# Diagnosing Various Severity Levels of Congestive Heart Failure Based on Long-Term HRV Signal
## 1 Introduction  
Heart failure is the disability of the heart in pumping blood to the body efficiently [1]. Many previous works have confirmed that autonomic imbalance is the leading cause of congestive heart failure (CHF) disease. The autonomic nervous system (ANS) consists mainly of two branches, the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS) [2, 3]. For the subjects in the normal group, the activities of two nervous systems are in a state of dynamic equilibrium. However, studies have shown that CHF breaks this equilibrium state due to excessive sympathetic activity [4]. The New York Heart Association (NYHA) has classified CHF into four scales based on the severity of the disease, NYHA I, II, III, and IV, respectively. Some papers have classified patients with mild heart failure (NYHA I or II) as low-risk patients and patients with severe heart failure (NYHA III or IV) as high-risk patients [5]. For patients with mild heart failure, the excessive activation of SNS andthe continuous reduction of PNS activity would lead to an extreme imbalance of autonomic nerves if there is no suitable treatment, which would inevitably lead to further deterioration of the disease [5]. Heart rate variability (HRV), which represents the variation of inter-beat-interval, provides very useful information on the dynamics of heartbeat behavior [6; 7]. Previous researchers have typically extracted linear and nonlinear HRV features from the inter-beat-interval (IBI) signals. The linear features are the signal parameters in time-domain, frequency-domain, and time-frequency domain [7; 8; 9]. In order to reveal other valuable information contained in the IBI signal, some researchers have used the complexity analysis method to extract nonlinear indicators [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20]. In 2003, [PERSON] et al. [21] applied Bayesian classifiers and nine long-term measurements for CHF discrimination with an accuracy of 93.24%. In 2007, [PERSON] et al. [22] utilized eight features of wavelet entropy and classical short-term HRV measurements and k-nearest-neighbor classifiers for CHF diagnosis and achieved an accuracy of 96.39%. In 2012, [PERSON] et al. [23] applied support vector machine (SVM) classifier and genetic algorithm (GA) for CHF recognition and achieved an accuracy of 98.79%. In their work, 16 features by using bi-spectral heart rate (HR) analysis of short-term measurements were applied for CHF recognition [23]. In 2016, [PERSON] et al. [24] applied empirical mode decomposition (EMD) for automated identification of congestive heart failure. Using 22 short-term HRV measurements with support vector machines, they achieved an accuracy of 97.64% [24]. In 2017, [PERSON] et al. [25] applied Ensemble classifiers and 10 short-term HRV measurements for CHF discrimination with an accuracy of 98.10%. In 2013, [PERSON] et al. [5] tried to assess the severity levels of CHF disease by using long-term HRV measurements. The classification and regression tree (CART) classifier were used to separate lower-risk patients from higher-risk patients with an accuracy of 85.4%. In 2015, [PERSON] et al. [26] proposed a generalized discriminant analysis method, applied k-nearest-neighbor classifier for CHF risk assessment based on long-term HRV, and obtained an accuracy of 100%. In 2016, [PERSON] et al. [27] built a four-level risk assessment model for CHF detection and quantification based on the DT-SVM (decision tree based support vector machine) algorithm. The model obtained a total accuracy of 96.61% in risk assessment among individuals of N (no risk), P1 (mild risk), P2 (moderate risk), and P3 (severe risk) [27]. In that paper, they used 180 features, including 126 dynamic measurements and 54 static measurements [27]. In 2019, [PERSON] et al. [28] proposed a four-stage classification problem using an end to end deep model, which extracted 20 features by convolution at max-pooling layers, and an accuracy of 97.6% was achieved.