Signal Recovery from Noise in Biological Systems Chathurika D. Abeyrathne 1 , Peter M. Farrell 1 , and Malka N. Halgamuge 1 1 School of Engineering, The University of Melbourne, Melbourne, VIC 3010, Australia Abstract Summary There are signal enhancement methods in biological systems which are similar to those used in the electronic systems. In this paper we investigated the possibility of extracting weak electromagnetic signals from noise. Keywords-weak electromagnetic fields; noise; phase locked loop; parametric processes I. INTRODUCTION There is an inconsistency between the physical arguments and interpretation of the experimental studies of biological effects of weak electromagnetic fields (EMF). Some physical arguments and experimental demonstrations tend to reject the likelihood of any effect of fields at extremely low levels [1]. The problem arises of explaining, how the low-energy influences of weak EMFs compete with the thermal and electrical noise of cells at normal temperature based on the physical laws apply to those systems. There are standard techniques to recover signals from noise such as phase locked loops and parametric processes [2]. The question discussed in this paper is the possibility that such processes exists in biological systems. In this paper we are discussing the effect of EMF on the neuron’s membrane voltage. Further, the possible ways of interacting EMFs with the phase locked loop and parametric processes in biological systems are discussed. II. EFFECT OF ELECTROMAGNETIC FIELDS ON NEURON’S MEMBRANE VOLTAGE The Hodgkin-Huxley model is one of the standard methods for model neuron’s membrane voltage. External EMFs change the membrane voltage [3] and thus the instantaneous frequency of potential spikes. This membrane voltage is in the mV range. Hence, in order to have a detectable change in the instantaneous frequency of potential spikes due to external EMFs, the membrane voltage should be changed by a few mV [3]. According to Adair, mV changes in the membrane voltage can only be caused by the external electric fields in the 10 9 V range and magnetic fields in the Tesla range [1]. Adair has argued that the influences of weak EMFs are masked by the noise [1]. However, there is experimental evidence of weak EMF effects on biological systems. Hence, we investigate the possibility of enhancing these weak electromagnetic signals from phase locked loop and parametric processes in biological systems. A. Phase Locked Loop The oscillator of the phase locked loop follows the phase of the input potential spikes. The instantaneous phase of the potential spikes (input to the phase detector) is determined by the instantaneous frequency of spikes [4]. If the potential spikes are influenced by the noise, then the iterative loop improves the signal to noise ratio of the detected phase due to the reduced bandwidth [5]. In the phase locked loop potential spikes are locked to the local oscillator signal. Hence, if the instantaneous phase of the potential spikes is affected by an external EMF, the iterative loop follows the altered phase. Thus, the output signal from the phase detector and low pass filter are also influenced by the external EMFs. Further, if the frequency of the local oscillator is changed by the external EMFs then the number of spikes locked to each half cycle is changed. The local oscillator’s frequency can be changed by the frequency modulation of the external EMF. B. Parametric Processes The parametric processes amplify weak signals and improve signal to noise ratio [6]. If the tissue is modeled as a parallel RL circuit (resistor and inductor) and membrane as a time varying capacitor then the membrane voltage depends on the frequency of weak external electromagnetic signal. III. CONCLUSION The parametic processes and phase locked loops enhance the signal to noise ratio. If weak signals are amplified by the parametric processes then the neuron’s membrane voltage in mV range can be changed by weak EMFs. REFERENCES [1] Adair, R. K. (1991). Constraints on biological effects of weak extremely low frequency electromagnetic fields. Physical Review A, 43, 1039 - 1048. [2] Barnes, F. S. (1992). Some engineering models for interactions of electric and magnetic fields with biological systems. Bioelectromagnetics, 1, 67 - 85. [3] Wang, J., Che, Y., Zhou, S., & Deng, B. (2009). Unidirectional synchronization of Hodgkin–Huxley neurons exposed to ELF electric field. Chaos, Solitons and Fractals, 39, 1335 - 1345. [4] Songnian, Z., Xiaoyun, X., Guozheng, Y., & Zhi, F. (2003). A computational model as neurodecoder based on synchronous oscillation in the visual cortex. Neural Computation, 15, 2399 - 2418. [5] Servin, M., Rodriguez-Vera, R., & Malacara, D. (1995). Noisy fringe pattern demodulation by an iterative phase locked loop. Optics and Lasers in Engineering, 23, 355 - 365. [6] Heffner, H., & Wade, G. (1958). Gain, band width, and noise characteristic of the variable-parameter amplifier. Journal of Applied Physics, 29(9), 1321 - 1331.