Prediction of PAF Attacks using Time-Domain Measures of Heart Rate Variability

2018 ◽  
pp. 536-542
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Time Domain ◽  
Paroxysmal Atrial Fibrillation ◽  
Classification Accuracy ◽  
Nearest Neighbor ◽  
Normal Subjects ◽  
Time Interval ◽  
K Nearest Neighbor

Paroxysmal atrial fibrillation (PAF) is the mainly encountered type of arrhythmia and there is no validated method to predict a PAF attack before it occurs. In this study, predicting the PAF event was aimed using time-domain heart rate variability (HRV) measures in k- nearest neighbor (k-nn) classifier. Traditional time-domain HRV measures were analyzed in every 5-minute segments from 49 normal subjects, 25 patients with PAF attack and 25 patients with no attack within 45 minutes. All features were investigated whether they showed statistically significance. Significant features were classified by k-nn for odd numbers of neighbors between 1 and 19. This setup was run with two different configurations as study 1 to discriminate patients with PAF attack from normals and patients with no attack, and study 2 to discriminate patients with PAF attack from patients with no attack. SDNN, RMSSD and pNN50 measures were found to show statistically significant differences with p less than 0.05 in segments of 0-5 min, 2.5-7.5 min and 5-10 min intervals only. The maximum classification accuracy was obtained in the time interval of 2.5-7.5 minutes with %79 for Study 1 and just before attack with %80 for Study 2 in the time interval of 0-5 minutes. Results showed that the prediction of PAF events was possible when the classification between normal subjects from PAF patients was accurate. PAF attack can be determined 2.5 minutes earlier by simple classifier algorithms.

scholarly journals P095 Heart Rate Variability during Sleep in Paroxysmal Atrial Fibrillation patients with and without Obstructive Sleep Apnoea

2021 ◽  
Vol 2 (Supplement_1) ◽  
pp. A52-A52
Author(s):  
A Mohammadieh ◽  
H Dissanayake ◽  
K Sutherland ◽  
S Ucak ◽  
P de Chazal ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Obstructive Sleep Apnoea ◽  
Paroxysmal Atrial Fibrillation ◽  
Sleep Apnoea ◽  
Relative Reduction ◽  
Obstructive Sleep ◽  
Sympathetic Modulation ◽  
Ectopic Beats

Abstract Introduction Physiological studies have demonstrated the importance of the autonomic nervous system in mediating acute apnoea-induced atrial fibrillation (AF). We aimed to compare Heart Rate Variability (HRV) markers of autonomic function in paroxysmal atrial fibrillation (PAF) patients with and without obstructive sleep apnoea (OSA). A secondary aim was the analysis of ectopic beats in these groups. Methods Nocturnal ECG traces from 89 PAF patients who underwent in-laboratory polysomnography were included. After identifying ectopic beats in the ECGs, periods of arrhythmia as well as sleep apnoea events were excluded. HRV time and frequency domains were reported by sleep stage (REM vs Non-REM) for patients with and without OSA. Results Frequency domain analysis of HRV during non-REM sleep in PAF patients with OSA showed increased cardiac parasympathetic modulation (HF-nu: 39.13 ± 15.74 vs 47.98 ± 14.60, p = 0.008*) and reduced cardiac sympathetic modulation (LF/HF ratio: 2.05 ± 2.02 vs 1.17 ± 0.98, p = 0.007*). Results remained significant after adjusting for age, sex and BMI (adjusted p values 0.024 and 0.018 respectively). PAF patients with severe OSA (AHI ≥ 30/hr) had more AF beats and Ventricular Ectopic Beats than those without severe OSA (22.7 ± 42.8% vs 3.7 ± 17.9%, p = 0.006*, 1.7 ± 3.8 vs 0.3 ± 0.9%, p = 0.004* respectively). Conclusions This is the first study of HRV in AF patients with and without OSA. It suggests a chronic increase in parasympathetic nervous modulation and relative reduction in sympathetic modulation in PAF patients with OSA.

scholarly journals Mental Stress Detection via Heart Rate Variability using Machine Learning

2020 ◽  
Vol 12 (3) ◽  
pp. 431-444
Author(s):  
Bimo Sunarfri Hantono ◽  
◽  
Lukito Edi Nugroho ◽  
Paulus Insap Santosa ◽  
◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Rate ◽  
Working Memory ◽  
Heart Rate Variability ◽  
Mental Stress ◽  
Nearest Neighbor ◽  
Frequency Domain Analysis ◽  
K Nearest Neighbor ◽  
Stress Levels ◽  
Back Task

Mental stress is an undesirable condition for everyone. Increased stress can cause many problems, such as depression, heart attacks, and strokes. Psychophysiological conditions possible use as a reference to a person’s mental state of stress. The development of mobile device technology, along with the accompanying sensors, can be used to measure the psychophysiological condition of its users. Heart rate allows measured from the photoplethysmography signal utilizing a smartphone or smartwatch. The heart rate variability is currently one of the most studied methods for assessing mental stress. Our objective is to analyze stress levels on the subjects when performing tasks on the smartphone. This study involved 41 students as respondents. Their heart rate was recorded using a smartphone while they were doing the n-back tasks. The n-back task is one of the performance tasks used to measure working memory and working memory capacity. In this study, the n-back task was also used as a stressor. The heart rate dataset and n-back task results are then processed and analyzed using machine learning to determine stress levels. Compared with three other algorithms (neural network, discriminant analysis, and naïve Bayes), the k-nearest neighbor algorithm is most appropriate to use in the classification of time and frequency domain analysis.

scholarly journals Cryoballoon Versus High-Power, Short-Duration Radiofrequency Ablation for Pulmonary Vein Isolation in Patients With Paroxysmal Atrial Fibrillation

2021 ◽  
Vol 14 (9) ◽  
Author(s):  
Hui-Nam Pak ◽  
Je-Wook Park ◽  
Song-Yi Yang ◽  
Tae-Hoon Kim ◽  
Jae-Sun Uhm ◽  
...  
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulmonary Vein ◽  
Pulmonary Vein Isolation ◽  
High Power ◽  
Paroxysmal Atrial Fibrillation ◽  
Antiarrhythmic Drugs ◽  
Procedure Time

Background: The efficacy of cryoballoon pulmonary vein isolation (Cryo-PVI) is equivalent to that of radiofrequency pulmonary vein isolation in patients with paroxysmal atrial fibrillation (AF). We aimed to compare the efficacy and safety profile of Cryo-PVI and high-power, short-duration (HPSD) radiofrequency catheter ablation (RFCA) in patients with AF. Methods: We prospectively randomized 314 patients with paroxysmal AF (men, 71.3%; 59.9±10.9 years old) to either the Cryo-PVI group (n=156) or HPSD-RFCA group (n=158). Cavotricuspid isthmus ablation and linear ablation from the superior vena cava to the right atrial septum in addition to pulmonary vein isolation were carried out in the majority of patients in the HPSD-RFCA group. The primary end point was AF recurrence after a single procedure; secondary end points were the recurrence pattern, cardioversion rate, follow-up heart rate variability, and response to antiarrhythmic drugs. Results: After a mean follow-up of 9.8±5.1 months, the clinical recurrence rate did not significantly differ between the two groups (log-rank P =0.840). The rate of recurrence as atrial tachycardia ( P >0.999), cardioversion ( P =0.999), and 3-month heart rate variability (high frequency; P =0.506) did not significantly differ. During the final follow-up, sinus rhythm was maintained without antiarrhythmic drugs in 70.5% of the Cryo-PVI group and 73.4% of the HPSD-RFCA group ( P =0.567). No significant difference was found in the major complication rate between the two groups (3.8% versus 0.6%; P =0.066), but total procedure time was significantly shorter in the Cryo-PVI group (78.5±20.2 versus 124.5±37.1 minutes; P <0.001). Conclusions: In patients with paroxysmal AF, the Cryo-PVI is an effective rhythm-control strategy with a shorter procedure time compared with the HPSD-RFCA. Registration: URL: https://www.clinicaltrials.gov ; Unique identifier: NCT03920917.

The Novel Indices of Short-Time Heart Rate Variability for Prediction of Cardiovascular and Cerebrovascular Events

2020 ◽  
Vol 10 (3) ◽  
pp. 769-774
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Chunhe Song ◽  
Yun Su ◽  
Xingchi Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Singular Value ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Cerebrovascular Events ◽  
The Hilbert Transform ◽  
Short Time

In this paper, eight novel instantaneous indices of short-time heart rate variability (HRV) signals are proposed for prediction of cardiovascular and cerebrovascular events. The indices are based on Bubble Entropy (BE) and Singular Value Decompose (SVD). The process of indices calculation is as follows, firstly, the instantaneous amplitude (IA), instantaneous frequency (IF) and instantaneous phase (IP) of HRV signals are estimated by the Hilbert transform. Secondly, according to the HRV, IA, IP and IF, the BE and singular value (SV) is calculated, then eight novel indices are obtained, they are BEHRV, BEIA, BEIF, BEIP, SVHRV, SVIA, SVIF and SVIP. Last but not least, in order to evaluate the performance of the eight novel indices for prediction of cardiovascular and cerebrovascular events, the difference analysis of eight indices is carried out by t-test. According to the p value, seven of the eight indices BEHRV, BEIA, BEIF, BEIP, SVIA, SVIF and SVIP are thought to be the indices to discriminate the E group and N group. The K-nearest neighbor (KNN), support vector machine (SVM) and decision tree (DT) are applied on the seven novel indices. The results are that, seven novel indices are significantly different between the events and non-events groups, and the SVM classifier has the highest classification Acc and Spe for prediction of cardiovascular and cerebrovascular events, they are 88.31% and 90.19%, respectively.

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