# Congestive Heart Failure Category Classification Using Neural Networks in Short-Term Series
Related to the length of time series, the distinction between long-term and short-term in cardiovascular time-series analysis depends on the context and the specific characteristics of the data. In cardiovascular time-series analysis, short-term variations typically refer to changes that occur over relatively brief time intervals, such as seconds (two beats per second) to minutes (eighty beats per minute). Short-term variations might be associated with specific events, such as arrhythmias, while long-term variations could be related to changes occurring over hours to days or even longer, and they may be associated with gradual changes in cardiovascular health or responses to treatment [41; 42]. The goals of this research were to study how the short length of time series of heart rate variability affects the performance of different classification models of neural network. For this purpose, different alternatives were used as classification methods, such as Multilayer Perceptron neural network (MLP), Extreme Learning Machine (ELM), Random Vector Functional Link Network (RVFL) models, and the CNN approach). The alternatives used as classification methods provide different accuracy for different short lengths, while also delivering explanation about the performance of neural networks on short heart rate variability records. In light of the concepts discussed earlier, the key contributions of this paper can be summarized as follows:  
* The importance of the classification of cardiovascular time series is linked to information that helps decision making. Typically, studies concentrate on analyzing long-term series (24 h of records) [43; 44; 45], giving less attention to the investigation of short time series. However, in this research, we aim to broaden the study of the application of neural networks for classifying congestive heart failure using short records of heart rate variability (RR intervals);
* We compare three different approaches for congestive heart failure conditions: Multi-layer MLP network, RVFL network, and ELM;
* We show that, for different congestive heart failure conditions, the output models provide misclassifications when classical variants of neural networks are used. However, using coupling, some deep feature maps with the RVFL and MLP models allow us to obtain a very high simulation accuracy. The contributions of this article aim to give information that helps decision-making in relation to the classification of different congestive heart failure categories. A correct classification of the degree of heart disease for patients with congestive heart failure could help specialists with medical diagnosis and apply appropriate treatments to the disease condition. Furthermore, the use of short time series could be useful in the implementation of devices for real-time monitoring of the patient's cardiovascular condition. The paper is organized as follows. Section 2 presents a detailed description of the methods based on neural networks. The materials and methods used in this work are presented in Section 3. Section 4 provides a discussion of cardiovascular classification results and describes the limitations of this study. It concludes the research with the conclusions in Section 5.