Montmany, Martí

Sainz, Víctor

Serra, Álvaro

English

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The heart as an adaptive oscillatorÁlvaro Serra, Mart́ı Montmany, and Vı́ctor Sainz(Dated: June 1, 2018)Heart Rate determination through ECG processing, study of its variability to detect activity onthe Autonomous Nervous System and evaluate stress.Acknowledgment: We would like to thank the ISI group for their hospitality and specially professor JaimeOscar Casas for his mentorship, enthusiasm and proficiency.I. INTRODUCTIONThe main objective of our project is to be capable toperform stress measures in humans using electrocardio-gram signal (ECG). To do that, we treated the heartas a Chronotaxic System oscillator. This model sustainsthat the heart can be treated as an oscillators which havetime-varying frequencies that can be perturbed by noisebut remain stable [1]. Our heart has been proved to adaptits Heart Rate (HR) depending on the necessity of thebody (it will increase for example when you start doingexercise). The parameter that quantifies those changeson the HR is the Heart Rate Variability (HRV) which isthe parameter we have used on our work to detect phys-ical and mental stress situations.II. FUNDAMENTALSThe HRV has been widely studied and used for count-less different proposes. For example there is a study thattries to monitor emotional state from physiological sig-nals acquired remotely using a simple camera detectingthe HRV [2]. Or other works in the medical field such asmonitoring mental health conditions such as bipolar dis-order and borderline personality disorder [3], detectingthe severity of obstructive sleep apnea (OSA) condition[4].The part of our body in charge of the regulation of theHR is the autonomic nervous system, divided anatomi-cally into the sympathetic and parasympathetic (or va-gal) systems. The vagal system regulates responses re-lated with the maintenance and conservation of the bodyfunction with actions such as the slowing of the heartrate. The sympathetic system is more related with stresssituations response. Therefore, the HR is modulated bythis two systems that act in the opposite way. It hasbeen proved that it is possible to detect the activity ofboth systems studying the HRV in the frequency domain,providing a non invasive tool for stress measurements.III. TESTSThe tests were carried out in the laboratories ofthe Instrumentation, sensors and interfaces researchgroup (isi.upc.edu) in Castelldefels campus. Wewere provided a machine, developed by the ISI Group,capable of performing 4 diferent types of acquisitionand processing of noninvasive cardiovascular signals thatwere limb-to-limb impedance plethysmogram (IPG),ballistocardiogram (BCG), electrocardiogram (ECG)and tonometry in carotid artery. We opted for usingonly the ECG signal because it was the best one in orderto measure the HR, which was the magnitude we wantedto study. The device used to measure ECG was anhorizontal bar with two electrodes that were supposedto be gripped with the hands.We performed different tests in order to alter the stresslevel of the subject, therefore affecting the HR and ob-taining different data. Subjects of these experimentsare tagged AS-subject1, MM-subject2 and VS-subject3.These tests involved rest, physical or mental stimulation:• Relax Test: The subject lies on a table quietly for3 minutes at rest.• Physical Effort Test: In this test the subjectwarms up on a stationary bicycle and when he isalready excited his ECG signal is measured dur-ing 2 minutes, in which intense physical activity isperformed on the bike.• Calmdown Test: Data is recorded for two min-utes after the subject stops doing physical activityon the bike. The purpose of these test is to measurehow the body comes back from stress to normality• Stroop Test: The participant sits on a chair andreads a list of words for colours on the screen ofa computer, but the words are printed in a colourdifferent to the word itself. Then, the participantshould name the colours that the words are printedin. So, when the word orange” is printed in green,the participant should say green and move on to thenext word. The test begins with 2 minutes of rest,then 6 of the colour challenge and 2 last minutes ofrest, for a total recording time of 10 minutes.• Math Test: The participant has to make a seriesof arithmetic calculations in front of the computerincluding sums and subtractions of 3 digit num-bers. 150 seconds of rest, then 120 seconds of arith-metic operations, 120 seconds of rest, 120 secondsof arithmetic and finally 90 seconds of rest. Totalrecording time is 10 minutes, as with the StroopTest.2In the Stroop and Math tests a computer was used.The codes in python of these tests were obtained fromRicard Cuervo’s TFG [5] and modified to meet our re-quirements.IV. DATA TREATMENTAs we have explained in the previous sections, we aimto deduce whether a person is in a stressful situation ornot solely by measuring and analyzing his Electrocar-diogram signals (ECG). However, we cannot do so bylooking directly into the signal itself for there are lotsof non-controllable artifacts that might arise much moredependent on the person being measured and not nec-essarily on whether he is stressed or not. On the otherhand, the changes of oscillating period in the heart areconsidered to be a more objective indicator of being instress than the raw ECG signal. These variations on thesignal period are registered in the tachogram of the ECGsignal. Therefore, given a raw ECG signal, the first stepis to obtain the latter by applying the Pan TompkinsAlgorithm. Then, we perform Time-Frequency Analysison the Tachogram signal obtained in order to check forstress indicators in the ECG signal.A. The Pan Tompkins AlgorithmThe Pan Tompkins Algorithm is the procedure we fol-low in order to extract the tachogram from the raw ECGsignal by checking the variation of time between consec-utive periodic impulses. The results obtained from suchalgorithm will serve as basis in order to detect whetherthe person whose signal was recorded was suffering fromstress or not. The Pan Tompkins Algorithm is composedby the following parts:• Filtering of the signal: First of all, we apply athird order Butterworth bandpass filter to the rawECG signal. Usually the bandwith goes from 0.5 Hzto 12 Hz. However, we may adapt the lower boundup to 5 Hz in order to deal with noisier acquireddata.• Derivation: A derivative operator is applied tothe resulting signal in order to increase the high fre-quency components of the QRS complex and lowerthe low frequency components of the PT waves.The operator applied is: y(n) = 18 (2x(n) + x(n −1) − x(n− 3) − 2x(n− 4)) [5]• Squaring: Squaring the signal makes it all positiveand increases relative difference between big andsmall values.• Smoothing: The signal is smoothed by perform-ing its mean along a sliding window. The purposeof this step is to deal and lower the multiplicity ofpeaks during a single QRS complex. The size of thesliding window so as to avoid confusion between theQRS complex and the T wave. However, when theT wave is too high to avoid confusion between bothpeaks, we might merge both signals so as to at leastget just one of the two, even though this results ina decrease of accuracy in time.• Thresholding: In order to start the search forlocal maxima, a threshold is set and those pointsfrom the signal not surpassing it are filtered out.In addition, those data points presenting a negativeslope are filtered out as well as they usually presenta greater delay with respect to the QRS complex.• Find local maxima: We find the local maximacorresponding to QRS peaks in both the filteredand raw signals. This allows us to double checkour local maxima candidates and filter out thosethat are susceptible to be false positives.• Inter-beat (RR) interval: We compute the RRinterval by checking the time distance from oneQRS peak to the next.• Check for false positives or negatives: Finally,we check for false positives or negatives throughoutthe QRS peaks candidates. It is assumed that ev-ery QRS peak represents a single heart beat and,therefore, our resulting sequence of QRS peaks issubject to biological limitations. For example, de-tected beats at a distance lower than 0.2 secondsare biologically impossible (HR of 300 beats perminute) and just one of these two should be se-lected as a correction measure as one of them is cer-tainly a false positive [6]. Another example wouldbe changes of more than 30-40 in the RR intervalswhich are biologically impossible too. This is likelyto be the consequence of not detecting a QRS peak(false negative), in which case we interpolate theposition in time of the missing one in order to cor-rect the misdetection.It is by applying the previous steps that we are able toobtain the tachogram signal. Ahead, we show an exam-ple of the ECG signal where the QRS peaks have beenidentified and its respective tachogram. In 1 the QRScomplex peaks of a small interval are shown, on 2 a 120second ECG signal is depicted with all its peaks and on3 the corresponding tachogram of that previous signal.This signal was obtained conducting the Calmdown Teston AS-subject1.B. Time-Frequency AnalysisBefore going on with this section, we must remarkthat all transformations into the frequency domain of theaforementioned signals have been performed using thecontinuous wavelet transform (CWT), not the Fourier3FIG. 1. A few peaks R peaks of QRS complex detected on aECG signal.FIG. 2. Complete ECG signal with QRS complex peaks.FIG. 3. Corresponding tachogram of the signal.transform. The problem with the Fourier transformarises from the fact that it uses a single analysis window.This results in a loss of resolution at lower than nominalfrequencies for the window size and makes impossible toobtain information about the Heart Rate Variation spec-trum (the signal measured in the tachogram) over timeat higher frequencies than the ones the window has beendesigned for. In order to tackle this problem, we use theCWT as it allows to obtain a similar transform but usinga size adapting window.As we have hinted previously, the interesting informa-tion provided by the tachogram signal is enclosed in thefrequency domain. In order to extract such information,we extract the power spectral density (PSD) of threemain frequency ranges:• Very low frequency range (VLF): All frequenciesbelow 0.04 Hz.• Low frequency range (LF): Frequencies rangingfrom 0.04 Hz to 0.15 Hz.• High frequency range (HF): Frequencies rangingfrom 0.15 Hz to 0.4 Hz.Apart from that, the total power (TP) is defined asthe power in the frequency band going from 0 Hz up to1 Hz. This latter measurement is mostly considered inorder to normalize the signal by the PSD contained inthe bandwidth: 0.04 Hz - 1 Hz.When interpreting the obtained normalized PSD sig-nals for LF and HF, we usually try to relate the ob-tained results to vagal and sympathetic activity of theAutonomous Nervous System (ANS). While the HF PSDis mostly related to vagal activity that does not mean LFis the same to the sympathetic. Actually, LF is relatedto the activity of both systems. This is why, in orderto extract the most information out of the computed re-sults, we will extract both the ratio LF/HF, providingsymptovagal balance information, and HF, related to va-gal activity information. Depending on the behaviour ofthe frequency bands, when a shift toward relative vagalenhancement is expected, we should encounter one of thefollowing possible scenarios [7]:• LF decreases and HF is unchanged: reflects achange in sympathetic activity with no decrease invagal activity.• LF is unchanged and HF increases: indicates a de-crease in sympathetic activity and an increase invagal activity.• Both LF and HF increase but their ratio does not:reflects an increase in vagal activity but no increasein sympathetic activity.• Both LF and HF and their ratio decrease: reducedvagal and sympathetic activities with a shift in bal-ance towards relative vagal enhancement.In exchange, a shift toward relative sympathetic en-hancement should manifest in one of the following ways:• HF decreases, LF does not: vagal activity decreasesand sympathetic activity increases.• LF increases and HF remains unchanged: reflectsan increase in sympathetic activity and no changesin vagal activity.4FIG. 4. Tachogram of MM-subject2.FIG. 5. Power of frequency bands of MM-subject2.V. RESULTSWe performed the tests aforementioned. Ahead, thetachogram, PSD distribution and both HF and LF nor-malized that result from the Math Test on patient MM-subject2 are shown on 4, 5 and 6.A low pass filter on 6 has been applied because thechanges on the trend of power of each frequency band(Low and High) are very slow but when plotted theyappear to oscillate very fast because of the numericalcalculation used to calculate them. On 6 it is clear howthe weights of the LF and HF change evolve betweena relaxing and a stressing phase and how they taketime to adapt when the situation switches from stressto relaxation and vice-versa. On 7 the power on thepreviously mentioned AS-subject1 Calmdown Test isdepicted. Whereas the average of HR reaches a uniformvalue around second 70 (see 3), power distribution hasnot change from what it was before, thus the sympa-thetic remains active and the person is still stressed.FIG. 6. Normalized power of High and Low bands on MM-subject2.FIG. 7. Normalized power of High and Low bands on AS-calmdown.VI. CONCLUSIONSThis work shows how the heart acts as an oscillatorwith a frequency HR whose variability can be used toextract information about the activity of the ANS andprediction of stress, outperforming other criteria such asHR average alone.Results of AS-subject1 and VS-subject3 on the MathTest are not included on this paper due to lack of space.They were similar to MM-subject2 but differed on theamplitude of the curves and their evolution on time,suggesting that each individual reacts differently for thesame stressor.5VII. REFERENCES[1] P. T. Clemson, Y. F. Suprunenko, T. Stankovski, andA. Stefanovska, The Heart as a Chronotaxic System - Whyits Rate Variability is both Complex and Simple: Theoryand Analysis Methods (2014).[2] Y. Benezeth, P. Li, R. Macwan, K. Nakamura, R. Gomez,and F. Yang, Remote heart rate variability for emotionalstate monitoring (2018).[3] O. C. F. Andreotti, K. E. A. Saunders, A. C. Bilderbeck,and . Guy M Goodwin Maarten De Vos1, Linking Changesin Heart Rate Variability to Mood Changes in Daily Life(2017).[4] M. J. Rahman, R. Mahajan, and B. I. Morshed, Sever-ity Classification of Obstructive Sleep Apnea Using OnlyHeart Rate Variability Mesures With Ensamble Classifier(2018).[5] R. C. Saliné, Heart rate variability acquisition from ambu-latory measurements of IPG (ETSETB, UPC, Barcelona,2017).[6] G. D. Clifford, ECG Statistics, Noise, Artifacts, and Miss-ing Data (2006).[7] E. Toledo, O. Gurevitz, H. Hod, M. Eldar, and S. Aksel-rod, Computers in Cardiology (2002) pp. 657–660.

The heart as an adaptive oscillator

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The heart as an adaptive oscillator

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