Estimation of Postmortem Period by Means of Artificial Neural Networks


Abstract


The issue of estimating the postmortem period has always been a serious problem. Current methods do not provide satisfactory solutions. The problem is highly nonlinear and the variables involved are many and various.

In this work we aim to propose a new method for estimating the postmortem period. This method is based on artificial neural networks. We use Multilayer Feedforward Networks. Learning takes place in supervised mode. We give a comparative study on a sample of 257 individuals to prove the advantage brought by this new technique, improving in this way the precision of the estimates given by the traditional methods.


DOI Code: 10.1285/i20705948v9n2p326

Keywords: Period post mortem, thermometry, artificial neural network, estimation.

References


Naumenko VG. (1984 Apr-Jun); Current state and perspectives of the solution of the problem of determining the time of death. Sud Med Ekspert.; 27(2):9-12. [PubMed: 6464093]

Pigolkin IuI, Bogomolov DV, Korovin AA. (1999 May-Jun); The current methods for determining the time of death. Sud Med Ekspert. 42(3):31-3. [PubMed: 10396964]

Kaliszan M, Hauser R, Kernbach-Wighton G., (2009 May); Estimation of the time of death based on the assessment of post mortem processes with emphasis on body cooling. Leg Med (Tokyo).11(3):111-7. [PubMed: 19200767]

Amendt J, Richards CS, Campobasso CP, Zehner R, Hall MJ. (2011 Dec); Forensic entomology: applications and limitations. Forensic Sci Med Pathol. 7(4):379-92. [PubMed: 10396964]

Amendt J, Krettek R, Zehner R. (2004 Feb) Forensic entomology. Naturwissenschaften. 91(2):51-65. [PubMed: 14991142]

Açikgöz HN. (2010); Forensic entomology. Turkiye Parazitol Derg.34(3):216-21. [PubMed: 20954127]

Jashnani KD, Kale SA, Rupani AB. (2010 Nov); Vitreous humor: biochemical constituents in estimation of postmortem interval. J Forensic Sci.;55(6):1523-7. [PubMed: 20666922].

Lin X, Yin YS, Ji Q. (2011 Feb); Progress on DNA quantification in estimation of postmortem interval. Fa Yi Xue Za Zhi.;27(1):47-9, 53. [PubMed: 21542228].

Berent J. (2005 Jul-Sep); Determining the post mortem interval based on temperature measurements. Part I: From the first 19th century studies to Marshall & Hoare's double exponential model. Arch Med Sadowej Kryminol. 55(3):209-14. [PubMed: 16320770].

Berent J. (2006 Apr-Jun); Determining post mortem interval by temperature data. Part II:research results from the 1970s to the end of the 20th century. Arch Med Sadowej Kryminol. 56(2):103-9. [PubMed: 16970082]

Knight B. (1988 Jan); The evolution of methods for estimating the time of death from body temperature. Forensic Sci Int. 36(1-2):47-55. [PubMed: 3276584].

Verica P, Janeska B, Gutevska A, Duma A. (2007 Oct); Post mortem cooling of the body and estimation of time since death. Soud Lek. 52(4):50-6. [PubMed: 18189070].

C. Henssge, B. Knight, T. Krompecher, B. Madea, L. Nokes (2002); The estimation of the time since death in the early postmortem period, 2nd edn, Arnold Publishers, London.

Henssge C, Brinkmann B. (1984 Sep-Oct); Determination of time of death by rectal temperature. Mathematical analysis of empirical material versus thermodynamic modeling. A critical case presentation. Arch Kriminol. 174(3-4):96-112. [PubMed: 6508475].

Green MA, Wright JC. (1985 May); The theoretical aspects of the time dependent Z equation as a means of postmortem interval estimation using body temperature data only. Forensic Sci Int.;28(1):53-62. [PubMed: 4018682].

Nelson EL. (2000 Mar); Estimation of short-term postmortem interval utilizing core body temperature: a new algorithm. Forensic Sci Int. 13;109(1):31-8. [PubMed: 10759069].

Al-Alousi LM, Anderson RA, Worster DM, Land DV. (2002 Feb); Factors influencing the precision of estimating the postmortem interval using the triple-exponential formulae (TEF). Part II. A study of the effect of body temperature at the moment of death on the postmortem brain, liver and rectal cooling in 117 forensic cases.Forensic Sci Int. 18;125(2-3):231-6. [PubMed: 11909669].

Al-Alousi LM. (2002 Feb); A study of the shape of the post-mortem cooling curve in 117 forensic cases. Forensic Sci Int. 18;125(2-3):237-44. [PubMed: 11909670].

Kil'diushov EM, Kil'diushov MS. (2002 Sep-Oct); Determination of the time of death according to rectal thermometry data using computer calculations. Sud Med Ekspert. 45(5):3-5. [PubMed: 12516265].

den Hartog EA, Lotens WA. (2004 Sep); Postmortem time estimation using body temperature and a finite-element computer model. Eur J Appl Physiol. 92(6):734-7. [PubMed: 15185081].

Vavilov AIu, Viter VI. (2007 Sep-Oct); Using some modern mathematical models of postmortem cooling of the human body for the time of death determination. Sud Med Ekspert. 50(5):9-12. [PubMed: 18050683].

Viter VI, Vavilov AIu. (2008 Jan-Feb); State-of-the-art of mathematical modeling of postmortal thermodynamics for the time of death determination. Sud Med Ekspert. 51(1):15-8. [PubMed: 18326239].

Muñoz Barús JI, Febrero-Bande M, Cadarso-Suárez C. (2008 Oct); Flexible regression models for estimating postmortem interval (PMI) in forensic medicine. Stat Med. 30. 27(24):5026-38. [PubMed: 8618426].

Biermann FM, Potente S. (2011 Jul); The deployment of conditional probability distributions for death time estimation. Forensic Sci Int. 15. 210(1-3):82-6. [PubMed: 21377303].

Robert A. Dunne, (2007); A Statistical Approach to Neural Networks for Pattern Recognition; Wiley-Interscience, Hoboken, New Jersey.

Simon Haykin, (1999); Neural Networks, a comprehensive foundation, 2e edition, Prentice-Hall, Inc., Upper Saddle River, New Jersey.

Christopher M. Bishop, (1995); Neural Networks for Pattern Recognition; Clarendon Press, Oxford.

Christopher M. Bishop, (2006); Pattern Recognition and machine Learning, Springer Science + Business Media, LLC, Singapore.

G.Dreyfus, J.-M. Martinez, M.Samuelides, M.B. Gordon, F. Badran, S.Thiria, L. Hérault, (2002); Réseaux de neurones : Méthodologie et applications, Editions Eyrolles, Paris.

Hornik K. Stinchcombe M., White H. (1989); Multilayer feedforward networks are universal approximators, Neural Networks, 2, 359-366.

Hornik K. Stinchcombe M., White H. (1990); Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks, Neural Networks, 3, 551-560.

Hornik K. (1991); Approximations capabilities of multilayer feedforward networks, Neural Networks, 4, pp 251-257.

Terrence L. Fine (1999); Feedforward Neural Network Methodology, Springer–Verlag, New York.

Hagan, M.T., and M. Menhaj (1994); "Training feed-forward networks with the Marquardt algorithm," IEEE Transactions on Neural Networks, 5(6), 989-993.

Friedlander, E., Estimating the Time of Death, available via URL www.pathguy.com/TimeDead.htm

Schweitzer, W., Estimation of time of death, Method of Henssge, available via URL www.swisswuff.ch/calculators/todeszeit.php

Smart JL, Kaliszan M. (2012 Mar); The post mortem temperature plateau and its role in the estimation of time of death. A review. Leg Med (Tokyo).


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