Skip to main content
SpringerLink
Log in

Nonlinear features of heart rate variability in paranoid schizophrenic

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Cardiovascular mortality is significantly increased in patients suffering from schizophrenia. However, psychotic symptoms are quantified by means of the scale for the assessment of positive and negative symptoms, but many investigations try to introduce new etiology for psychiatric disorders based on combination of biological, psychological and social causes. Classification between healthy and paranoid cases has been achieved by time, frequency, Hilbert–Huang (HH) and a combination between those features as a hybrid features. Those features extracted from the Hilbert–Huang transform for each intrinsic mode function (IMF) of the detrended time series for each healthy case and paranoid case. Short-term ECG recordings of 20 unmedicated patients suffering from acute paranoid schizophrenia and those obtained from healthy matched peers have been utilized in this investigation. Frequency features: very low frequency (VLF), low frequency (LF), high frequency (HF) and HF/LF (ratio) produced promising success rate equal to 97.82 % in training and 97.77 % success rate in validation by means of IMF1 and ninefolds. Time–frequency features [LF, HF and ratio, mean, maximum (max), minimum (min) and standard deviation (SD)] provided 100 % success in both training and validation trials by means of ninefolds for IMF1 and IMF2. By utilizing IMF1 and ninefolds, frequency and Hilbert–Hang features [LF, HF, ratio, mean value of envelope (\(\bar{a}\))] produced 96.87 and 95.5 % for training and validation, respectively. By analyzing the first IMF and using ninefolds, time and Hilbert–Hang features [mean, max, min, SD, median, first quartile (Q1), third quartile (Q3), kurtosis, skewness, Shannon entropy, approximate entropy and energy, (\(\bar{a}\)), level of envelope variation ([\(\dot{a}\)(t)]^2), central frequency \((\bar{W})\) and number of zero signal crossing \((\left| {\bar{W}} \right|)\)] produced a 100 % success rate in training and 90 % success rate in validation. Time, frequency and HH features [energy, VLF, LF, HF, ratio and (\(\bar{a}\))] provided 97.5 % success rate in training and 95.24 % success rate in validation using IMF1 and sixfolds. However, frequency features have produced promising classification success rate, but hybrid features emerged the highest classification success rate than using features in each domain separately.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Acharya UR, Joseph KP (2006) Heart rate variability: a review. Med Bio Eng Comput 44(12):1031–1051

    Article  Google Scholar 

  2. Anas EMA, Lee SY, Hasan MK (2010) Sequential algorithm for life threatening cardiac pathologies detection based on mean signal strength and EMD functions. Biomed Eng Online 9(43):3–22

    Google Scholar 

  3. Andreasen NC, Arndt S, Miller D, Flaum M, Nopoulos P (1995) Correlational studies of the scale for the assessment of negative symptoms and the scale for the assessment of positive symptoms: an overview and update. Psychopathology 28(1):7–17

    Article  Google Scholar 

  4. Arlot S, Celisses A (2009) A survey of cross-validation procedures for model selection. Stat Surv 4:40–79. doi:10.1214/09-SS054

    Article  Google Scholar 

  5. Bär KJ, Boettger MK, Koschke M, Schulz S, Chokka P, Yeragani VK, Voss A (2007) Non-linear complexity measures of heart rate variability in acute schizophrenia. Clin Neurophysiol 118(9):2009–2015

    Article  Google Scholar 

  6. Bär KJ, Letzsch A, Jochum T, Wagner G, Greiner W, Sauer H (2005) Loss of efferent vagal activity in acute schizophrenia. J Psychiatr Res 39(5):519–527

    Article  Google Scholar 

  7. Barbieri R, Brown EN (2006) Analysis of heartbeat dynamics by point process adaptive filtering. IEEE Trans Biomed Eng 53(1):4–12

    Article  Google Scholar 

  8. Barentt AG, Wolff RC (2005) A time-domain test for some types of nonlinearity. IEEE Trans Signal Process 53(1):26–33

    Article  MathSciNet  Google Scholar 

  9. Benvenuto J, Jin Y, Casale M, Lynch G, Granger R (2002) Identification of diagnostic evoked response potential segments in Alzheimer’s disease. Exp Neurol 176(2):269–276

    Article  Google Scholar 

  10. Berntson GG, Bigger JT, Eckberg DL, Grossman P, Kaufmann PG, Malik M (1997) Heart rate variability: origins, methods, and interpretive caveats. Psychophysiology 34(6):623–648

    Article  Google Scholar 

  11. Boettger S, Hoyer D, Falkenhahn K, Kaatz M, Yeragani VK, Bär KJ (2006) Altered diurnal autonomic variation and reduced vagal information flow in acute schizophrenia. Clin Neurophysiol 117(12):2715–2722

    Article  Google Scholar 

  12. Boostani R, Sadatnezhad K, Sabeti M (2009) An efficient classifier to diagnose of schizophrenia based on the EEG signals. Expert Syst Appl 36(3):6492–6499

    Article  Google Scholar 

  13. Boutros NN, Arfken C, Galderisi S, Warrick J, Pratt G, Iacono W (2008) The status of spectral EEG abnormality as a diagnostic test for schizophrenia. Schizophr Res 99(1–3):225–237

    Article  Google Scholar 

  14. Burges JC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167

    Article  Google Scholar 

  15. Burri H, Chevalier P, Arzi M, Rubel P, Kirkorian G, Touboul P (2006) Wavelet transform for analysis of heart rate variability preceding ventricular arrhythmias in patients with ischemic heart disease. Int J Cardiol 109(1):101–107

    Article  Google Scholar 

  16. Castro MN, Vigo DE, Chu EM, Fahrer RD, de Achával D, Costanzo EY, Leiguarda RC, Nogués M, Cardinali DP, Guinjoan SM (2009) Heart rate variability response to mental arithmetic stress is abnormal in first-degree relatives of individuals with schizophrenia. Schizophr Res 109(1–3):134–140. doi:10.1016/j.schres.2008.12.026

    Article  Google Scholar 

  17. Carney RM, Freedland KE, Veith RC (2005) Depression, the autonomic nervous system, and coronary heart disease. Psychosom Med 67(Suppl 1):S29–S33

    Article  Google Scholar 

  18. Cerutti S, Bianchi AM, Mainardi LT (1995) Spectral analysis of the heart rate variability signal. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publishing Company, Armonk NY, pp 63–74

    Google Scholar 

  19. Cheng CH (1995) A branch and bound clustering algorithm. IEEE Trans Syst Man Cybern 25(5):895–898

    Article  Google Scholar 

  20. Collins MP, Pape SE (2011) P03-194—the potential of support vector machine as the diagnostic tool for schizophrenia: a systematic literature review of neuroimaging studies. Eur Psychiatry 26(1):1363

    Article  Google Scholar 

  21. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273. doi:10.1007/BF00994018

    MATH  Google Scholar 

  22. Ding H, Huang Z, Song Z, Yan Y (2007) Hilbert–Huang transform based signal analysis for the characterization of gas–liquid two phase flow. Flow Meas Instrum 18(1):37–46

    Article  Google Scholar 

  23. Doop ML, Park S (2009) Facial expression and face orientation processing in schizophrenia. Psychiatry Res 170(2–3):103–107

    Article  Google Scholar 

  24. Feldman M (2008) Theoretical analysis and comparison of the Hilbert transform decomposition methods. Mech Syst Signal Process 22(3):509–519

    Article  Google Scholar 

  25. Fernández EA, Neto EPS, Abry P, Macchiavelli R, Balzarini M, Cuzin B, Baude C, Frutoso J, Gharib C (2010) Assessing erectile neurogenic dysfunction from heart rate variability through a generalized linear mixed model framework. Comput Methods Programs Biomed 99(1):49–56

    Article  Google Scholar 

  26. Friedman BH, Thayer JF (1998) Autonomic balance revisited: panic anxiety and heart rate variability. J Psychosom Res 44(1):133–151

    Article  Google Scholar 

  27. Fritsch FN, Carlson RE (1980) Monotone piecewise cubic interpolation. SIAM J Numer Anal 17(2):238–246

    Article  MATH  MathSciNet  Google Scholar 

  28. Galletly CA, Clark CR, McFarlane AC (1996) Artificial neural networks: a prospective tool for the analysis of psychiatric disorders. J Psychiatry Neurosci 21(4):239–247

    Google Scholar 

  29. Goren Y, Davrath LR, Pinhas I, Toledo E, Akselrod S (2006) Individual time-dependent spectral boundaries for improved accuracy in time-frequency analysis of heart rate variability. IEEE Trans Biomed Eng BME 53(1):35–42

    Article  Google Scholar 

  30. Gregory JA (1986) Shape preserving spline interpolation. Comput Aided Des 18(1):53–57

    Article  Google Scholar 

  31. Grodins FS (1959) Integrative cardiovascular physiology: a mathematical synthesis of cardiac and blood vessel hemodynamics. Q Rev Biol 34(2):93–116

    Article  Google Scholar 

  32. Guyton AC, Hall JE (2000) Medical physiology, 11th edn. Elsevier Saunders, Chapter 55

  33. Han Q, Wang P (2007) Estimation of the largest Lyapunov exponent of the HRV signals. PubMed 24(4):732–735

    Google Scholar 

  34. Healey KM, Pinkham AE, Richard JA, Kohler CG (2010) Do we recognize facial expressions of emotions from persons with schizophrenia? Schizophr Res 122(1–3):144–150

    Article  Google Scholar 

  35. Henry BL, Minassian A, Paulus MP, Geyer MA, Perry W (2010) Heart rate variability in bipolar mania and schizophrenia. J Psychiatr Res 44(3):168–176. doi:10.1016/j.jpsychires.2009.07.011

    Article  Google Scholar 

  36. Hodrick RJ, Prescott EC (1997) Postwar U.S. business cycles: an empirical investigation. J Money Credit Bank 29(1):1–16

    Article  Google Scholar 

  37. Hojgaard MV, Holstein-Rathlou NH, Anger EE, Kanters JK (1998) Dynamics of spectral components of heart rate variability during change in autonomic balance. Am J Physiol 275(1)Pt 2:H213–H219

  38. Hong LE, Turano KA, O’Neill HB, Hao L, Wonodi I, McMahon RP, Thaker GK (2009) Is motion perception deficit in schizophrenia a consequence of eye-tracking abnormality? Biol Psychiatry 65(12):1079–1085

    Article  Google Scholar 

  39. Huang J, Chan RC, Gollan JK, Liu W, Ma Z, Li Z, Gong QY (2011) Perceptual bias of patients with schizophrenia in morphed facial expression. Psychiatry Res 185(1–2):60–65

    Article  Google Scholar 

  40. Huang N, Attoh-Okine NO (2005) The Hilbert–Huang transform in engineering, Chap. 1. Taylor & Francis, New York, ISBN 13: 978-0-8493-3422-1

  41. Huang N, Wu M, Qu W, Long S, Shen S (2003) Applications of Hilbert–Huang transform to non-stationary financial time series analysis. Appl Stoch Models Bus Ind 19(3):245–268

    Article  MATH  MathSciNet  Google Scholar 

  42. Huang NE, Shen SSP (2005) Hilbert–Hung transform and its applications, interdisciplinary mathematical sciences, vol 5. World Scientific Publication, London

    Google Scholar 

  43. Huang NE, Shen Z, Long SR, Wu M, Shih HH, Zheng Q, Yen NC, Tung CC, Liu HH (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time-series analysis. Proc R Soc Lond A 454(1971):903–905

    Article  MATH  MathSciNet  Google Scholar 

  44. Junsheng C, Dejie Y, Yu Y (2006) Research on the intrinsic mode function (IMF) criterion in EMD method. Mech Syst Signal Process 20(4):817–824

    Article  Google Scholar 

  45. Kallimani D, Theleritis C, Evdokimidis I, Stefanis NC, Chatzimanolis I, Smyrnis N (2009) The effect of change in clinical state on eye movement dysfunction in schizophrenia. Eur Psychiatry 24(1):17–26

    Article  Google Scholar 

  46. Kamel MS, Selim SZ (1994) A relaxation approach to the fuzzy clustering problem. Fuzzy Sets Syst 61(2):177–188

    Article  MathSciNet  Google Scholar 

  47. Karavidas MK, Lehrer PM, Vaschillo E, Vaschillo B, Marin H, Buyske S, Malinovsky I, Radvanski D, Hassett A (2007) Preliminary results of an open label study of heart rate variability biofeedback for the treatment of major depression. Appl Psychophysiol Biofeedback 32(1):19–30

    Article  Google Scholar 

  48. Kheder G, Kachouri A (2008) HRV analysis using wavelet package transform and least square support vector machine. Int J Circuits Syst Signal Process 2(1):18–25

    Google Scholar 

  49. Kleiger RE, Stein PK, Bosner MS, Rottman JN (1995) Time domain measurements of heart rate variability. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publishing Company, inc., Armonk, NY, pp 33–45

    Google Scholar 

  50. Kwiatkowski D, Phillips PCB, Schmidt P, Shin Y (1992) Testing the null hypothesis of stationarity against the alternative of a unit root. J Econ 54(1–3):159–178

    Article  MATH  Google Scholar 

  51. Lake D (2006) Renyi entropy measures of heart rate Gaussianity. IEEE Trans Biomed Eng BME 53(1):21–27

    Article  Google Scholar 

  52. Lan LY, Chen LT (2012) Prevalence of high blood pressure and its relationship with body weight factors among inpatients with schizophrenia in Taiwan. Asian Nurs Res 6(1):13–18

    Article  Google Scholar 

  53. Lendasse A, Wertz V, Verleysen M (2003) Model selection with cross validations and bootstraps application to time series prediction with RBFN models. In: ICANN/ICONIP LNCS, vol 2714, pp 573–580. doi: 10.1007/3-540-44989-2_68

  54. Linden SC, Jackson MC, Subramanian L, Wolf C, Green P, Healy D, Linden DE (2010) Emotion–cognition interactions in schizophrenia: implicit and explicit effects of facial expression. Neuropsychologia 48(4):997–1002

    Article  Google Scholar 

  55. Litvack DA, Oberlander TF, Carney LH, Saul JP (1995) Time and frequency domain methods for heart rate variability analysis: a methodological comparison. Psychophysiology 32(5):492–504

    Article  Google Scholar 

  56. Liu YH, Chen YT (2007) Face recognition using total margin-based adaptive fuzzy support vector machines. IEEE Trans Neural Netw 18(1):178–192

    Article  Google Scholar 

  57. Liu YH, Huang HP, Weng CH (2007) Recognition of electromyographic signals using cascaded kernel learning machine. IEEE/ASME Trans Mechatron 12(3):253–264

    Article  Google Scholar 

  58. Low PA, Pfeifer MA (1997) Standardization of autonomic function. In: Low PA (ed) Clinical autonomic disorders. Little, Brown and Company, New York, pp 287–295

    Google Scholar 

  59. Malik M (1996) Heart rate variability. Standards of measurement, physiological interpretation, and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Eur Heart J 17(23):354–381. doi:10.1161/01.CIR.98.23.2643

    Article  Google Scholar 

  60. Malliani A (2000) Principles of cardiovascular neural regulation in health and disease. Circulation. doi:10.1161/01.CIR.0000027503.89988.2A

    Google Scholar 

  61. Mao J, Jain AK (1996) A self-organizing network for hyperellipsoidal clustering (HEC). IEEE Trans Neural Netw 7(1):16–29

    Article  Google Scholar 

  62. Merati G, Rienzo MD, Parati G, Veicsteinas A, Castiglioni P (2006) Assessment of the autonomic control of heart rate variability in healthy and spinal-cord injured subjects: contribution of different complexity-based estimators. IEEE Trans Biomed Eng BME 53(1):43–52

    Article  Google Scholar 

  63. Mitchell T (1997) Machine learning, 1st edn. Chap. 1 and Chap. 2, McGraw Hill, New York

  64. Myers GA, Martin GJ, Magid NM, Barnett PS, Schhaad JW, Lesch M, Singer DH (1986) Power spectral analysis of heart rate variability in sudden cardiac death: comparison to other methods. IEEE Trans Biomed Eng 33(12):1149–1156

    Article  Google Scholar 

  65. Nakata A, Takata S, Yuasa T, Shimakura A, Nagai H, Sakagami S, Kobayashi K (1998) Spectral analysis of heart rate, arterial pressure and muscle sympathetic nerve activity in normal human. Am J Physiol 274(4 Pt 2):H211–H217

    Google Scholar 

  66. Niskanen JP, Tarvainen MP, Ranta-aho PO, Karjalainen PA (2004) Software for advanced HRV analysis. Comput Methods Programs Biomed 76(1):73–81

    Article  Google Scholar 

  67. Osuna E, Freund R, Girosi F (1997) Training support vector machines: an application to face detection. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition. IEEE Computer Society, Puerto Rico, pp 130–136

  68. Perakakis P, Joffily M, Taylor M, Guerra P, Vila J (2010) KARDIA: a Matlab software for the analysis of cardiac interbeat intervals. Comput Methods Programs Biomed 98(1):83–89

    Article  Google Scholar 

  69. Pichot V, Gaspoz JM, Molliex S, Antoniadis A, Busso T, Roche F, Costes F, Quintin L, Lacor JR, Barthelemy J (1999) Wavelet transform to quantify heart rate variability and to assess its instantaneous changes. J Appl Physiol 86(3):1081–1091

    Google Scholar 

  70. Pincus SM, Glodbergar AL (1994) Physiological time-series analysis: what does regularity quantify? Am J Physiol 266(4) Pt 2: H1643–H1656

  71. Polat K, Günes S (2007) A novel approach to estimation of E. coli promoter gene sequences: combining feature selection and least square support vector machine (FS_LSSVM). Appl Math Comput 190(2):1574–1582

    Article  MATH  MathSciNet  Google Scholar 

  72. Polat K, Günes S, Arslan A (2008) A cascade learning system for classification of diabetes disease: generalized discriminant analysis and least square support vector machine. Expert Syst Appl 34(1):482–487

    Article  Google Scholar 

  73. Quek T, Tua S, Wang Q (2003) Detecting anomalies in beams and plate based on the Hilbert–Huang transform of real signals. Smart Mater Struct 12(3):447–460. doi:10.1088/0964-1726/12/3/316

    Article  Google Scholar 

  74. Rao R, Hsu EC (2008) Hilbert–Huang transform analysis of hydrological and environmental time series. Water Science and Technology Library, vol 60. Springer, The Netherlands. ISBN 978-1-4020-6454-8

  75. Saini BS, Singh SD, Uddin M, Kumar V (2008) Improved power spectrum estimation for RR-interval time series. World Acad Sci Eng Technol 46(10):44–48

    Google Scholar 

  76. Sapoznikov D, Luria MH, Gotsman MS (1993) Comparison of different methodologies of heart rate variability analysis. Comput Methods Programs Biomed 41(2):69–75

    Article  Google Scholar 

  77. Selim SZ, Al-Sultan KS (1991) A simulated annealing algorithm for the clustering problem. Pattern Recogn 24(10):1003–1008

    Article  MathSciNet  Google Scholar 

  78. Singer DH, Ori Z (1995) Changes in heart rate variability associated with sudden cardiac death. In: Malik M, Camm AJ (eds) Heart rate variability. Futura Publishing Company, New York, pp 429–448

    Google Scholar 

  79. Solem K, Laguna P, Sörnmo L (2006) An efficient method for handling ectopic beats using the heart timing signal. IEEE Trans Biomed Eng BME 53(1):13–20

    Article  Google Scholar 

  80. Tang J, Zou Q. Tang Y, Liu B, Xiao-kai Z (2007) Hilbert–Huang transform for ECG de-noising. In: Bioinformatics and biomedical engineering. ICBBE 2007. The 1st international conference on, Wuhan, pp 664–667, doi: 10.1109/ICBBE.2007.173

  81. Tarvainen MP, Niskanen JP, Lipponen JA, Ranta-aho PO, Karjalainen PA (2014) Kubios HRV—heart rate variability analysis software. Comput Methods Programs Biomed 113(1):210–220

    Article  Google Scholar 

  82. Theodoridis S, Koutroumbas K (2003) Pattern recognition, 2nd edn. Elsevier Academic Press, San Diego

    Google Scholar 

  83. Theodoridis S, Koutroumbas K (2010) An introduction to pattern recognition: a Matlab approach, 4th edn, Chap. 3, Elsevier Inc., New York

  84. Togo F, Kiyono K, Struzik ZR, Yamamoto Y (2006) Unique very low-frequency heart rate variability during deep sleep in humans. IEEE Trans Biomed Eng BME 53(1):28–34

    Article  Google Scholar 

  85. Toledo E, Gurevitz O, Hod H, Eldar M, Akselro S (2003) Wavelet analysis of instantaneous heart rate: a study of autonomic control during thrombolysis. Am J Physiol Regul Integr Comp Physiol 284(4):1079–1091

    Google Scholar 

  86. Valkonen-Korhonen M, Tarvainen MP, Ranta-Aho P, Karjalainen PA, Partanen J, Karhu J, Lehtonen J (2003) Heart rate variability in acute psychosis. Psychophysiology 40(5):716–726

    Article  Google Scholar 

  87. Vapnik VN (1998) Statistical learning theory, 1st edn. Chap. 1 and Chap. 2, Wiley-Interscience, New York

  88. Vert JP, Tsuda K, Scholkopf B (2004) Kernel methods in computational biology, 1st edn. Massachusetts Institute of Technology, Chapter 2, USA, pp 35–77, ISBN 0-262-19509-7

  89. Vigo DE, Guinjoan SM, Scaramal M, Siri LN, Cardinali DP (2005) Wavelet transform shows age-related changes of heart rate variability within independent frequency components. Auton Neurosci Basic Clin 123(1–2):94–100

    Article  Google Scholar 

  90. Xun J, Yan S (2008) A revised Hilbert–Huang transformation based on the neural networks and its application in vibration signal analysis of a deployable structure. Mech Syst Signal Process 22(7):1705–1723

    Article  Google Scholar 

  91. Weiner RD (2001) the practice of electroconvulsive therapy: recommendations for treatment, training, and privileging, 2nd edn. American Psychiatric Association, Committee on Electroconvulsive Therapy. American Psychiatric Publishing, Washington, DC. ISBN 978-0-89042-206-9

  92. Yu B, Yuan B (1995) A global optimum clustering algorithm. Eng Appl Artif Intell 8(2):223–227

    Article  Google Scholar 

  93. Zhang LY, Wei HH, Wang GJ, Chen FB, Sun J, Ning LI, Gao ZQ (2011) Study on discriminant analysis by military mental disorder prediction scale for mental disorder of new recruits. Med J Chin People’s Lib Army 36(11):1226–1230

    Google Scholar 

  94. Zhang WR, Pandurangi AK, Peace KE, Zhang YQ, Zhao Z (2011) MentalSquares: a generic bipolar support vector machine for psychiatric disorder classification, diagnostic analysis and neurobiological data mining. Int J Data Min Bioinform 5(5):532–557

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Biomedical Engineering Department, Faculty of Engineering, Misr University for Science and Technology, 6th of October City, Egypt

    Mohamed Abdelkader Aboamer

  2. Faculty of Computers and Information, Benha University, Benha, Egypt

    Ahmad Taher Azar

  3. Faculty of Engineering, Systems & Biomedical Engineering Department, Cairo University, Giza, Egypt

    Abdallah S. A. Mohamed & Khaled Wahba

  4. Klinik für Psychiatrie und Psychotherapie, Universitätsklinikum Jena, Jena, Germany

    Karl-Jürgen Bär & Sandy Berger

Authors
  1. Mohamed Abdelkader Aboamer
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ahmad Taher Azar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abdallah S. A. Mohamed
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Karl-Jürgen Bär
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Sandy Berger
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Khaled Wahba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Ahmad Taher Azar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aboamer, M.A., Azar, A.T., Mohamed, A.S.A. et al. Nonlinear features of heart rate variability in paranoid schizophrenic. Neural Comput & Applic 25, 1535–1555 (2014). https://doi.org/10.1007/s00521-014-1621-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-014-1621-1

Keywords

Search

Navigation