 2. The values obtained for the normal refractory period and the changes produced by atropine and by stimulation of the vagus agree closely with those of previous observers. 3. The automatic features of the method make possible the determination of the refractory period under adrenalin. This drug brings about a distinct shortening of the refractory period but less than that produced by stimulation of the vagus. 4. During vagal stimulation a single induction shock, introduced soon after the end of the refractory period, frequently produces auricular fibrillation. The cause of this irregularity is discussed and its relation to clinical auricular fibrillation is suggested.

scholarly journals Meeting the unmet needs of clinicians from AI systems showcased for cardiology with deep-learning–based ECG analysis

2021 ◽  
Vol 118 (24) ◽  
pp. e2020620118
Author(s):  
Yonatan Elul ◽  
Aviv A. Rosenberg ◽  
Assaf Schuster ◽  
Alex M. Bronstein ◽  
Yael Yaniv
Keyword(s):  
Large Scale ◽  
Unmet Needs ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Daily Practice ◽  
Great Promise ◽  
Specific Patient ◽  
Ecg Analysis ◽  
Normal Sinus ◽  
Out Of Sample

Despite their great promise, artificial intelligence (AI) systems have yet to become ubiquitous in the daily practice of medicine largely due to several crucial unmet needs of healthcare practitioners. These include lack of explanations in clinically meaningful terms, handling the presence of unknown medical conditions, and transparency regarding the system’s limitations, both in terms of statistical performance as well as recognizing situations for which the system’s predictions are irrelevant. We articulate these unmet clinical needs as machine-learning (ML) problems and systematically address them with cutting-edge ML techniques. We focus on electrocardiogram (ECG) analysis as an example domain in which AI has great potential and tackle two challenging tasks: the detection of a heterogeneous mix of known and unknown arrhythmias from ECG and the identification of underlying cardio-pathology from segments annotated as normal sinus rhythm recorded in patients with an intermittent arrhythmia. We validate our methods by simulating a screening for arrhythmias in a large-scale population while adhering to statistical significance requirements. Specifically, our system 1) visualizes the relative importance of each part of an ECG segment for the final model decision; 2) upholds specified statistical constraints on its out-of-sample performance and provides uncertainty estimation for its predictions; 3) handles inputs containing unknown rhythm types; and 4) handles data from unseen patients while also flagging cases in which the model’s outputs are not usable for a specific patient. This work represents a significant step toward overcoming the limitations currently impeding the integration of AI into clinical practice in cardiology and medicine in general.

scholarly journals Improving Accuracy of Heart Failure Detection Using Data Refinement

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 520
Author(s):  
Jinle Xiong ◽  
Xueyu Liang ◽  
Lina Zhao ◽  
Benny Lo ◽  
Jianqing Li ◽  
...  
Keyword(s):  
Heart Failure ◽  
Heart Rate ◽  
Normal Sinus Rhythm ◽  
Characteristic Curve ◽  
Failure Detection ◽  
Original Method ◽  
Individual Variability ◽  
Data Refinement ◽  
Series Length ◽  
Rr Interval

Due to the wide inter- and intra-individual variability, short-term heart rate variability (HRV) analysis (usually 5 min) might lead to inaccuracy in detecting heart failure. Therefore, RR interval segmentation, which can reflect the individual heart condition, has been a key research challenge for accurate detection of heart failure. Previous studies mainly focus on analyzing the entire 24-h ECG recordings from all individuals in the database which often led to poor detection rate. In this study, we propose a set of data refinement procedures, which can automatically extract heart failure segments and yield better detection of heart failure. The procedures roughly contain three steps: (1) select fast heart rate sequences, (2) apply dynamic time warping (DTW) measure to filter out dissimilar segments, and (3) pick out individuals with large numbers of segments preserved. A physical threshold-based Sample Entropy (SampEn) was applied to distinguish congestive heart failure (CHF) subjects from normal sinus rhythm (NSR) ones, and results using the traditional threshold were also discussed. Experiment on the PhysioNet/MIT RR Interval Databases showed that in SampEn analysis (embedding dimension m = 1, tolerance threshold r = 12 ms and time series length N = 300), the accuracy value after data refinement has increased to 90.46% from 75.07%. Meanwhile, for the proposed procedures, the area under receiver operating characteristic curve (AUC) value has reached 95.73%, which outperforms the original method (i.e., without applying the proposed data refinement procedures) with AUC of 76.83%. The results have shown that our proposed data refinement procedures can significantly improve the accuracy in heart failure detection.

scholarly journals Medication Use Evaluation of Dronedarone in Comparison to Amiodarone

2016 ◽  
Vol 7 (4) ◽  
Author(s):  
Adam Corey ◽  
Nita Johnston
Keyword(s):  
Conflicts Of Interest ◽  
Normal Sinus Rhythm ◽  
Statistical Significance ◽  
Medication Use ◽  
Clinical Decision ◽  
Female Population ◽  
Risk Of Death ◽  
Normal Sinus ◽  
Increased Risk ◽  
Significant Difference

Amiodarone is the most effective rhythm-control for atrial fibrillation, but produces serious potential side effects. Dronedarone was designed to eliminate amiodarone toxicities, but increased the risk of mortality in clinical trials. This medication use evaluation compares one year of dronedarone use with a matched cohort of amiodarone patients at a single hospital in Greensboro, NC. Forty-eight patients were included with an average age of 71.8 years and 37.5% female population. No significant difference was found for the primary composite outcome of death, myocardial infarction, stroke, and systemic embolism (OR = 2.4, p = 0.148). Likewise, no statistical significance was demonstrated between the two groups for QTc prolongation, hypothyroidism, liver dysfunction or maintenance of normal sinus rhythm. In conclusion, the clinical decision process demonstrated no increased risk of death or other adverse events in the use of dronedarone. Conflict of Interest We declare no conflicts of interest or financial interests that the authors or members of their immediate families have in any product or service discussed in the manuscript, including grants (pending or received), employment, gifts, stock holdings or options, honoraria, consultancies, expert testimony, patents and royalties   Type: Student Project

scholarly journals HRV analysis: undependability of approximate entropy at locating optimum complexity in malnourished children

2021 ◽  
pp. 1-6
Author(s):  
David M. Garner ◽  
Gláucia S. Barreto ◽  
Vitor E. Valenti ◽  
Franciele M. Vanderlei ◽  
Andrey A. Porto ◽  
...  
Keyword(s):  
Statistical Significance ◽  
Accurate Determination ◽  
Approximate Entropy ◽  
Effect Sizes ◽  
Potential Hazard ◽  
Autonomic Modulation ◽  
Embedding Dimension ◽  
Malnourished Children ◽  
Hrv Analysis

Abstract Introduction: Approximate Entropy is an extensively enforced metric to evaluate chaotic responses and irregularities of RR intervals sourced from an eletrocardiogram. However, to estimate their responses, it has one major problem – the accurate determination of tolerances and embedding dimensions. So, we aimed to overt this potential hazard by calculating numerous alternatives to detect their optimality in malnourished children. Materials and methods: We evaluated 70 subjects split equally: malnourished children and controls. To estimate autonomic modulation, the heart rate was measured lacking any physical, sensory or pharmacologic stimuli. In the time series attained, Approximate Entropy was computed for tolerance (0.1→0.5 in intervals of 0.1) and embedding dimension (1→5 in intervals of 1) and the statistical significances between the groups by their Cohen’s ds and Hedges’s gs were totalled. Results: The uppermost value of statistical significance accomplished for the effect sizes for any of the combinations was −0.2897 (Cohen’s ds) and −0.2865 (Hedges’s gs). This was achieved with embedding dimension = 5 and tolerance = 0.3. Conclusions: Approximate Entropy was able to identify a reduction in chaotic response via malnourished children. The best values of embedding dimension and tolerance of the Approximate Entropy to identify malnourished children were, respectively, embedding dimension = 5 and embedding tolerance = 0.3. Nevertheless, Approximate Entropy is still an unreliable mathematical marker to regulate this.

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