Empirical investigation of multi-tier ensembles for the detection of cardiac autonomic neuropathy using subsets of the Ewing Features

This article is devoted to an empirical investigation of per- formance of several new large multi-tier ensembles for the detection of cardiac autonomic neuropathy (CAN) in diabetes patients using subsets of the Ewing features. We used new data collected by the diabetes screening research initiative (DiScRi) project, which is more than ten times larger than the data set originally used by Ewing in the investigation of CAN. The results show that new multi-tier ensembles achieved better performance compared with the outcomes published in the literature previously. The best accuracy 97.74% of the detection of CAN has been achieved by the novel multi-tier combination of AdaBoost and Bagging, where AdaBoost is used at the top tier and Bagging is used at the middle tier, for the set consisting of the following four Ewing features: the deep breathing heart rate change, the Valsalva manoeuvre heart rate change, the hand grip blood pressure change and the lying to standing blood pressure change.

Improving classifications for cardiac autonomic neuropathy using multi-level ensemble classifiers and feature selection based on random forest

This paper is devoted to empirical investigation of novel multi-level ensemble meta classifiers for the detection and monitoring of progression of cardiac autonomic neuropathy, CAN, in diabetes patients. Our experiments relied on an extensive database and concentrated on ensembles of ensembles, or multi-level meta classifiers, for the classification of cardiac autonomic neuropathy progression. First, we carried out a thorough investigation comparing the performance of various base classifiers for several known sets of the most essential features in this database and determined that Random Forest significantly and consistently outperforms all other base classifiers in this new application. Second, we used feature selection and ranking implemented in Random Forest. It was able to identify a new set of features, which has turned out better than all other sets considered for this large and well-known database previously. Random Forest remained the very best classier for the new set of features too. Third, we investigated meta classifiers and new multi-level meta classifiers based on Random Forest, which have improved its performance. The results obtained show that novel multi-level meta classifiers achieved further improvement and obtained new outcomes that are significantly better compared with the outcomes published in the literature previously for cardiac autonomic neuropathy.

Concordance with phase-one cardiac rehabilitation guidelines in the inpatient setting

The aim of the paper was to examine concordance with phase-one cardiac rehabilitation (CR) guidelines, undertake an intervention that might optimise adherence to the guidelines, and establish a benchmark for practice in the coronary care unit (CCU) setting.

Altered cardiac autonomic nervous function in depression

Background:
Depression is an independent risk factor for coronary artery disease. Autonomic instability may play a mediating or moderating role in this relationship; however this is not well understood. The objective of this study was to explore cardiac autonomic function and cardiac arrhythmia in depression, the correlation between depression severity and Heart Rate Variability (HRV) related indices, and the prevalence of arrhythmia.

Methods:
Individuals (n = 53) with major depression as assessed by the Diagnostic and Statistical Manual of Mental Disorders, who had a Hamilton Rating Scale for Depression (HAMD) score ≥20 and a Zung Self-Rating Depression Scale score > 53 were compared to 53 healthy individuals, matched for age and gender. Multichannel Electrocardiograph ECG-92C data were collected over 24 hours. Long-term changes in HRV were used to assess the following vagally mediated changes in autonomic tone, expressed as time domain indices: Standard deviation of the NN intervals (SDNN), standard deviation of 5 min averaged NN intervals (SDANN), Root Mean Square of the Successive Differences (RMSSD) and percentage of NN intervals > 50 ms different from preceding interval (pNN50). Pearson’s correlations were conducted to explore the strength of the association between depression severity (using the SDS and HRV related indices, specifically SDNN and low frequency domain / high frequency domain (LF/HF)).

Results:
The values of SDNN, SDANN, RMSSD, PNN50 and HF were lower in the depression group compared to the control group (P<.05). The mean value of the LF in the depression group was higher than the in control group (P<.05). Furthermore the ratio of LF/HF was higher among the depression group than the control group (P<.05). A linear relationship was shown to exist between the severity of the depression and HRV indices. In the depression group, the prevalence of arrhythmia was significantly higher than in the control group (P<.05), particularly supraventricular arrhythmias.

Conclusions:
Our findings suggest that depression is accompanied by dysfunction of the cardiac autonomic nervous system, and further, that depression severity is linked to severity of this dysfunction. Individuals with depression appear to be susceptible to premature atrial and/or ventricular disease.