FEATURE EXTRACTION AND ABNORMALITY DETECTION IN AUTONOMIC REGULATION OF CARDIOVASCULAR SYSTEM

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1 Proceedings of the ASME 2011 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference IDETC/CIE 2011 August 28-31, 2011, Washington, DC, USA DETC FEATURE EXTRACTION AND ABNORMALITY DETECTION IN AUTONOMIC REGULATION OF CARDIOVASCULAR SYSTEM Ali Jalali * C. Nataraj Margaret Butchy Ali Ghaffari K.N. Toosi University of Tech. Tehran, Iran ABSTRACT The objective of this study is to develop an efficient methodology for classifying patients suffering any type of blood pressure dysregulation from healthy subjects. Four features of malfunctions in blood pressure regulation are introduced, and a criterion is proposed for each feature to evaluate and distinguish patients from healthy subjects. The evaluated features are based on the analysis of difference between data related to healthy subjects and those collected from patients. The proposed criteria are implemented on a group of healthy and patient subjects by collecting their systolic blood pressure (SBP) and their heart rate (HR) time series. The proposed method is applied on three different groups of subjects each containing four healthy and eleven patients. It is shown that the algorithm properly detects the status of all fifteen subjects in one group and fourteen subjects in two groups. The results obtained indicate that the selected features have remarkable capability in detection of blood pressure dysregulation. INTRODUCTION Early Precise variations in the short-term, beat-to-beat cardiovascular hemodynamic parameters may be considered to reflect the dynamic interplay between the continuous perturbations in the blood circulation and the correctional response of neurally mediated regulatory mechanisms. Autonomic nervous system (ANS) provides adjustment of blood pressure and heart rate at each beat, allowing humans great flexibility in posture and environment. Since many clinicians are unfamiliar with disorders in the autonomic nervous system, noninvasive and automatic assessment of disorders in regulatory mechanism of cardiovascular system would be extremely valuable in clinical use. Disorders in the function of neural regulatory mechanism can result from disease and environmental conditions. Certain kinds of diseases, hypertension [1-2] and congestive heart failure [3-4], to name a few, affect short term regulation. Other common diseases affecting short-term regulation may be diabetes mellitus and Parkinson s disease [3]. Depending on their effects on blood pressure in the upright posture, these autonomic disorders also referred to as dysautonomias, can be divided into two groups. The first group includes those that are severe, always causing significant orthostatic hypotension (a fall in blood pressure of more than 20/10 mmhg, measured with the patient lying quietly after 5 minutes of quiet standing.). Although these disorders are rare or uncommon, they can all be serious. The second group, the mild dysautonomias, are more common but less serious, and orthostatic hypotension is usually absent, though heart rate abnormalities are often prominent [5]. Few research efforts have focused on this area. Javorka et al. [6] compared heart rate and blood pressure variability among young patients with type I diabetes mellitus (DM) and 1 Copyright 2011 by ASME

2 control subjects by Poincare plot. They found significant reduction of all HRV Poincare plot measure in patients with type I diabetes mellitus, indicating heart rate dysregulation. The study done by Pagani et al. [7] was about patients suffering from hypertension. They showed that baroreflex gain decreases with the presence of hypertension. Patients with diabetic autonomic neuropathy (DAN) were the subjects of the study done by Mukkamala et al. [8] In their study, they indicated that baroreflex amplitude progressively decreased with increasing severity of diabetic autonomic neuropathy. Patients with sleep apnea (SA) before and after continuous positive airway pressure (CPAP) therapy were studied by Belozeroff et al. [9] they inferred that baroreflex gain would increase with CPAP therapy. Mainardi et al. [10] studied the baroreflex regulation under the syncope; they showed inhabitation of the baroreflex preceding the syncope event. The previous researchers studied blood pressure dysregulation; however, they were limited to study effects of one specific disease on baroreflex. The aim of this study is to remedy this limitation. In this paper we present a new approach to detect subjects who suffer from disorders in blood pressure regulation from healthy subjects. This study is a general case of abnormality detection among all the subjects in any given data base of healthy and patient ones. METHOD The data used in this paper is collected from Physionet [11]. Data are collected from two databases: MIT-BIH Polysmonographic and MIMIC II databases within Physionet archive. Data of Polysmonographic database is for healthy subjects and data of MIMIC II database is for abnormal patients. Twenty five subjects from this data base have been collected for training. For each normal subject and abnormal patient, ECG signal and blood pressure waveforms have been collected in a 5 hour range. For the first part of the study, the HR and SBP series for each subject are derived. The method for separation between the two categories of normal against abnormal subjects is based on the hypothesis that there should be differences between the SBP and HR data in normal subjects and abnormal patients [12]. The reason for this hypothesis is clear; since HR and SBP are output and input for an HR baroreflex, which is a part of the autonomic nervous system; thus any disorder in this dynamical system will directly affect the output. It should be noticed that since HR and SBP are coupled through the mechanical cardiovascular system, changes in HR will affect the SBP. We can therefore conclude that changes in the HR and SBP series reflect disorders in the regulatory mechanism of the cardiovascular system. In the following sections we describe four criteria which were evaluated for distinguishing healthy subjects from patients. CIRCLE CRITERION To evaluate differences between healthy and abnormal subjects, the SBP against HR diagram for each subject is plotted. These plots show a significant difference between normal subjects and abnormal patients such that the data for normal subjects are concentrated, while those of abnormal patients are scattered. The mean value of SBP and HR for each normal subject and abnormal patient is then calculated and plotted in one diagram. Figure 1 shows the mean values for all subjects in one diagram. The main difference between the two groups is quite clear. This diagram reveals the fact that there are differences between the HR and SBP data in normal subjects and abnormal ones. The plot shows that the data for normal subjects clustered and limited in a specific area, while those of the abnormal patients are spread out through the whole area. The first criterion is named the "circle criterion". The center of the circle is located at point "O" where its coordinates are the mean values of HR and SBP of normal patients, and the radius of the circle is calculated based on Euclidian distance between center and boundary of circle. Figure 1. THE PLOT SHOWING THE MEAN VALUES FOR ALL SUBJECTS ESTIMATION ERROR CRITERION In this part, system identification method is used for the identification of HR baroreflex. A nonlinear autoregressive with exogenous input (NARX) model for HR baroreflex is employed to estimate HR series. NARX models in general are represented by the following equation: y(t) = F y(t 1), y(t 2),, y(t n a ), u(t n k ),, u(t n k n b + 1) (1) 2 Copyright 2011 by ASME

3 where y(t) and u(t) are the output and input of the system, respectively. In Eqn. (1) terms n a, n b, n k are order of the model. Model order for data in this research is calculated as 9, 6 and 3 respectively. In this criterion, Artificial Neuro Fuzzy Inference System (ANFIS) structure is employed for the identification purpose. The model has 15 inputs and one output. Membership functions for inputs are designed based on physiological facts. It is well known that part of the function of nervous system is due to the sympathetic and parasympathetic nerves. Therefore, for each input, two generalized bell-shaped membership functions are assigned to designate sympathetic and parasympathetic functions. Therefore, the third criterion is defined as "Poincare plot deviation". Based on this criterion, subjects would be called abnormal if deviation from line y=x is more than 15%. AUTONOMIC RESPONSE DELAY Delay in autonomic response is caused from the parasympathetic nerves function. It means that there is a delay in autonomic response to stimulus. Calculating the delay for healthy subjects and patients we inferred that response delays in abnormal subjects are remarkably higher than in healthy subjects. The results of calculating the delay in autonomic response are illustrated in Fig. 3. Fifteen abnormal patients and ten healthy subjects were involved in the training group. System identification results are described in Tab. (1). The results in this table show that differences exist in the normalized root mean square error (NRMSE) with respect to the estimation of the HR for the two groups under study. In particular, the results indicate that NRMSE is smaller for normal subjects than for abnormal patients. These results also confirm the assumption made earlier in this paper about the difference existing in the data corresponding to the normal subjects and abnormal patients. These differences are due to the fact that the model is designed for normal subjects, and hence the output of the model for abnormal patients have more errors than for normal subjects. Based on these results and noticing that the maximum error for healthy subject is 0.238, while the minimum error for patient is 0.263, we define a second criterion called "estimation error criterion". According to this criterion, subject would be flagged as abnormal, if the calculated error in HR estimation rise is more than Table 1: ERROR ESTIMATION FOR IDENTIFICATION OF HR BAROREFLEX Group Mean Max Min Normal Abnormal POINCARE PLOT DEVIATION A Poincare plot, named after Henri Poincare, is used to quantify self-similarity in processes, usually periodic functions. Poincare plot is plot of each HR sample is plotted versus previous HR, and then the line y = x is fitted to the data. In previous work [6] this method has been also applied to classify patients with type I DM from healthy subjects. Drawing Poincare plot for healthy and abnormal subjects, it is illustrated that in healthy subjects, the deviation from the mentioned line is less than that for abnormal subjects. These plots are illustrated in Fig. 2. Figure 2. POINCARE PLOTS OF HR FOR TWO HEALTHY (UP) AND TWO ABNORMAL (DOWN) CASES. LINE Y=X IS ILLUSTRATED IN ALL PICTURES. Based on the above results, the fourth criterion called "autonomic response delay criterion" is introduced. Based on this criterion, the subject is called abnormal if the calculated delay in autonomic response rises to more than second. After deriving the four criteria mentioned above, an algorithm is designed to classify healthy subjects from patients. In the following section we described the proposed algorithm. 3 Copyright 2011 by ASME

4 PROPOSED ALGORITHM Based on the evaluated criteria, an algorithm is developed to automatically classify patients with disorder in their regulatory mechanism of cardiovascular system from healthy subjects. The algorithm is based on the fuzzy decision method. First, for each criterion, three Gaussian bell membership functions are designed to indicate three major groups: healthy, risky and patient. Since this algorithm is designed for clinical use, we divided subjects under study to three subgroups; healthy, risky and abnormal. Figure 4 represents two of the designed membership functions for each criterion. In the next step, for each subjects all features are extracted and used as an input for the four abnormality criteria. Then, for each criterion, the subject's degree of membership to all groups is evaluated. Figure 4. THE DESIGNED MEMBERSHIP FUNCTIONS FOR TWO CRITERION. After evaluating degree of memberships, cumulative sum of all three groups is calculated for each subject. A given subject will belong to the group whose cumulative sum is the largest. The schematic of the proposed algorithm is presented in Fig. 5. Figure 3. RESULTS OF CALCULATING DELAY IN AUTONOMIC RESPONSE. CALCULATED DELAYS IN PATIENTS ARE REMARKABLY LARGER THAN IN HEALTHY SUBJECTS. DELAY IN AUTONOMIC RESPONSE HAS ITS ORIGIN IN THE PARASYMPATHETIC NERVES FUNCTION. Figure 5. SCHEMATIC DIAGRAM OF THE PROPOSED ALGORITHM. RESULTS The algorithm is first trained with twenty five subjects including ten healthy and fifteen patients. Then, three groups of subjects are tested, each group with four healthy individuals and eleven patients. All subjects are properly detected in the first group. All patients in the second and third group are correctly detected; just one healthy subject in the second and third group is classified as risky. 4 Copyright 2011 by ASME

5 DISCUSSION AND CONCLUSIONS In this paper a method based on fuzzy logic is proposed to distinguish healthy subjects from patients with dysregulation in their blood pressure. The method is based on the differences between hemodynamic data of healthy subjects and patients. Four different criteria are presented to detect patients. For each criterion a fuzzy classifier is designed such that the individuals are classified into the healthy, risky and abnormal fuzzy subjects. In other words, a given person may have membership grade in all three classes. The algorithm is equipped with a combined criterion to make a final judgment of each individual based on the individual results of the four criteria. It is shown that the algorithm is highly reliable in the sense that, it has been able to detect correctly all members of the first group. It is been also able to detect all eleven patients in each of the next two groups correctly. Only one of the healthy members in the second and third groups was classified as risky. In this study, four different criteria were explained and used in the proposed algorithm in order to detect the abnormalities in testing subjects. From each testing subject, different features have been extracted and used as input for the criteria and based on a combined result of all four criterioa, a decision was made about the type of subject: normal, risky and abnormal. The results of the proposed algorithm in detecting disorder in autonomic nervous system show that the algorithm is reliable. The difference between the proposed method in this study and other similar research in this field of area is that, by using the presented algorithm in this study, existence of any abnormality in a patient will be found, while in most of similar studies in this area, a specific abnormality is found in a patient or among a database of subjects. Therefore, our results are more general and more useful for clinical applications. Although this algorithm is only applicable for detecting disorders in regulatory mechanism of cardiovascular system and the type of disorder is not specified, it is not restricted to one type of disorders as published in other studies. [5] Javorka M., J. Javorka, I. Tonhajzerova and K. Javorka, Visualization of heart rate and blood pressure dysregulation in young patients with type 1 diabetes mellitus by Pioncare plot, In Computers in Cardiology. Vol. 33, pp [6] Pagani M., V. Somers, R. Furlan, S. Dell Orto, J. Conway, G. Baselli, S. Cerutti, P. Sleight and A. Malliani, Changes in autonomic regulation induced by physical training in mild hypertension. Hypertension, 12, pp [7] Mukkamala R., J. M. Mathias, T. J. Mullen, R. J. Cohen and R. Freeman, System identification of closedloop cardiovascular control mechanisms: diabetic autonomic neuropathy. American Journal of Physiology: regulatory, integrative and comparative physiology, 276(3),Mar, pp [8] Belozeroff, V., R.B. Berry, C.S. Sasson, M.C. Khoo, Effects of CPAP therapy on cardiovascular variability in obstructive sleep apnea: a closed-loop analysis. American Journal of Physiology Heart and Circulation Physiology, 282(1),Jan, pp [9] Mainardi L. T., A. M. Bianchi, R. Furlan, S. Piazza, R. Barbieri, V. Di Virgilio, A. Malliani and S. Cerutti, Multivariate time-variant identification of cardiovascular variability signals: a beat-to-beat spectral parameter estimation in vasovagal syncope. IEEE Transactions on Biomedical Engineering, 44(10),Oct, pp [10] Goldberger A. L., L. A. Amaral, L. Glass, J. M. Housdorff, P. C. Ivanov, R. G. Mark, J.E. Mietus, G. B. Moody, C. K. Peng and H. E. Stanley, PhysioBank, PhysioToolkit and PhysioNet, components of new research resource for complex physiologic signals. Circulation. 101(23),Jan, pp [11] Jalali A., A. Ghaffari, M. Ghasemi, H. SadAbadi, P. Ghorbanian and H. Golbayani, Disorder classification in the regulatory mechanism of the cardiovascular system. In Computers in Cardiology. Vol. 34, pp REFERENCES [1] Akselrod, S., O. Oz, M. Grinberg, and L. Keselbrenner, Dynamic autonomic response to change of posture investigated by time-dependent heart rate variability among normal and mild hypertensive adults. Journal of Autonomic. Nervous. System, 64(1), May, pp [2] Zippes, D. P., M. N. Levy, L. A. Cobb, S. Julius, P. G. Kaufman, N. E. Miller, and R. L. Verrier, Task force-2-sudden cardiac death-neural-cardiac interaction. Circulation. 76 pp [3] Airaksinen, K. E. J., Autonomic mechanisms and sudden death after abrupt coronary occlusion, Ann. Med. 31(4), Aug, pp [4] Fuste, V., A. R. Wayne, R. A. O'Rourke, R. Roberts, S. B. King, E. N. Prystowsky, and I. Nash, Hurst's The Heart, McGrow-Hill, NY. 5 Copyright 2011 by ASME