Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal

Abstract

Previous studies have attempted to find autonomic differences of the cardiac system between the congestive heart failure (CHF) disease and healthy groups using a variety of algorithms of pattern recognition. By comparing previous literature, we have found that there are two shortcomings: 1) Previous studies have focused on improving the accuracy of models, but the number of features used has mostly exceeded 10, leading to poor generalization performance; 2) Previous works rarely distinguish the severity levels of CHF disease. In order to make up for these two shortcomings, we proposed two models: model A was used for distinguishing CHF patients from the normal people; model B was used for diagnosing the four severity levels of CHF disease. Based on long-term heart rate variability (HRV) (40000 intervals–8h) signals, we extracted linear and non-linear features from the inter-beat-interval (IBI) series. After that, the sequence forward selection algorithm (SFS) reduced the feature dimension. Finally, models with the best performance were selected through the leave-one-subject-out validation. For a total of 113 samples of the dataset, we applied the support vector machine classifier and five HRV features for CHF discrimination and obtained an accuracy of 97.35%. For a total of 41 samples of the dataset, we applied k-nearest-neighbor (K = 1) classifier and four HRV features for diagnosing four severity levels of CHF disease and got an accuracy of 87.80%. The contribution in this work was to use the fewer features to optimize our models by the leave-one-subject-out validation. The relatively good generalization performance of our models indicated their value in clinical application.

Multiscale Entropy Analysis with Low-Dimensional Exhaustive Search for Detecting Heart Failure

“…Many methods have been used for feature selection in preprocessing multidimensional HRV metrics, such as sequential forward feature selection [48,57], sequential backward feature selection [42], and genetic algorithms [39]. Up until now, the use of an exhaustive search has been ignored because it is extremely time-consuming in higher dimensions.…”

Pattern Recognition of Cognitive Load Using EEG and ECG Signals

“…In order to quantify the ANS activity, we extracted 27 linear and nonlinear parameters from the RR interval series as features of cognitive load conditions. These parameters were commonly applied to HRV analysis in literature [ 30 , 31 , 32 , 33 ]. Besides, six commonly used EEG parameters which measured the central nervous system (CNS) activity, e.g., powers of sub-band brainwaves [ 34 ], were also applied as features of cognitive load conditions.…”

“…The original 27 HRV parameters and 6 EEG parameters were commonly applied to the analysis of ANS and CNS activities in literature [ 30 , 31 , 32 , 33 , 34 ]. However, they are not specific to the pattern recognition of cognitive load conditions.…”

A Prototype Photoplethysmography Electronic Device that Distinguishes Congestive Heart Failure from Healthy Individuals by Applying Natural Time Analysis

“…Parameters Used Sensitivity s CHF Hua et al [9] (see Table 8) Various HRV classifiers 85.37% to 97.56%…”

“…Several studies have attempted to find differences of the autonomic nervous cardiac system that controls the electrical cardiac function between patients with HF and healthy (H) subjects using a variety of algorithms of pattern recognition primary based on long term data [9]. Computer aided detection methods for automatic HF diagnosis using ECG signals have been reported in literature [10].…”