Editorial Was Einthoven a 21st Century Visionary? Stanley H. Rosenbaum, Lee A. Fleisher, MDT
MD*,
Department of Anesthesiology, Yale University School of Medicine, New Haven, CT, and Department of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD.
*Professor of Anesthesiology, Medicine, and
Surgery, Yale University School of Medicine tAssistant Professor of Anesthesiology and Critical Care Medicine, The Johns Hopkins University School of Medicine Address reprint requests to Dr. Rosenbaum at the Department of Anesthesiology, Yale University School of Medicine, 333 Cedar St., Box 3333, New Haven, CT06510, USA, Received for publication April 15, 1992; revised manuscript accepted for publication April 29, 1992. 0 1992 Butterworth-Heinemann J. Clin. Anesth.
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1992.
The electrocardiograph (ECG) signal is a complex time varying repetitive scalar quantity that is commonly measured, recorded, and interpreted in cardiovascular medicine. Since the original description by Wilhelm Einthoven in 1903,r this signal traditionally has been regarded as containing significant information about the heart, including details about cardiac rate, ischemia and other injury, cardiac chamber size, electrical conduction patterns, among others. These types of information all have been derived from the interpretation of short-term portions of the signal, i.e., one or two beats, or at most a short rhythm strip. However, the ECG yields a complex signal that has longterm regularity with additional information that can be derived from longer time period analyses. In the method of signal examination termed Fourier analysis, we consider the ECG signal to be composed of a summation of different repetitive signals, each at its own frequency.’ This is analogous to regarding music as a sumRegarding mation of different notes, each a pure tone, played simultaneously. the ECG signal in such a manner, we can see that the traditional categories of information have mostly been contained in components of the signal in the range of 1 Hertz (Hz) or faster. There is also information in the frequency range of the order of 0.5-0.05 Hz. The importance of these long-term oscillations to normal physiology was first appreciated with fetal heart rate (HR) monitoring.” However, that application was purely observational, and the individual components of the HR signal were not computed. With the advances in digital signal processing over the last two decades, accurate determination of the intervals between successive heart beats (R-R interval) has become possible, after which various algorithms can be used to assess the frequency and amplitude of the various oscillatory components that make up the signal. Two of these algorithms, the fast Fourier transformation and an autoregressive analysis, have been used by investigators to elucidate two distinct frequency peaks. the high frequency (centered around 0.25 Hz) corresponding to the parasympathetic system and responsible for the respiratory sinus arrhythmia, and the low frequency (centered around 0.1 Hz) corresponding to both parasympathetic and sympathetic influences, as determined in animal studies.’ These long-term ECG components have been studied in several human pathophysiologic states, and a change in the balance between the high and low frequencies has been observed.2.4 In the article by Latson et al.3 in this issue of the Journal oj‘Clinica1 Anesthesia, we see an analysis of these phenomena used in the comparison of several common anesthetic drugs. General anesthesia represents a unique physiologic state during which respiration, and therefore its influence on HR, is controlled. The authors are able to use the full power of the technique by describing the baseline state and observing the change caused by a defined intervention (administration of an anesthetic drug) in an individual patient. Even when anesthetics decreased the total amplitude of HR changes, the ratio J. Clin. Anesth.,
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of the high- to low-frequent components provided useful information abotri sympathetic-parasympathetic balance and correlated \j.ilh kno\+~n chatlges observed with thrse anesthetics.fs It is reasonable to suppose that the long-term time period inlormariott 111 the ECG signal also can be extended 10 the study of’othrr anestheric drug\, as well as other nonpharrrtacologic interactions in car-d& homeos&s. in which the effects are less well known. For example. spinal anesrhesia has bectr shown to result in a decrpase in all frquencies and amplitude 01’the powct. spectra.’ Furthermore, there are ocher time varying repetitive signals of‘ interest to us as applied physiologists. ‘I-he information available in an anal+ of‘the signals produced by measuring blood pressure. blood tlo\~. respiration, and electroencephalograms, tnay, he porentially relevant. Put-thermore. iriteract,ions between acute physiologtcal stress (as CaLlsed hv anes~hrsia and surget-y) and these signals may con\.ev e\‘ett more infi)rmation and tt1u.s need IO be explored. Who would have thought in the last certcut-y that listening c~losel\ to the sounds over a thotl vessel in the arm. Fvhile :I nhber bladttet~ is intla& over the upper arm, would prmide one of the more Jundarnetttal c-ate,qot-ies of data about human cardiovascular fimctioning? In addition to the analysis of multiple beal tnscmblec tr-on1 ihe k:( .C; signal by Fourier analysis, there are other even mot-r mathematically sopltisrirated methods available. The analysis of repetitive signals has been a topic of muc-It theoretical research in the engineering and inli)rmatiott science c~omtrtunitirs in the past few decades, and multiple analytic- methods are available.~” I;~rt,thermore, even highly repetitive physiohgic sigds havr ;I cwkiiti ~111101111~ 01 This \.ari;ttioti, /.c’.. lhc norirepelilivc part of thr sigrial. raIidoIntless ol- noise. ran also be separated out and quan~ifietl 1)) trchniques SIN h as I Ire reccntl\ described method of approximate entropv. I” Scvrral sttIflic\ Ii;45 e shvtr that the randomness in the HK signal (orrdalcs with phvsiologic Itrrtc~iott ;IIKI perhaps even clinical long-ret-m outconi~s. ‘I lIo\~e\er1 it is important to tc’member that some of the drugs we 1156’ (an pt-oducr changes itt tiighcr or&r decreasing htlarl riilc vat-iabilil\9 I tial arc vet7 similar IO t how signals (f,.:, observed m very sick patients, suggesting tltc ttretl lot- mwl’ftl cloctrntertt;ttiott of the specificitv and sensitivity of‘ these methods. With increas&gly sophisticated signal analysis applied 10 phvsiologic signals, we expect further research in this area IO continue to pr&idc rete\~anl information to both the clinician and the physiologist. References 1. Einthoven M’: A new galvanometer. Artn P/zvc II’ 1903:X11: 1059 2. Malliani A, Pagani M, Lombardi F, Cerutti S:.(:ardiovasculalneural regulation expk~etl in the frequency domain. Czrczrlutw~ 1991;84:482-91. 3. Hon EH. Lee ST: Electronir evaluation of the fetal heart rate pattern\ precedmg leral death, further observations. Am J Ob.&t Gyecd 1965:87:8 14-26. 4. Uigger JT, Albrecht P, Steinman RC, ct al: Comparison of time- and ttequenc) domainbased measures of cardiac parasympathetic activity in Holrer recordings after m\oc;o-dial infarction. Awl.1 Car&o/ 1989;64:536-38. 5. Latson TW, McCarroll SM, Mirh_l MA, Hyndman VA. Whitten (ICZ’. Liptorl,lM: E_ftccts of three anesthetic induction techmques on heart rare variability.,/ Clzrl An&h 1992:4:2tZ276. 6. Ebert TJ, Kanitz DD, Kampine JP: Inhibition of sympathetic nrulal outflo\\ during thiopental anesthesia in humans. An&h Am& 1990;7 1:3 19-26. 7. Pruett JK, Yodlowski EH, Introna RPS, Buggay DS, Crumrinr KS: I he influence 01 spinal anesthetics on heart rate variations. Pharmncnl (Life Sci Adv) 199I ; IO:5I--.‘,. 8. Eckmann JP, Ruelle D: Ergodic theory of chaos and strange attractors. Rev Mod Ply 1985;57:617-56. 9. Goldberger AL, West BJ: Fractals in physiology and medicine. Y&J Bio/dZlrd 1987:60:4:! I--35. 10. Pincus SM: Approximate Il.
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entropy as a measure 01‘tlstem compiexitr f’rt~ .Yuti ,,lrmd Sci USA 1991;88:2297-301. Fleisher LA, Pincus SM, Rosenbaum SH: Approximate entropy aa ‘I corwlate of postoperative ventricular dysfunction [Abstract]. CrztCnw Ma’ 1992:20:A22.