# Method and System for Heart Rate Estimation Using Linear Prediction Filtering +
###### Abstract  
Cardiovascular diseases represent one of the major problems faced by modern society. In addition to reducing people's quality of life, bringing high costs to the health system, and causing losses in economic productivity, they are the leading cause of death in the world. Early diagnosis and treatment are the best actions to minimize the damage and costs caused by these diseases. For this, developing techniques and technologies that have higher accuracy in the analysis of electrocardiogram (ECG) signals is necessary. Early diagnosis benefits from relevant ECG interpretation. Then, it can contribute to reducing healthcare costs by replacing interventionist responses with preventive actions. This work presents a method and system for heart rate estimation using Linear Prediction Coefficients (LPCs) centered on an ESP32 microprocessor module and an AD8232 ECG signal conditioning module. The proposal was validated with a Tektronix AFG1022 function generator that produces ECG signals and obtained measurements with accuracy above 98.87%, showing performance similar to studies presented in the literature. Also, the LPC algorithm showed good performance in rejecting low-frequency noise caused by some common artifacts, such as body movement and electrode displacement. heart rate; linear prediction; ESP32 2025618 February 202518 March 202518 March 202518 March 202518 February 202518 February 202518 March 202518 March 202518 March 202518 March 202518 March 202518 February 202518 February 202518 March 2025  
ment and readmissions, as well as a decrease in the mortality rate and improved patient comfort and satisfaction [5]. In [6], the effectiveness of telemedicine interventions compared with conventional healthcare for patients with cardiac insufficiency was examined, and the study found that medically supported telemedicine systems were more effective for adults, particularly in reducing all-cause hospitalization, cardiac hospitalization, all-cause mortality, cardiac mortality, and length of stay. In [7], a case study of the use of telemedicine with the auxiliary tool of artificial intelligence in the city of Taruma--SP is presented, in which there was a 21% decrease in premature deaths due to cardiovascular diseases and a 25% decrease in premature deaths due to circulatory diseases. To detect CDs and to give accurate diagnoses, the electrocardiogram--ECG--is one of the main types of test/exam, and innovation trends and available technologies make this exam very applicable to telemedicine procedures. It consists of recording the variation in the bio-electrical activity of the heart, which represents the cyclic contractions and relaxations of the human heart muscle and provides important information about the functional aspects of the heart and the cardiovascular system [8]. It is a procedure that has the benefits of having well-defined parameters, low cost, and high sensitivity in detecting CD [9]. The use of artificial intelligence (AI) in analyzing ECG signals to classify arrhythmia and CD has been a trend. Automation is a good route to reduce errors and human burden while interpreting ECG signals; also, automated classification/measurements of ECG signals can improve the accuracy and efficiency of diagnoses [1]. Some examples of recent studies that apply AI tools for detecting cardiovascular diseases focused on deep learning techniques, mainly well-known unidimensional convolutional neural networks--1D-CNNs. A new classification method was presented in [10] for three classes of cardiomyopathyopathy, in which a data elimination and remodeling procedure substantially reduced the processing and learning time. Also, the final precision of the classification was improved, reaching values higher than 94.7% of test precision. In [11], the author proposed a solution that includes data filtering and pulsated dynamic segmentation in a ten-layer 1D-CNN, reaching a precision of 97.8% in a 14-class scenario of arrhythmias from the MIT-BIH database.