Modelo basado en support vector machine para la estimación de la variabilidad de la frecuencia cardíaca

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(1)Model based on Support Vector Machine for the estimation of the Heart Rate Variability Catalina Maria Hernández Ruiz1 [0000-0001-8264-363X], Sergio Andrés Villagrán Martínez1, Johan Enrique Ortiz Guzmán2 and Paulo Alonso Gaona Garcia. 1 1. Universidad Distrital Francisco José de Caldas, Facultad de Ingeniería, Bogotá, Colombia. cmhernandezr@correo.udistrital.edu.co 2 Universidad del Rosario, Facultad de Medicina, Bogotá, Colombia.. Abstract. This paper shows the design, implementation and analysis of a Machine Learning (ML) model for the estimation of Heart Rate Variability (HRV). Through the integration of devices and technologies of the Internet of Things, a support tool is proposed for people in health and sports areas who need to know an individual's HRV. The cardiac signals of the subjects were captured through pectoral bands, later they were classified by a Support Vector Machine algorithm that determined if the HRV is depressed or increased. The proposed solution has an efficiency of 90.3% and it’s the initial component for the development of an application oriented to physical training that suggests exercise routines based on the HRV of the individual. Keywords: Heart Rate Variability (HRV), Internet of Things (IOT), Support Vector Machine (SVM), Heart Rate Monitor (HRM).. 1. Introduction. The heart rate variability (HRV) is the difference per unit of time between heartbeats in any given interval [1]. It is a useful tool to evaluate the control of the autonomic nervous system over the heart rate (HR), as it is shown by the changes given in the balance between the sympathetic and parasympathetic systems. Obtaining the HRV does not require invasive processes as it is carried out through the analysis of the electrical signals of the heart, reflecting the regularity of heartbeats [2]. Through the Internet of Things (IOT), it is possible to monitor and control a great diversity of systems through the use of sensor sets which facilitate the capture of data for further analysis and processing [3]. In order to obtain HR specifically, there are HR monitors (HRM), commonly used in medicine and sports sciences by doctors, athletes, coaches and researchers, as a reliable and robust means of recording the activity of the heart [4]. Among these HRM, there are wristbands and wireless chest straps with electrodes connected with services and web / mobile applications so as to send the captured information. These applications also offer complementary information associated with.

(2) 2. the statistics and individual’s profile, which is something beneficial for physical training purposes [5]. HRV has gained relevance in recent decades due to its association with heart diagnosis. For this reason, several authors have developed tools for their analysis and use [1]. Among the most commonly used traditional methods for calculating HRV, the frequency and time domain measurements as well as the non-linear methods can be found [6]. Song et al. [7] claim that, for the analysis of HRV, these conventional practices have some limitations to make predictions and diagnosis. Due to this fact, new techniques and mechanisms based on the usual mathematical models have emerged. These, when combined with computational systems, are more accurate in their calculation, as Matta et al. [8] who applied neural networks to obtain HRV through the recognition and categorization of patterns. From this perspective, the work presented below is a model based on Support Vector Machine (SVM) for the classification of HRV using low cost equipment such as chest straps with HR sensors that allow monitoring and obtaining the activity of the heart. The aim of this is to generate a tool which could provide any person - an expert or not - with the value of HRV in a practical and simple way so that this can be applied afterwards in order to make decisions with regard to health areas. The following article is organized as follows: Section 2 provides a context for the topic as well as related work and background information. Section 3 presents the methodology used for the work conducted. Section 4 describes the proposed model. Section 5 expresses the results obtained. Subsequently, section 6 shows the analysis of results and discussions. Finally, Section 7 covers the conclusions and future works.. 2. Related Works. The most widely used resource for the capture of HRV is the electrocardiogram (ECG), which registers the origin and propagation of electric potential through the cardiac muscle [9], and is the means by which the most information about the activity of the heart is obtained [1]. The ECG consists of waves, segments and intervals. Such waves are expressed with deflection of the electrical activity, finding either positive deflections (when the deflection is upward) or negative (when it is downward) in relation to the baseline of the heart rate. On the other hand, the segments are understood as the space lying between two consecutive waves, whereas the intervals are the period resulting from the sum of a wave and a segment. Another determining factor given by the ECG is the QRS complex, which indicates the depolarization of the ventricular muscle. In this way, the time between each heartbeat is determined by the interval between the QRS complexes, more commonly known as R-R intervals [10]. HRV is a valuable tool to examine the sympathetic and parasympathetic functions of the autonomic nervous system and is inversely proportional to the regularity of the.

(3) 3. HR; that is to say, the higher the regularity there is, the lower HRV there is and vice versa. Additionally, it serves as a measure of the balance between sympathetic and parasympathetic mediators. The former ones reflect the effect of epinephrine and norepinephrine that sympathetic nerve fibers release on the sinoatrial and atrioventricular nodules, which leads to an increase in the rate of cardiac contraction. The latter ones influence on the release of acetylcholine by parasympathetic nerve fibers that decrease HR [11]. Sao et al. [12] state that the combination between the electrical signals of the heart and the HRV generate a good basis for the analysis of its state. According to Giles et al. [4], from several clinical studies undertaken, it was found that the decrease in HRV is related to the diagnosis of cardiovascular diseases, diabetic neuropathy and hypertension, among others. Such authors also claim that the HRV serves as a measure in the sports environment when facing diverse conditions such as overtraining, recovery, endurance training and exercise. Karim et al. [11] describe the calculation of heart rate variability using different methods. Time domain is among one the most known and simplest to apply, in which R-R intervals, which are necessary for the generation of statistical metrics as well as indexes for calculating HRV, are identified based on the ECG. SDNN corresponds to the standard deviation of all the R-R intervals. Besides, RMSSD and PNN50 can also be found, the former one being the square root of the mean squared difference in successive heartbeats, whereas the latter one is the number of successive intervals that differ by more than 50ms, expressed as a percentage of the total number of heartbeats. Some other classic measurements to determine HRV are those of the frequency domain. McCraty et al. [6] state that the heart rate oscillations are divided into 4 primary frequency bands: high frequency (HF), low frequency (LF), very low frequency (VLF) and ultra-low frequency (ULF). The first two will be vital for the present study since they are directly related to the HRV. The HF goes from 0.15 Hz to 0.4 Hz, which is equivalent to rhythms with periods between 2.5 and 7 seconds, whereas the LF lays between 0.04 Hz and 0.15 Hz, which means rhythms of 7 and 25 seconds respectively. The HF reflects the parasympathetic or vagal activity and is also called the respiratory band because it responds to the variations of the HR that occurs in the respiratory cycle. On the other hand, the LF shows the sympathetic activity of the system. The HR is regulated by the balance between the actions of the sympathetic and the parasympathetic nervous system, so it is vital to know the HF and LF bands to determine the HRV. Among the non-linear methods, there is the Poincaré plot, which is a non-linearvisual technique that allows examining the behavior of the R-R intervals, through the classification of the forms of the ECG plot. Analysis and recognition allow to identify degrees of heart failure. This differentiation can be done through the calculation of the standard deviations SD1 and SD2 that are related to HRV [12]. To classify HRV, multiple authors have resorted to fields and techniques derived from artificial intelligence, like fuzzy logic, neural networks, ML, among others. Such as Patel et al. [13], who designed a neural network for the detection of early fatigue in.

(4) 4. people who drive for long periods of time, not only warned about the lethargy which seriously affects the performance of drivers but also claimed that this could be a very common cause of accidents. Through the classification of time domain measurements and the frequency of HRV, they were able to quantify somnolence with an accuracy of 90%, for which they distinguished the levels of sympathetic (LF) and parasympathetic (HF) activity of the organism. This technique of fatigue detection, based on HRV, was recommended as a countermeasure for fatigue. Asl et al. [14] applied SVM for the identification of 6 different types of arrhythmias: normal sinus rhythm, premature ventricular contraction, atrial fibrillation, sick sinus syndrome, ventricular fibrillation and heart block. They did this by classifying 15 characteristics of the HRV calculated through linear and non-linear methods. The accuracy of this algorithm for each case was greater than 98%. On the other hand, Liu et al. [15] classified the combination of cardiac variability and complexity to determine those patients who required lifesaving interventions. Such authors captured information from 104 patients through the use of wireless vital signs monitoring systems from which they obtained their heart rate data. They applied classification techniques such as neural networks and multivariable logistic regression, which were evaluated and compared by statistical analysis. The conclusions indicated that in the neural network model, the multilayer perceptron (MLP) algorithm demonstrated more efficiency and effectiveness in the classification of patients who needed a rescue measure in contrast with the logistic regression algorithm. Considering the aforementioned reference points, the following study intends to determine the classification of HRV suggesting an algorithm based on SVM, as Song, et al. [7] did. The authors applied the same technique for the analysis and identification of patients who suffered acute myocardial infarction, based on the fact that the decrease in HRV was associated with a potential risk of ventricular arrhythmias for patients who had had such episodes. The aim of this work is to develop a tool which can support decision-making strategies for the areas of health and physical training. In view of the above, it is important to consider that classification is a problem which may be solved through ML, in which there could exist from one to two or more classifications in a sample data. The study included a process of design and implementation of the proposed algorithm, established a work methodology described in the following section.. 3. Work Methodology. The working method to carry out the following study was quasi-experimental and applied. Then, in Fig. 1 a series of phases that define it and that allowed to glimpse a navigation map for the study are shown..

(5) 5. Fig. 1. Work phases used for study.. The first phase involved the search and analysis of literature on conventional techniques for the calculation of HRV, from them, specific methods were identified and explored in Phase 2. In stage 3, the definition of the process was carried out of capture of cardiac signals through IoT devices and the generation of a strategy for the transfer of collected data. During phase 4, a method based on SVM was implemented to classify HRV, this was applied through a case study in phase 5. The results and their analysis were performed in Phase 6, where the efficiency of the algorithm was determined. 3.1. Case Study. The case study included the capture of cardiac signals from a group of individuals through chest straps that obtained the HR value. Table 1 presents the characteristics of used strap [16]. POLAR H10 HEART RATE SENSOR Battery type CR 2025 Battery sealing ring O-ring 20.0 x 0.90 Material Silicone Battery lifetime 400 h Operating temperature -10 °C to +50 °C / 14 °F to 122 °F Connector material ABS, ABS + GF, PC, Stainless steel Strap material 38% Polyamide, 29% Polyurethane, 20% Table 1. Characteristics of the Polar H10 chest strap.. These non-invasive records were made in 33 people whose HR was obtained for 12 minutes. In total, 56 data constituted the training set that served as the input for the learning of ML algorithm. The average age of the individuals ranged between 25 and 35 years, mostly healthy people with few exceptions, such as thyroid dysfunctions and hypertension. Close amounts of women and men, although no data was taken on children because their nervous system has not yet fully matured as in the case of adults. During each session, the person was required to remain at rest for approximately 12 minutes, which included sitting without speaking and minimizing movements. In addition of HR, other information was recorded such as age, weight, height, gender, preexisting diseases and the use of regular medications or treatments. By means of these.

(6) 6. cardiac registers the necessary information was obtained to feed the ML algorithm, its model will be described in the following section.. 4. Proposed Working Model. The model that was carried out has two main components that can be observed in Fig 2. The first is the IoT system that aims to define the capture and disposition of the information, this being the input for the following component: the HRV classification system, which takes the data and processes it by classifying the HRV as depressed or increased. IoT system used pectoral bands to record the HR, its transmission was made through a mobile application that communicated with the sensor via bluetooth.. Fig. 2. Proposed working model.. Pitale et al. [1] describe two steps for the implementation of classification algorithms: the definition of the model and the selection and application of a method to classify it. For our study, the first one included the processing of the information given by the IoT system to obtain the entries of the classification algorithm, which were diverse variables on the domains of time, frequency and non-linear methods. Among the first were the nnxx which is the number of successive R-R intervals that differ by more than xx milliseconds and pnnxx, which is its corresponding in percentage [17]. In the domain of frequency, the HF and LF were taken, due to their direct relationship with the activity of the sympathetic and parasympathetic systems of the organism [6]. Finally, variables from nonlinear methods such as SD1 and SD2 were analyzed, which are the standard deviations of the Poincaré plot perpendicular and along the identity line respectively [18]. In addition, alpha1 and alpha2 were obtained, short and long-term fluctuations of the detrended fluctuation analysis [19]. The expected results were a reduced or increased HRV as explained by Task Force et al. [20]. The classification technique chosen was SVM, due to its efficiency and reliability as described in the background section. Song et al. [7], state that SVMs are supervised learning models that are used in regression and classification problems because they are based on data analysis and pattern recognition, generating n-dimensional hyperplanes to distinguish and separate various sets of characteristics, thus finding the optimal hyperparameters. The algorithm was trained with the variables generated from the 56 records obtained with the chest strap, the results of its application are described in the following section..

(7) 7. 5. Results Obtained. Multiple combinations of inputs were applied for the algorithm training with the purpose of obtaining the best model for the HRV classification. Zhao et al., [21] describe a multiclass classification function in Matlab fitcecoc, which was used in the present study with a linear kernel and its parameters were optimized using automatic hyperparameter optimization. The corresponding evaluation was carried out through obtaining two types of errors: the classification error in the sample, and the error generated from cross validation. He et al. [22], state that the cross-validation technique divides the training data into several non-contiguous parts with similar length. Each one is selected as test data, while the rest are used as training. Then, the prediction model is applied with these data and this process is repeated with each of the divisions obtained. All predictions are averaged to give an estimate of the performance of the algorithm.. Fig. 3. Optimization of the proposed classification model.. As a first result, the most efficient inputs set was: HF, alpha1, alpha2 and nnxx. With an error of classification of the sample of 8.9% and a cross-validation error of 9.7%, the behavior of the algorithm with this configuration is presented in Fig 3. The evaluation carried out by the optimization function to compare the expected behavior with the real one, decreasing the cross-validation error, returning 90.3% of effectiveness.. 6. Analysis of results and discussions. During the algorithm tests, multiple cases with negative behaviors were evidenced, such as the use of frequency domain variables only: HF and LF, because it did not grant a satisfactory classification rate for the algorithm, it presented an error of 19.6%. Likewise, the inclusion of the 8 entries in the model generated an overfitting problem, same case was perceived when modifying the algorithm's Kernel to Gaussian, presenting a perfect fit to the training set with a sample classification error of 0%, but with cross validation, the error was greater than 30%. This situation was propitiated by the amount of data for the training set being very small in contrast to a high number of features or entries, very common difficulty that is presented in the classification algorithms with few data..

(8) 8. The most efficient set presents a mixture between the three methods that generate variables for the HRV calculation, time and frequency domains and non-linear methods, which outlines a complementary behavior of these variables in HRV obtaining.. 7. Conclusions. One of the main advantages presented in this study is the low cost in the acquisition of the cardiac registry. The use of chest straps is a non-invasive method that does not generate any secondary effects on the individual and does not present environmental requirements, it can be applicable in any person who is doing any activity. Its use is recommended in conjunction with applications that allow its consumption to be carried out, because they have shown high reliability in its evaluation. The integration and combination of variables of time and frequency domains and nonlinear methods is a viable and effective alternative for the classification of HRV. The proposed solution is suggested as a useful and practical tool for people who need to know the HRV, since it is a health indicator and is related to various deficiencies and diseases as expressed in the section of background. As future work and continuation of this study we propose the improvement of the propounded model, increasing its efficiency through the enrichment of the training set, providing greater experience to the algorithm for its learning. Also, we want to make use of this solution as a component of an application for physical training, supporting an athlete and personal trainers suggesting exercise routines according to their physical condition, by tracking their HRV, analyzing their progress and history, making use of GPS, to know changes of altitude and length of routes.. 8. References. 1. Pitale, R., Tajane, K., Umale, J.: Heart Rate Variability Classification and Feature Extraction Using Support Vector Machine and PCA: An Overview. Journal of Engineering Research and Applications, 381-384. (2014). 2. Borchini, R., Veronesi, G., Bonzini, M., Gianfagna, F., Dashi, O., Ferrario, M.: Heart Rate Variability Frequency Domain Alterations among Healthy Nurses Exposed to Prolonged Work Stress. International Journal of Environmental Research and Public Health. 15, 113 (2018). 3. Hernández, C., Villagrán, S., Gaona, P.: Predictive Model for Detecting MQ2 Gases Using Fuzzy Logic on IoT Devices. In: Jayne C., Iliadis L. (eds) Engineering Applications of Neural Networks. EANN 2016. Communications in Computer and Information Science, vol 629. Springer, Cham. (2016). 4. Giles, D., Draper, N., Neil, W.: Validity of the Polar V800 heart rate monitor to measure RR intervals at rest. European Journal of Applied Physiology, 563-571. (2015). 5. Erkkila, M., Rae, R., Thurlin, T., Korva, T., Manninen, T.: Managing physiological exercise data. US Patent 9855463B2, 16 Jan 2014..

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