Investigating Circadian Heart Rate Variability in Coronary Artery Disease Patients with Various Degrees of Left Ventricle Ejection Fraction

2020 ◽  
Author(s):  
Mohanad Alkhodari ◽  
Herbert F. Jelinek ◽  
Naoufel Werghi ◽  
Leontios J. Hadjileontiadis ◽  
Ahsan H. Khandoker
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Left Ventricle ◽  
Ejection Fraction ◽  
Left Ventricle Ejection Fraction ◽  
Artery Disease

scholarly journals The influence of cardiac rehabilitation on heart rate variability indices in men with type 2 diabetes and coronary artery disease

2021 ◽  
Vol 18 (3) ◽  
pp. 147916412110201
Author(s):  
Katarzyna Szmigielska ◽  
Anna Jegier
Keyword(s):  
Type 2 Diabetes ◽  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Cardiac Rehabilitation ◽  
Interval Training ◽  
Patients With Diabetes ◽  
Artery Disease

The study evaluated the influence of cardiac rehabilitation (CR) on heart rate variability (HRV) in men with coronary artery disease (CAD) with and without diabetes. Method: The study population included 141 male CAD patients prospectively and consecutively admitted to an outpatient comprehensive CR program. Twenty-seven patients with type-2 diabetes were compared with 114 males without diabetes. The participants performed a 45-min cycle ergometer interval training alternating 4-min workload and a 2-min active restitution three times a week for 8 weeks. The training intensity was adjusted so that the patient’s heart rate achieved the training heart rate calculated according to the Karvonen formula. At the baseline and after 8 weeks, all the patients underwent the HRV assessment. Results: HRV indices in the patients with diabetes were significantly lower as compared to the patients without diabetes in SDNN, TP, LF parameters, both at the baseline and after 8 weeks of CR. After 8 weeks of CR, a significant improvement of TP, SDNN, pNN50% and HF occurred in the patients without diabetes, whereas in the patients with diabetes only HF component improved significantly. Conclusions: As regards HRV indices, CR seems to be less effective in patients with CAD and type-2 diabetes.

scholarly journals 1072-215 Depressive symptoms are associated with reduced heart rate variability in individuals without coronary artery disease

2004 ◽  
Vol 43 (5) ◽  
pp. A120-A121
Author(s):  
Viola Vaccarino ◽  
Rachel Lampert ◽  
Forrester Lee ◽  
J.Douglas Bremner ◽  
Jerome L Abramson ◽  
...  
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Depressive Symptoms ◽  
Coronary Artery ◽  
Artery Disease

scholarly journals Melatonin for nondippers with coronary artery disease: assessment of blood pressure profile and heart rate variability

2009 ◽  
Vol 33 (1) ◽  
pp. 56-61 ◽  
Author(s):  
Tomasz Rechciński ◽  
Ewa Trzos ◽  
Karina Wierzbowska-Drabik ◽  
Maria Krzemińska-Pakuła ◽  
Małgorzata Kurpesa
Keyword(s):  
Blood Pressure ◽  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Pressure Profile ◽  
Disease Assessment ◽  
Blood Pressure Profile ◽  
Artery Disease

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

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