Heart Rate Variability Techniques, Applications, and Future Directions Albert C. -C. Yang, M. D. Department of Psychiatry Taipei Veterans General Hospital ccyang@physionet. org
Explore Three Themes p Overview of physiology and techniques p Applications p Future directions
中醫對於脈象變異的觀察 十難 論一脈十變 p 十 剛 心 甚 自 心 肺 ； 膀 脈 難曰：一脈為十變者，何謂也？然：五邪 柔 相 逢 之意 也 。 假 令 心 脈 急 甚 者 ， 肝 邪 干 也 ； 心 脈 微 急 者 ， 膽邪 干 小 腸 也 ； 心 脈 大 者 ， 心 邪 自 干 心 也 ； 心 脈 微 大 者， 小 腸 邪 干小腸也；心脈緩甚者，脾邪干心也； 脈微 緩 者 ， 胃 邪 干 小 腸 也 ； 心 脈 濇 甚 者 ， 邪 干 心 也 ； 心脈 微 濇 者 ， 大 腸 邪 干 小 腸 也 心 脈 沉 甚 者 ， 腎 邪 平 心 也； 心 脈 微 沉 者 ， 胱 邪 干 小 腸 也 。 五 藏 各 有 剛 柔 邪 ， 故令 一 輒變為十也。 難經 戰國晚期 99 B. C.
Quantitative Measurements of Arterial Blood Pressure 1733 AD - United Kingdom Stephen Hales noted the correlation between the respiratory cycle, blood pressure level, and interbeat intervals, the phenomenon known as “normal sinus arrhythmia”
Electrocardiogram 1912 Invented by Willem Einthoven 1924 Nobel Prize in Medicine
What is Heart Rate Variability? Picture from www. heartratemonitor. co. uk
Autonomic Nervous System Parasympathetic modulation Sympathetic modulation
Cardiac Transplantation Sands, KEF et al. , Circulation 79: 76 -82, 1989
Physiological Origin of Heart Rate Variability p Respiratory sinus arrhythmia (seconds) p Slower rhythms (minutes) p Day-night periodicity (hours)
Respiratory Sinus Arrhythmia Increased heart rate during inspiration Reduced heart rate during expiration
Slower Rhythms Data from normal control database at Dept. Psychiatry, Taipei Veterans General Hospital
Day-Night Periodicity p Heart rate, mean blood pressure, and renal sympathetic nerve activity recorded 3 weeks after implantation of catheters and electrodes in a conscious rabbit. p Note the strong day-night periodicity in heart rate and mean blood pressure that are accompanied by similar oscillations in sympathetic nerve activity. Malpas SC, Am J Physiol Regul Integr Comp Physiol 286: R 1 -R 12, 2004
Why is Heart Rate Variability Useful? p Changes in heart rate over time provide a window onto autonomic physiology. p Analysis of heart rate variability is a non-invasive method for identifying abnormalities in cardiac autonomic modulation. p Heart rate variability in combination with other risk stratifiers improves risk stratification. Goldberger AL, et al. Fractal dynamics in physiology: alterations with disease and aging. Proc Natl Acad Sci U S A 2002; 99 Suppl 1: 2466 -2472.
Quantifying Heart Rate Variability Approach 1: How much variability is there? Time Domain and Geometric Analyses Approach 2: What are the underlying rhythms? Frequency Domain Analysis Approach 3: How much complexity or selfsimilarity is there? Non-linear Analyses
Time Domain and Geometric Analyses
Which Subject is Healthier?
Variability: Within the World of Statistics 0. 90 ± 0. 06 A Healthy Subject 0. 91 ± 0. 01 B Heart Failure
Definition of Time Domain HRV Measures SDNN SDANN RMSSD p. NN 50 Standard deviation of normal interbeat intervals standard deviation of the averages of normal interbeat intervals in all five minutes segments of the entire recording Square root of the mean of the squared differences between successive interbeat intervals Percentage of adjacent intervals that varied by greater than 50 ms Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043 -1065. 14
Geometric Analysis - Poincaré Plot SD 1: dispersion (standard deviation) of points perpendicular to the axis of line-of-identity Line-of-identity SD 1 SD 2: dispersion (standard deviation) of points along the axis of line-of-identity Huikuri, H. V. et al. Circulation 1996; 93: 1836 -1844
Poincaré HRV Plot: Healthy vs. Disease Healthy Control Critically ill Patient mean heart rate : 52 bpm mean heart rate : 51 bpm Data Source: Taipei Veterans General Hospital, Taiwan
HRV in Different Stages of Cancer Early-detected cancer patient Chemotherapy cancer patient Hospice cancer patient Data Source: Taipei Veterans General Hospital, Taiwan
Frequency Domain Analyses
What are the Underlying Rhythms? One rhythm Fourier Transform 5 seconds/cycle or 12 times/minute Frequency = (1/5) cycle/second =0. 2 Hz
What are the Underlying Rhythms?
What are the Underlying Rhythms? Three Different Rhythms High Frequency = 0. 25 Hz (15 cycles/min) Low Frequency = 0. 1 Hz (6 cycles/min) Very Low Frequency = 0. 016 Hz (1 cycle/min)
Frequency Domain Analysis Akselrod S et al. Science. 1981; 213: 220 -2
Frequency Domain Analysis Task Force of the European Society of Cardiology. Circulation 1996; 93: 1043 -1065.
Definition of Frequency Domain Measures VLF (ms 2) HF (ms 2) LF/HF Total spectral power of all normal interbeat intervals between 0. 003 and 0. 04 Hz Total spectral power of all normal interbeat intervals between 0. 04 and 0. 15 Hz Total spectral power of all normal interbeat intervals between 0. 15 and 0. 4 Hz Ratio of low to high frequency power Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043 -1065.
Frequency Domain Analysis Malliani A et al. , Circulation, 84: 482 -492, 1991
Autonomic Function Assessment HRV Long-term HRV Parasympatheticrelated measures Time domain SDANN measures RMSSD p. NN 50 Frequency domain measures HF Sympatheticrelated measures VLF LF/HF Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology: Heart rate variability: standards of measurement, physiological interpretation and clinical use. Circulation 1996; 93: 1043 -1065.
Complexity Analyses
Which Subject is Healthier?
Variability vs. Complexity: Beyond ANOVA 0. 96 ± 0. 02 A Heart Failure 0. 97 ± 0. 02 B Healthy Subject
Three Key Concepts: “Beyond ANOVA” p Physiologic signals are the most complex in nature p Important basic/clinical information is “hidden” (encoded) in these fluctuations p Complexity degrades with pathology/aging The complex dynamics is an essential feature of the healthy status
Hallmarks of Healthy Complexity Healthy Heart Rate Dynamics p Nonstationarity n p Nonlinearity n p Statistics change with time Components interact in unexpected ways ( “cross-talk” ) Multiscale Organization n Fluctuations/structures may have fractal properties
Are there Fractal Processes in Biology? Fractal: A tree-like object or process, composed of sub-units (and sub-subunits, etc) that resemble the larger scale structure Self-similarity (scale invariance) may be a property of dynamics as well as structure Goldberger AL, et al. Proc Natl Acad Sci U S A 2002; 99 Suppl 1: 2466 -2472.
Aging and Illness: Loss of Fractal Complexity Healthy Dynamics: Multiscale Fractal Variability Two Patterns of Pathologic Breakdown Single Scale Periodicity Uncorrelated Randomness Healthy dynamics poised between too much order and total randomness. But randomness is not chaos! Nature 1999; 399: 461 Phys Rev Lett 2002; 89 : 068102 PNAS 2002 (suppl 1)99: 2466
Why is it Healthy to be Fractal? p Healthy function requires capability to cope with unpredictable environments p Scale-free (fractal) systems generate broad range of responses adaptability p Absence of characteristic time scale helps prevent getting locked into a rigid (single) pattern of response
Toolbox of Nonlinear Dynamics Scaling/fractal analyses Detrended fluctuation analysis (DFA) Regularity measures Approximate entropy/Sample entropy (Ap. En/Samp. En) Complexity measures Multiscale entropy (MSE) Correlation dimension PD 2 measure Information-based method Similarity and nonrandomness indexes
Phylogenetic Tree of Human Heartbeat Yang, C. -C. , et al. Phys. Rev. Lett. 90, 108103 (2003).
NYHA Class II-III Class IV NYHA Class III NYHA Yang, C. -C. , et al. Phys. Rev. Lett. 90, 108103 (2003).
Which Parameters to Use? • Time/frequency domain measures are frequently used in assessment of autonomic function • Nonlinear/complexity measures are more likely used in risk stratification and outcome prediction
How Long the ECG Data is Needed? • Short-term HRV (5 -30 minutes) is suitable for experimental maneuver and observation for change of autonomic function • Long-term HRV (30 minutes to 24 hours) is suitable for autonomic function assessment and risk stratification
Limitations of Heart Rate Variability p Results can be varied due to different algorithms or ECG record-length and therefore are often non-comparable among literatures p Many parameters, but no one is characteristic p There is no definite “normal range”
Applications p Association of cognitive decline with reduced HRV p Association of APOE gene polymorphism with reduced physiologic complexity p Association of BDNF gene polymorphism with altered sympathovagal balance p Autonomic dysfunction in headache patients p Sleep state quantification using cardiopulmonary coupling analysis
微星科技 可攜式心律變異監測儀 My. ECG E 3 -80 94年台北榮總醫療技術創新獎 衛生署核准醫療器材 (衛署醫器製字第 002195號 )
Association of cognitive decline with reduced HRV p Objectives: To examine the association between cognitive decline and autonomic function measured by heart rate variability (HRV) in the nondemented elderly population. p Setting: Veterans house in urban community in Taipei, Taiwan. p Participants: Total of 63 nondemented male elderly man aged 65 and older with normal function of daily activities participated in this research. p Measurements: Cognitive functions were assessed by Cognitive Abilities Screening Instruments (CASI). Two hours electrocardiographic recordings at resting status were obtained from the recruited subjects for subsequent HRV analysis.
Association of cognitive decline with reduced HRV Yang, AC, et al. J Am Geriatr Soc 56(5): 958 -60 (2008).
Association of APOE gene polymorphism with reduced physiologic complexity p Objectives: We examined the impact of apolipoprotein-E (APOE) polymorphism on heart rate dynamics in a sample of healthy elderly men. p Setting: Veterans house in urban community in Taipei, Taiwan. p Participants: Total of 69 nondemented male elderly man aged 65 and older with normal function of daily activities participated in this research. p Measurements: Two means of complexity analysis adapted from the complexity theory were employed to analyze heart rate dynamics: detrended fluctuation analysis (DFA) and multiscale entropy (MSE). Cheng D. , et al. in submission
Association of APOE gene polymorphism with reduced physiologic complexity APOE ε 4 -negative APOE ε 4 -positive Cheng D. , et al. in submission
Association of APOE gene polymorphism with reduced physiologic complexity Cheng D. , et al. in submission
Association of BDNF gene polymorphism with altered sympathovagal balance p Objectives: We examine the association of BDNF Val 66 Met polymorphism with cardiac sympathovagal balance. p Participants: hospital colleagues at two medical centers: Taipei Veterans General Hospital and Kaohsiung E-DA Hospital, Taiwan. p Measurements: Two hours electrocardiographic recordings at resting status were obtained from the recruited subjects for subsequent HRV analysis. Yang, AC, et al. in submission
Association of BDNF gene polymorphism with altered sympathovagal balance Yang, AC, et al. in submission
Association of BDNF gene polymorphism with altered sympathovagal balance Yang, AC, et al. in submission
Autonomic dysfunction in headache patients reversible cerebral vasoconstriction syndromes p Objectives: This study aimed to assess the autonomic function by analyzing heart rate variability (HRV) in patients with reversible cerebral vasoconstriction syndromes (RCVS). p Participants: 42 Patients with RCVS and 42 age- and gender-matched controls were consecutively recruited. p Measurements: Each subject underwent 24 -hour ambulatory electrocardiographic (ECG) recordings with some patients receiving follow-up ECG monitoring during remission. HRV measures covering time and frequency domains were used to assess autonomic functioning. Yang, AC, et al. in submission
Autonomic dysfunction in headache patients RMSSD HF Yang, AC, et al. in submission
Autonomic dysfunction in headache patients LF / HF Yang, AC, et al. in submission
New Method of Quantifying Sleep Adapted from Cardiopulmonary Coupling Analysis developed initially by Dr. Thomas at Harvard University for detecting sleep apnea
Detecting Sleep Apnea: Cardiopulmonary Coupling Analysis Thomas RJ, Mietus JE, Peng CK, Goldberger AL. An electrocardiogram-based technique to assess cardiopulmonary coupling during sleep. Sleep. 2005; 28(9): 1151 -61. Gilmartin G, Mietus J, Peng C-K, Goldberger A, Thomas RJ. Sleep spectrograms and central sleepdisordered breathing in the sleep heart health study. Sleep 2006; 29: A 159. Yeh GY et al. , Enhancement of sleep stability with Tai Chi exercise in chronic heart failure: Preliminary findings using an ECG-based spectrogram method. Sleep Medicine 2008; 9: 527 -536. Thomas RJ et al. , Differentiating obstructive from central and complex sleep apnea using an automated electrocardiogram-based method. Sleep 2007; 30: 17561769.
Cyclic Alternating Patterns (CAP) A marker for sleep instability Figure from Sudhansu Chokroverty, Robert Thomas, Meeta Bhatt Atlas of Sleep Medicine Butterworth-Heinemann 2005
Cyclic Alternating Patterns (CAP) A marker for sleep instability Figure from Sudhansu Chokroverty, Robert Thomas, Meeta Bhatt Atlas of Sleep Medicine Butterworth-Heinemann 2005
Association of CAP/Non-CAP with Physiologic Parameters Mathematically, when combining heart rate dynamics and respiration signals to determine the degree of “coupling” … p Sleep-disordered breathing is associated with dominant low-frequency coupling p CAP (unstable sleep) is associated with lowfrequency (0. 01 -0. 1 Hz) coupling p Non-CAP (stable sleep) is associated with high-frequency (0. 1 -0. 4 Hz) coupling
遠距及自動化睡眠品質報告 http: //psynet. vghtpe. gov. tw 網路
Normal Subject with Good Sleep
Depressive Patient with Insomnia
Sleep spectrogram profile Yang, AC, et al. in submission
Sleep state transitions Fragmented sleep in a depressed patient
Sleep State Transitions Stable Sleep Unstable Sleep Efficient Sleep REM Wake Sleep Instability in Sleep Normal Depressed Patients Inefficient Sleep
Sleep state transitions Yang, AC, et al. in submission
Sleep fragmentation index Yang, AC, et al. in submission
Future Directions
Aging and illness: loss of complexity Healthy Dynamics: Multiscale Fractal Variability Two Patterns of Pathologic Breakdown Single Scale Periodicity Uncorrelated Randomness
Psychiatric disorder: loss of complexity Healthy Psychic Functioning Two Patterns of Pathologic Breakdown • Schizophrenia • negative symptoms • Schizophrenia • disorganized speech & behaviors • Unipolar depression • Bipolar disorder, mania • Obsessive-Compulsive Disorder • Borderline personality disorder • Dementia, early stage • Dementia with Behavioral and Psychological Symptoms
Sleep p 研究 n n p 臨床 n p 藥物對於睡眠穩定性的影響 睡眠穩定性的生理及分子調控機轉 自費睡眠品質檢測服務 產學合作 n n 開發新的多頻道睡眠硬體 開發低頻電刺激技術以增加深度睡眠
References p Yang A. C. , Tsai S. J. , Hong C. J. , Hsieh C. H. , Liu M. E. , Yang C. H. Association of heart rate variability and cognitive functions in nondemented male elderly subjects in the community: A preliminary report. J Am Geriatr Soc, J Am Geriatr Soc 56(5): 958 -60 (2008) p Cheng D. , Tsai S. J. , Hong C. J. , Yang A. C*. Reduced physiologic complexity of heart rate dynamics in healthy elderly men with APOE ε 4 allele: a pilot study. (2009) in submission (*corresponding author) p Yang A. C. , Chen T. J. , Tsai S. J. , Hong C. J. , Yang C. H. BDNF Val 66 Met polymorphism alters sympathovagal balance in healthy subjects. (2009) in submission p Yang A. C. , Chen S. P. , Fuh J. L. , Wu J. C. , Wang S. J. Autonomic Dysfunction in Reversible Cerebral Vasoconstriction Syndromes. (2009) in submission p Yang A. C. , Yang C. H. , Hong C. J. , Tsai S. J. , Kuo C. H. , Peng C. K. , Mietus J. E. , Goldberger A. L. , Thomas R. J. Sleep State Instabilities in Major Depression: Detection and Quantification with an Electrocardiogram-based Spectrogram Method. (2009) in submission
Acknowledgement p p p 台北榮總 n 蔡世仁主任 n 楊誠弘主任 n 洪成志醫師 n 王署君主任 (神經內科 ) n 傅中玲醫師 (神經內科 ) 助理 n 郭忠訓 (腦科所碩士班 ) 中央大學 數據分析方法研究中心 n 黃鍔院士 n 羅孟宗博士 p 哈佛大學 /Beth Israel Deaconess Medical Center n 彭仲康教授 n Ary Goldberger教授 n Robert Thomas醫師 p 硬體設計 n 微星公司 / 張儀中 p 網路應用程式 n Dyna. Dx Corp/ 劉彥輝博士
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