Remote Monitoring of Phasic Heart Rate Changes From the ... - Neteera

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Remote Monitoring of Phasic Heart Rate Changes From the Palm Yael Kurzweil-Segev, Moshe Brodsky, Alik Polsman, Eli Safrai, Yuri Feldman, Sharon Einav, and Paul Ben Ishai

Abstract—The ever-increasing pace of development of high-frequency sources is opening new vistas in the field of remote physiological sensing. In this work, we report the successful detection of the heart beat in the reflection coefficient of human skin, monitored at 303 and 404 GHz. The measurement was taken at a distance of 72 cm and the beam was focused on the hand palm. The results demonstrate a high correlation with the heart beat extracted from a concurrent ECG measurement. Furthermore, these frequencies demonstrate a good capability to follow phasic changes in the heart rate. Index Terms—Heart rate (HR), phasic response, remote monitoring, sub-THz radiation.

I. INTRODUCTION

T

HE need to monitor physiological data is a characteristic of the current day and age. Apart from the “conventional” requirements for monitoring of physiological changes for medical purposes, the modes and motivation of data collection are becoming increasingly diverse. For example, surveillance systems designed to detect people with fever in a large crowd are currently being developed exploiting methods that seek an abnormal body heat signature [1]–[3]. The decoding of facial expressions is also being explored as a tool to detect stress in the individual [4], [5]. Heart rate (HR) irregularity is another vital sign that is currently being investigated as a telltale sign of personal stress [6]–[9]. Momentary changes in HR (also called “phasic” HR changes) stem from the sympathetic nervous response of the body and can encode important information on sudden stress. In mock-crime studies, for example, questions regarding relevant items of the crime were associated with a relative reduction of the phasic HR compared with questions regarding irrelevant items [10], [11]. No differences in phasic HR were observed in subjects who remained ignorant of the ”crime” [12]. In the last few years, there have been several attempts to monitor human HR remotely, using microwave radiation in the GHz band [13] and interferometric methods from the chest wall. In

these studies, the phase was extracted and was used to compute the displacement of the chest. Such measurements provide an accurate estimate of respiratory rate through tracking of chest wall excursion (10 to 20 mm) [14]. However, precise measurement of the HR at distances up to 10 m from the chest wall has currently been possible only using 94 GHz. Unfortunately, with 94 GHz, one cannot measure the signal through clothing that contains organic fabric because one cannot avoid the dielectric signature of water at this frequency. Consequently, there is strong motivation to seek a signal measurable at higher frequencies. In particular, [15] attacked this problem theoretically by assuming a model for the periodic variation in any reflected signal from the human as a result of the heartbeat. To date, the highest frequency measurement of this sort was reported at 228 GHz with an aperture of 15 cm from distances of up to 50 m [16]. We report a successful remote HR measurement from the palm using 303 and 404 GHz. This incidental result was observed during performance of a broader study of phasic emotional reactions, as measured through the reflection coefficient of the human hand at these frequencies. The motivation for the broader study was the observation that the human eccrine sweat duct is helical in the upper layers of the skin [17]. This morphology, coupled with the dielectric properties of human skin, led to the hypothesis that the duct could work as a passive antenna in the extremely high frequency range (70–400 GHz) [18]–[21]. Being coupled to the sympathetic nervous response (SNR) of the body, stress can be deduced from the reflection coefficient, due to the electromagnetic response of these ducts [18], [20]. However, motion of the reflection plane, for instance, vibration of the palm of the hand, could also introduce a perturbation to measured reflection coefficient. It is this vibrational element that we will explore in this paper. The palm is a practical location for performance of measurements because it mostly remains exposed. Thus, the implications of this finding are also considered. II. MEASUREMENTS A. Subjects

Manuscript received November 17, 2013; revised April 09, 2014; accepted May 29, 2014. Y. Kurzweil-Segev, M. Brodsky, A. Polsman, E. Safrai, Y. Feldman, and P. B. Ishai are with The Hebrew University of Jerusalem, Applied Physics, Jerusalem, Israel (e-mail: [email protected]; [email protected]; alexande. [email protected]; [email protected]; [email protected]; [email protected]; [email protected]; [email protected]). S. Einav is with The Hebrew University of Jerusalem, Applied Physics, Jerusalem, Israel, and also with Shaare Zedek Medical Center, Jerusalem, Israel. Digital Object Identifier 10.1109/TTHZ.2014.2330196

The local ethical committee of the Hebrew University of Jerusalem approved the study, and the study was carried out in accordance with their research guidelines. Prior to recruitment, candidates were informed regarding the duration, intensity, and frequency of exposure to electromagnetic radiation and its relevance to human health. All freely gave their written consent to participate. Measurements were carried out on three male subjects aged 27, 29, and 30. The subjects confirmed that they

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were not taking any medications and were healthy to the best of their knowledge. B. Measurement Setup The reflection coefficient of the hand was measured using an AB MILLIMETRE Vector Network Analyzer (MVNA) [22] that can cover the frequency range 8–660 GHz (AB millimeter Ltd., Paris, France). The optical path in this setup from the source to the hand was 72 cm and was focused onto the palm with elliptical mirrors (see Fig. 1). This system is based on harmonic multipliers, multiplying a base frequency range of 8–18 GHz to the desired frequency range of interest. Typically the impinging power on the sample is less than 1 W and the dynamic range of the system in the -band is about 120 dB [22]. The MVNA was set up to work in continuous-wave mode, transmitting concurrent signals at 303 and 404 GHz. In this frequency range, the generator and detector are separated. The wire grids act as the necessary directional coupler. The hand of the subject was immobilized at the point of focus, using a special holder designed to be of minimal inconvenience to the subject. Prior to the initiation of the study the measurement protocol was read to the subject and the subject was asked to avoid movement as much as possible during measurement. In order to avoid tonic stress, the subject was seated on an ergonomic chair. The right hand of the subject was immobilized with the bottom of the palm at the point of focus, using a custom-made holder which was designed to impose minimal inconvenience while providing maximal stabilization of the hand and preventing flinch movements. The reflection coefficients at 303 and 404 GHz were measured concurrently. The reflected signal was sampled every 0.1 s. Phasic emotional stress was induced by viewing a picture arousal test, designed to elicit an emotional response. The test consisted of a number of images displayed on a monitor. All images were taken from the International Affective Picture System (IAPS) [23], [24], provided by CSEA (“looking at pictures: affective, facial, visceral and behavioral reactions”). IAPS images can range from benign landscapes to explicit images of mutilation. The advantage of the IAPS is that it contains data on each image in terms of its psychological effects (dominance, arousal, and pleasure) drawn from an extensive pool of subjects. For the purpose of the current experiment, the computer program displayed a random set of images drawn from IAPS, interspersed with a white screen. The subject was exposed to each image for 6 s at a time and to the white screen for 8–12 s. The exposure times of the white screen were randomly varied in order to avoid “training” and increase the probability of a phasic reaction. The program contained 24 pictures that included six negative pictures (IAPS rating Arousal 1–3, Pleasure ), six unpleasant pictures (in the IAPS rating system defined as Arousal level 5-7, Pleasure level 4-7), six very negative pictures (IAPS rating Arousal 8-9, Pleasure 1-3) and six neutral pictures (IAPS rating Arousal 1-3, Pleasure 8-9). The main measure in choosing the images was the IAPS Arousal factor. Care was taken to avoid sequential viewing of particularly intense images. Three-lead electrocardiography was measured concurrently with electrodes placed according to Einthoven’s triangle arrangement on the chest wall of the subject [25]. This technique

Fig. 1. Schematic representation of the MVNA measuring system. The arrows represent the propagation of the beam from the source to the sample, and its reflection to the detector. A directional coupler is created by two polarizing grids, standing at 45 to each other. The schematic of the hand shows the area probed by the beam. The stratum corneum is approximately 100 m in this area, and there are no veins close to the surface.

Fig. 2. Representative ECG trace, demonstrating the main features of the graph. The relevant parameter for this paper is the RR peak-to-peak time lapse that reflects the pulse rate.

is mainly used to identify heart disease [26]. The instrument used to record the data was an ADInstruments ML138 octal bio amp platform (ADInstruments Pty Ltd., Castle Hill, Australia). The bandwidth of the bio amp was 5 kHz. A typical ECG trace is presented in Fig. 2. The main features of the trace are labeled P, Q, R, S and T. Clinical diagnosis through interpretation of an ECG is carried out by comparing several parameters of the ECG trace to a normal baseline [27]. It is not the purpose of this article to provide a detailed description of the various components of the ECG and their clinical meaning can be found in [26], [28]. In order to rule out the possibility that the measured signal was being generated by a spectral component originating from

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Fig. 3. Amplitude of the reflection coefficient of the human hand, measured (a) at 404 GHz and (b) at 303 GHz as part of the picture arousal test. The dashed lines represent the moment the subject is shown an image. The solid circles indicate that the image a particularly unpleasant one and the solid squares indicate an unpleasant picture. These lines are carried down to the bottom panel. The inserts demonstrates the steady oscillation due to the heart beat.

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Fig. 4. Phase of the measured signal (a) at 404 GHz and (b) at 303 GHz, during the picture arousal test. The dotted vertical lines indicate the showing of a picture to the subject. Solid circles indicate particularly unpleasant pictures and solid squares indicate unpleasant images. The lines are continued down to the bottom panel.

movement rather than from changes to the dielectric properties of the skin, the hand of one subject was covered with aluminum foil, 15.5 m thick. The experiment was repeated with the hand thus wrapped. All data were analyzed with codes developed in our lab on a MATLAB platform [29]. III. RESULTS AND DISCUSSION Figs. 3 and 4 present typical traces of the amplitude (a) and phase (b) of the reflected signals at 404 and 303 GHz, respectively, measured from the subject during the experiment. In Figs. 3 and 4, dot-headed vertical lines represent particularly unpleasant images (for instance, mutilated bodies) and square headed lines represent unpleasant images. Both amplitude (Fig. 3) and phase (Fig. 4) demonstrate responses to the images at both frequencies. While these responses to disturbing images are interesting and will be the subject of further study in the future, we will concentrate on the existence of a constant oscillation of the signal throughout the experiment. In order to understand this feature further, a discrete fast Fourier transform (FFT) was applied to the amplitude of this signal (see Fig. 5). There is a noticeable peak around the value of 1 Hz, which coincides with the resting HR frequency in most people. In order to isolate only the spectral components pertaining to this oscillation a band pass filter (covering 0.75 to 1.5 Hz) was applied to the original amplitude and phase. An example of the result is shown in Fig. 6. By marking the time position of each positive peak, taking the derivative and

Fig. 5. Discrete Fourier transform of the electromagnetic response, as measured amplitude of the signal at 404 GHz during the picture arousal test, showing the normalized power per frequency.

calculating the reciprocal, the HR can be measured in beats per minute. The HR was also extracted from the ECG according to the equation (1) is the time between the peak of successive beats Where [26] (see Fig. 2). The HR signal as calculated from an electromagnetic response can be seen in unison with the heart rate signal, as

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TABLE I THE CORRELATION BETWEEN THE HEART RATES CALCULATED FROM THE ECG TRACE AND FROM THE REFLECTION COEFFICIENT AT 303 AND 404 GHZ

Fig. 6. Filtered signal amplitude at 404 GHz. The filter is a bandpass filter with window 0.75 to 1.5 Hz.

Both HR signals were interpolated to the time axis of the electromagnetic response, using nearest neighbor and extrapolation methods. Following this the correlation between the two could be calculated by

(2)

The MATLAB function “corr2.m” [29] was used for this purpose. Table I shows the relatively high correlation observed between the two signals. After removal of the tail sections, which arrive only as an artifact of the smoothing, this correlation approached 0.93. Only relevant subjects are shown. These measurements demonstrate very good correlation for all subjects for the phase of the signal. For subject 1, there was significant correlation for the amplitude of the signal as well. The very low levels of correlation between the HR and the amplitude for subject 2 suggest that the subject’s hand was out of the plane of focus. The fact that the highest correlation is seen in the phase, even after covering the hand in aluminum foil, suggests a slight linear movement of the palm that would leave the amplitudes of the impinging and returning wave similar, but introducing a time lag between them. This view is further reinforced by the target area on the palm, where there are no veins near the surface, negating variation in flow as the source of the effect. Since the hand was fixed within a holder giving support to the forearm from the elbow this would be consistent with a tremor, on a par with a tenth of a wavelength, approximately 0.07 mm. This is consistent with literature values of hand tremor while at rest [30]. IV. LIMITATIONS OF THE STUDY Fig. 7. HR signals after interpolation and smoothing, as calculated from the filtered signal amplitude (a) at 404 GHz and (b) at 303 GHz. The right-hand axis in both panels is the heart beat as calculated from the electrocardiogram during the picture arousal test.

calculated from the electrocardiogram, in Fig. 7. We applied smoothing by using a moving average of 81 points corresponding to 8 s.

The subject pool for this study was limited and needs to be expanded. This should ideally be conducted on a pool of ten subjects with variations in gender and age. A further point to note is that the hand was not freestanding, and, in future studies, the effect of natural movement on the acquired signal should be investigated. The use of aluminum foil to isolate reflection originating from movements is a crude technique. Future studies should address a more dependable method.

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V. CONCLUSION We demonstrate that it is possible to accurately extract the HR from the reflection coefficient of the human hand at frequencies up to 404 GHz. More importantly, this is possible because of a submillimeter tremor of the hand. This tremor leads to a phase shift that is accurately collated to the HR obtained directly from an ECG trace. Furthermore, this measurement was done remotely at a distance of 75 cm (optical path). These excellent correlations suggest that one might apply existing algorithms that decode the stress level of the individual as a function of HR irregularities. While these ideas are interesting they have yet to be pursued. ACKNOWLEDGMENT The authors would like to gratefully thank Prof. G. Ben Shachar, of the Hebrew University, for many interesting and fruitful discussions without which this article could not have been written. REFERENCES [1] A. V. Nguyen, N. J. Cohen, H. Lipman, C. M. Brown, N.-A. Molinari, W. L. Jackson, H. Kirking, P. Szymanowski, T. W. Wilson, B. A. Salhi, R. R. Roberts, D. W. Stryker, and D. B. Fishbein, “Comparison of 3 infrared thermal detection systems and self-report for mass fever screening,” Emerging Infectious Diseases, vol. 16, no. 11, pp. 1710–1717, Nov. 2010. [2] H. Nishiura and K. Kamiya, “Fever screening during the influenza (H1N1-2009) pandemic at Narita International Airport, Japan,” BMC Infectious Diseases, vol. 11, no. 1, p. 111, May 2011. [3] G. Sun, S. Abe, O. Takei, and T. Matsui, “A portable screening system for onboard entry screening at international airports using a microwave radar, reflective photo sensor and thermography,” in Proc. 2nd Int. Conf. Instrum., Commun., Inf. Technol., Biomed. Eng., 2011, pp. 107–110. [4] D. F. Dinges, R. L. Rider, J. Dorrian, E. L. McGlinchey, N. L. Rogers, Z. Cizman, S. K. Goldenstein, C. Vogler, S. Venkataraman, and D. N. Metaxas, “Optical computer recognition of facial expressions associated with stress induced by performance demands,” Aviation, Space, Environ. Med., vol. 76, no. 6, pp. B172–B182, Jun. 2005. [5] P. Ekman, “Facial expressions,” in Handbook of Cognition and Emotion. New York, NY, USA: Wiley, 1999, pp. 301–320. [6] M. Pagani, G. Mazzuero, A. Ferrari, D. Liberati, S. Cerutti, D. Vaitl, L. Tavazzi, and A. Malliani, “Sympathovagal interaction during mental stress. A study using spectral analysis of heart rate variability in healthy control subjects and patients with a prior myocardial infarction,” Circulation, vol. 83, no. 4 Suppl, pp. II43–51, Apr. 1991. [7] T. G. M. Vrijkotte, L. J. P. van Doornen, and E. J. C. de Geus, “Effects of work stress on ambulatory blood pressure, heart rate, and heart rate variability,” Hypertension, vol. 35, no. 4, pp. 880–886, Apr. 2000. [8] P. A. Obrist, C. J. Gaebelein, E. S. Teller, A. W. Langer, A. Grignolo, K. C. Light, and J. A. McCubbin, “The relationship among heart rate, carotid dp/dt, and blood pressure in humans as a function of the type of stress,” Psychophysiology, vol. 15, no. 2, pp. 102–115, Mar. 1978. [9] G. G. Berntson and J. T. Cacioppo, “Heart rate variability: Stress and psychiatric conditions,” in Dynamic Electrocardiography. Oxford, U.K.: Blackwell, 2004, pp. 57–64. [10] M. T. Bradley and D. Ainsworth, “Alcohol and the psychophysiological detection of deception,” Psychophysiology, vol. 21, no. 1, pp. 63–71, Jan. 1984. [11] M. Gamer, H.-G. Rill, G. Vossel, and H. W. Gödert, “Psychophysiological and vocal measures in the detection of guilty knowledge,” Int. J. Psychophysiol., vol. 60, no. 1, pp. 76–87, Apr. 2006. [12] M. Gamer, H. W. Gödert, A. Keth, H.-G. Rill, and G. Vossel, “Electrodermal and phasic heart rate responses in the Guilty Actions Test: Comparing guilty examinees to informed and uninformed innocents,” Int. J. Psychophysiol., vol. 69, no. 1, pp. 61–68, Jul. 2008.

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[13] I. Mikhelson, S. Bakhtiari, T. Elmer, and A. Sahakian, “Remote sensing of heart rate and patterns of respiration on a stationary subject using 94 GHz millimeter wave interferometry,” IEEE Trans. Biomed. Eng., vol. 58, no. 6, pp. 1671–16771, Jun. 2011. [14] G. Ossberger, T. Buchegger, E. Schimback, A. Stelzer, and R. Weigel, “Non-invasive respiratory movement detection and monitoring of hidden humans using ultra wideband pulse radar,” in Proc. Int. Workshop Ultra Wideband Syst. Joint With Conf. on Ultrawideband Syst. Technol., 2004, pp. 395–399. [15] Y. Wu, Z. Xu, and J. Li, “Terahertz radar signal for heart and breath rate detection based on time-frequency analysis,” in Commun., Signal Processi., Syst., Q. Liang, W. Wang, J. Mu, J. Liang, B. Zhang, Y. Pi, and C. Zhao, Eds. New York, NY, USA: Springer, 2012, pp. 523–530. [16] D. T. Petkie, C. Benton, and E. Bryan, “Millimeter wave radar for remote measurement of vital signs,” in Proc. IEEE Radar Conf., 2009, pp. 1–3. [17] S. Takagi and M. Tagawa, “Predominance of right-handed spirals in the interaepidermal sweat ducts in man and the primates,” Jpn. J. Physiol., vol. 7, no. 2, pp. 113–118, Jun. 1957. [18] Y. Feldman, A. Puzenko, P. Ben Ishai, A. Caduff, and A. J. Agranat, “Human skin as arrays of helical antennas in the millimeter and submillimeter wave range,” Phys. Rev. Lett., vol. 100, no. 12, Mar. 2008, Art. ID 128102. [19] Y. Feldman, A. Puzenko, P. Ben Ishai, A. Caduff, I. Davidovich, F. Sakran, and A. Agranat, “The electromagnetic response of human skin in the millimetre and submillimetre wave range,” Phys. Med. Biol., vol. 54, no. 11, pp. 3341–3363, Jun. 2009. [20] E. Safrai, P. Ben Ishai, A. Caduff, A. Puzenko, A. Polsman, A. J. Agranat, and Y. Feldman, “The remote sensing of mental stress from the electromagnetic reflection coefficient of human skin in the sub-THz range,” Bioelectromagnetics, vol. 33, no. 5, pp. 375–382, Jul. 2012. [21] I. Hayut, A. Puzenko, P. Ben Ishai, A. Polsman, A. J. Agranat, and Y. Feldman, “The helical structure of sweat ducts: Their influence on the electromagnetic reflection spectrum of the skin,” IEEE Trans. THZ Sci. Technol., vol. 3, no. 2, pp. 207–215, 2013. [22] AB Millimtre MVNA-8-350, , Oct. 10, 2010 [Online]. Available: http:// www.abmillimetre.com/Products.htm [23] M. Bradley and Lang, “The International Affective Picture System (IAPS) in the study of emotion and attention,” in Handbook of Emotion Elicitation and Assessment. New York, NY, USA: Oxford Univ., 2007. [24] P. J. Lang et al., “International affective picture system (IAPS): Affective ratings of pictures and instruction manual,” NIMH, Center for the Study of Emotion & Attention, Univ. Florida, Gainesville, FL, USA, Tech. Rep. A-6, 2005. [25] D. E. Becker, “Fundamentals of electrocardiography interpretation,” Anesth. Prog., vol. 53, no. 2, pp. 53–64, 2006. [26] A. L. Goldberger, Clinical Electrocardiography: A Simplified Approach. New York, NY, USA: Elsevier Health Sciences, 2012. [27] I. Mozos and C. Serban, “The relation between QT interval and T-wave variables in hypertensive patients,” J. Pharmacy and Bioallied Sci., vol. 3, no. 3, p. 339, 2011. [28] P. M. Rautaharju, B. Surawicz, and L. S. Gettes, “AHA/ACCF/HRS recommendations for the standardization and interpretation of the electrocardiogrampart IV: The segment, and waves, and the interval a scientific statement from the American Heart Association electrocardiography and arrhythmias committee, Council on Clinical Cardiology; the American College of Cardiology Foundation; and the Heart Rhythm Society endorsed by the International Society for Computerized Electrocardiology,” J. Amer. Coll. Cardiol., vol. 53, no. 11, pp. 982–991, Mar. 2009. [29] MATLAB – The Language of Technical Computing, , Aug. 15, 2012 [Online]. Available: http://www.mathworks.com/products/matlab/ [30] R. N. Stiles, “Frequency and displacement amplitude relations for normal hand tremor,” J. Appl. Physiol., vol. 40, no. 1, pp. 44–54, Jan. 1976. Yael Kurzweil Segev received the B.Sc. degree in physics and chemistry and M.Sc. degree in electrooptics from The Hebrew University of Jerusalem, Jerusalem, Israel, where she is currently working toward the Ph.D. degree under the supervision of Prof. Feldman.

Moshe Brodsky received the B.S. degree in biomedical engineering from the Jerusalem College of Technology, Jerusalem, Israel, in 2008. He is currently

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working toward the M.Sc. degree in applied physics at The Hebrew University of Jerusalem, Jerusalem, Israel.

Alexander Polsman received his B.Sc. degree in Physics from Racah Institute of Physics, The Hebrew University of Jerusalem, in 2009. He is currently an M.Sc. student at the Department of Applied Physics, School of Computer Science and Engineering, The Hebrew University of Jerusalem. His research interests include microwave, millimeter- and submillimeter-wave technologies.

Yuri Feldman was born in Kazan, USSR, in 1951. He received the M.S. degree in radio physics and Ph.D. degree in molecular physics from the Kazan State University, Kazan, USSR, in 1973 and 1981, respectively. From 1973 to 1991, he was with the Laboratory of Molecular Biophysics, Kazan Institute of Biology, Academy of Science of the USSR. In 1991, he immigrated to Israel and, since 1991, he has been with The Hebrew University of Jerusalem, Jerusalem, Israel, where he is currently a Full Professor and the Head of the Dielectric Spectroscopy Laboratory. He also is a Director of the Center for Electromagnetic Research and Characterization (CERC) and since 2002 he has been a member of the International Dielectric Society Board. He has spent over 30 years in the field and has more than 200 scientific publications related to dielectric spectroscopy and its applications. He holds 8 patents in the areas of electromagnetic properties of the matter. His current interests include broadband dielectric spectroscopy in frequency and time domain; theory of dielectric polarization and relaxation; relaxation phenomena and strange kinetics in disordered materials; electromagnetic properties of biological systems in vitro and in vivo.

Sharon Einav-Bromiker received the M.D. degree from the Hadassah School of Medicine, Jerusalem, in 1991.

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She completed her internship in the Hadassah Hebrew University Medical Center, Jerusalem. She is a Senior Lecturer in Anesthesiology and Critical Care Medicine at the Hebrew University in Jerusalem and the Ben Gurion University in the Negev. In 1999, she joined the faculty of the Hadassah Hebrew University Medical Center as an Anesthesiologist and Critical Care physician. During her work at Hadassah, she also served as Medical Co-director of airborne critical care transport services and appointed as the physician on the personal entourages of Pope John Paul II and President Bill Clinton in Israel. She was also elected for a research fellowship on Epidemiology of cardiac arrest and resuscitation which she performed during the years 2002–2003.In 2005 Dr. Einav joined the faculty of the Shaare Zedek Medical Center where she was appointed director of Surgical critical care and Director of the hospital committee for resuscitation. She works full time in the Intensive Care Unit of the Shaare Zedek Medical center in Jerusalem. She serves as a consultant for medical device development for Zoll and Covidien. Her fields of research include the study of shock and cardiopulmonary resuscitation and the management of trauma patients, particularly during Multiple Casualty events. She has published on her subjects of interests in leading journals such as “Annals of Surgery”, “Critical Care”, and “Resuscitation” and recently received the American College of Surgeons award for Best Scientific Exhibit for some of her research on treatment of victims of multiple casualty events. She is currently involved in the writing of disaster management guidelines with the American College of Chest Physicians and in the writing of the upcoming guidelines for cardiopulmonary resuscitation within the American Heart Association.

Paul Ben Ishai received the Ph.D. degree from The Hebrew University of Jerusalem, Jerusalem, Israel, in 2009. He is currently Director of The Hebrew University’s Center for Electromagnetic Research and Characterization, situated in the Applied Physic Department. For the last 12 years, he has been involved with the laboratory of Prof. Feldman, concentrating on dielectric research. His research topics include soft condensed matter physics, glassy dynamics, biophysics, terahertz spectroscopy and dielectric spectroscopy. In 2004 he was part of the founding team investigating the interaction of the human sweat duct with sub terahertz electromagnetic radiation.