The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform
Riccardo Barbieri - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Zhe Chen, Emery N. Brown, Riccardo BarbieriAbstract:Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track Heart Beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic estimate of RSA gain. We use Kolmogorov-Smirnov tests and autocorrelation function analyses to assess model goodness-of-fit. We illustrate the properties of the new dynamic estimate of RSA in the analysis of simulated Heart Beat data and actual Heart Beat data recorded from subjects in a four-state postural study of Heart Beat dynamics: control, sympathetic blockade, parasympathetic blockade, and combined sympathetic and parasympathetic blockade. In addition to giving an accurate description of the Heart Beat data, our adaptive filter algorithm confirms established findings pointing at a vagally mediated RSA and provides a new dynamic RSA estimate that can be used to track cardiovascular control between and within a broad range of postural, pharmacological, and age conditions. Our paradigm suggests a possible framework for designing a device for ambulatory monitoring and assessment of autonomic control in both laboratory research and clinical practice.
-
A study of probabilistic models for characterizing human Heart Beat dynamics in autonomic blockade control
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Zhe Chen, Emery N. Brown, Riccardo BarbieriAbstract:In this paper, we compare and validate different probabilistic models of human Heart Beat intervals for assessment of the electrocardiogram data recorded with varying conditions in posture and pharmacological autonomic blockade. The models are validated using the adaptive point process filtering paradigm and Kolmogorov-Smirnov test. The inverse Gaussian model was found to achieve the overall best performance in the analysis of autonomic control. We further improve the model by incorporating the respiratory covariate measurements and present dynamic respiratory sinus arrhythmia (RSA) analysis. Our results suggest the instantaneous RSA gain computed from our proposed model as a potential index of vagal control dynamics.
Zhe Chen - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Zhe Chen, Emery N. Brown, Riccardo BarbieriAbstract:Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track Heart Beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic estimate of RSA gain. We use Kolmogorov-Smirnov tests and autocorrelation function analyses to assess model goodness-of-fit. We illustrate the properties of the new dynamic estimate of RSA in the analysis of simulated Heart Beat data and actual Heart Beat data recorded from subjects in a four-state postural study of Heart Beat dynamics: control, sympathetic blockade, parasympathetic blockade, and combined sympathetic and parasympathetic blockade. In addition to giving an accurate description of the Heart Beat data, our adaptive filter algorithm confirms established findings pointing at a vagally mediated RSA and provides a new dynamic RSA estimate that can be used to track cardiovascular control between and within a broad range of postural, pharmacological, and age conditions. Our paradigm suggests a possible framework for designing a device for ambulatory monitoring and assessment of autonomic control in both laboratory research and clinical practice.
-
A study of probabilistic models for characterizing human Heart Beat dynamics in autonomic blockade control
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Zhe Chen, Emery N. Brown, Riccardo BarbieriAbstract:In this paper, we compare and validate different probabilistic models of human Heart Beat intervals for assessment of the electrocardiogram data recorded with varying conditions in posture and pharmacological autonomic blockade. The models are validated using the adaptive point process filtering paradigm and Kolmogorov-Smirnov test. The inverse Gaussian model was found to achieve the overall best performance in the analysis of autonomic control. We further improve the model by incorporating the respiratory covariate measurements and present dynamic respiratory sinus arrhythmia (RSA) analysis. Our results suggest the instantaneous RSA gain computed from our proposed model as a potential index of vagal control dynamics.
Emery N. Brown - One of the best experts on this subject based on the ideXlab platform.
-
Assessment of Autonomic Control and Respiratory Sinus Arrhythmia Using Point Process Models of Human Heart Beat Dynamics
IEEE Transactions on Biomedical Engineering, 2009Co-Authors: Zhe Chen, Emery N. Brown, Riccardo BarbieriAbstract:Tracking the autonomic control and respiratory sinus arrhythmia (RSA) from electrocardiogram and respiratory measurements is an important problem in cardiovascular control. We propose a point process adaptive filter algorithm based on an inverse Gaussian model to track Heart Beat intervals that incorporates respiratory measurements as a covariate and provides an analytic form for computing a dynamic estimate of RSA gain. We use Kolmogorov-Smirnov tests and autocorrelation function analyses to assess model goodness-of-fit. We illustrate the properties of the new dynamic estimate of RSA in the analysis of simulated Heart Beat data and actual Heart Beat data recorded from subjects in a four-state postural study of Heart Beat dynamics: control, sympathetic blockade, parasympathetic blockade, and combined sympathetic and parasympathetic blockade. In addition to giving an accurate description of the Heart Beat data, our adaptive filter algorithm confirms established findings pointing at a vagally mediated RSA and provides a new dynamic RSA estimate that can be used to track cardiovascular control between and within a broad range of postural, pharmacological, and age conditions. Our paradigm suggests a possible framework for designing a device for ambulatory monitoring and assessment of autonomic control in both laboratory research and clinical practice.
-
A study of probabilistic models for characterizing human Heart Beat dynamics in autonomic blockade control
2008 IEEE International Conference on Acoustics Speech and Signal Processing, 2008Co-Authors: Zhe Chen, Emery N. Brown, Riccardo BarbieriAbstract:In this paper, we compare and validate different probabilistic models of human Heart Beat intervals for assessment of the electrocardiogram data recorded with varying conditions in posture and pharmacological autonomic blockade. The models are validated using the adaptive point process filtering paradigm and Kolmogorov-Smirnov test. The inverse Gaussian model was found to achieve the overall best performance in the analysis of autonomic control. We further improve the model by incorporating the respiratory covariate measurements and present dynamic respiratory sinus arrhythmia (RSA) analysis. Our results suggest the instantaneous RSA gain computed from our proposed model as a potential index of vagal control dynamics.
Chris I De Zeeuw - One of the best experts on this subject based on the ideXlab platform.
-
Enhancing Heart-Beat-Based Security for mHealth Applications
IEEE journal of biomedical and health informatics, 2015Co-Authors: Robert M Seepers, Christos Strydis, Ioannis Sourdis, Chris I De ZeeuwAbstract:In Heart-Beat-based security, a security key is derived from the time difference between two consecutive Heart Beats (the Inter-Pulse-Interval, IPI) which may, subsequently, be used to enable secure communication. While Heart-Beatbased security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects which should be considered in practice, including subjects with reduced Heart-rate variability, different sensor-sampling frequencies, inter-sensor variability (i.e., how accurate each entity may measure Heart Beats) as well as average and worst-caseauthentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, lessentropic keys may actually increase the key strength by reducing the effects of inter-sensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject's Heart-rate variability. To improve security, we introduce the Inter-multi-Pulse Interval (ImPI), a novel method of extracting entropy from the Heart by considering the time difference between two non-consecutive Heart Beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4x (+19.2 bits) for subjects with limited Heart-rate variability, at the cost of an extended key-generation time of 4.8x (+45 sec).
-
TrustCom/BigDataSE/ISPA (1) - On Using a Von Neumann Extractor in Heart-Beat-Based Security
2015Co-Authors: Robert M Seepers, Christos Strydis, Ioannis Sourdis, Chris I De ZeeuwAbstract:The Inter-Pulse-Interval (IPI) of Heart Beats has previously been suggested for facilitating security in mobile health (mHealth) applications. In Heart-Beat-based security, a security key is derived from the time difference between consecutive Heart Beats. As two entities that simultaneously sample the same Heart Beats may generate the same key (with some inter-key disparity), these keys may be used for various security functions, such as entity authentication or data confidentiality. One of the key limitations in Heart-Beat-based security is the low randomness intrinsic to the most-significant bits (MSBs) in the digital representation of each IPI. In this paper, we explore the use of a von Neumann entropy extractor on these MSBs in order to increase their randomness. We show that our von Neumann key-generator produces significantly more random bits than a non-extracting key generator with an average bit-extraction rate between 13.4% and 21.9%. Despite this increase in randomness, we also find a substantial increase in inter-key disparity, increasing the mismatch tolerance required for a given true-key pair. Accordingly, the maximum-attainable effective key-strength of our key generator is only slightly higher than that of a non-extracting generator (16.4 bits compared to 15.2 bits of security for a 60-bit key), while the former requires an increase in average key-generation time of 2.5x.
-
peak misdetection in Heart Beat based security characterization and tolerance
International Conference of the IEEE Engineering in Medicine and Biology Society, 2014Co-Authors: Robert M Seepers, Christos Strydis, Pedro Perislopez, Ioannis Sourdis, Chris I De ZeeuwAbstract:The Inter-Pulse-Interval (IPI) of Heart Beats has previously been suggested for security in mobile health (mHealth) applications. In IPI-based security, secure communication is facilitated through a security key derived from the time difference between Heart Beats. However, there currently exists no work which considers the effect on security of imperfect Heart-Beat (peak) detection. This is a crucial aspect of IPI-based security and likely to happen in a real system. In this paper, we evaluate the effects of peak misdetection on the security performance of IPI-based security. It is shown that even with a high peak detection rate between 99.9% and 99.0%, a significant drop in security performance may be observed (between -70% and -303%) compared to having perfect peak detection. We show that authenticating using smaller keys yields both stronger keys as well as potentially faster authentication in case of imperfect Heart Beat detection. Finally, we present an algorithm which tolerates the effect of a single misdetected peak and increases the security performance by up to 155%.
-
EMBC - Peak misdetection in Heart-Beat-based security: Characterization and tolerance.
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Inte, 2014Co-Authors: Robert M Seepers, Christos Strydis, Ioannis Sourdis, Pedro Peris-lopez, Chris I De ZeeuwAbstract:The Inter-Pulse-Interval (IPI) of Heart Beats has previously been suggested for security in mobile health (mHealth) applications. In IPI-based security, secure communication is facilitated through a security key derived from the time difference between Heart Beats. However, there currently exists no work which considers the effect on security of imperfect Heart-Beat (peak) detection. This is a crucial aspect of IPI-based security and likely to happen in a real system. In this paper, we evaluate the effects of peak misdetection on the security performance of IPI-based security. It is shown that even with a high peak detection rate between 99.9% and 99.0%, a significant drop in security performance may be observed (between -70% and -303%) compared to having perfect peak detection. We show that authenticating using smaller keys yields both stronger keys as well as potentially faster authentication in case of imperfect Heart Beat detection. Finally, we present an algorithm which tolerates the effect of a single misdetected peak and increases the security performance by up to 155%.
Peter D. Larsen - One of the best experts on this subject based on the ideXlab platform.
-
Coupling of spontaneous ventilation to Heart Beat during benzodiazepine sedation
British journal of anaesthesia, 1997Co-Authors: Duncan C. Galletly, Peter D. LarsenAbstract:We have examined in eight male volunteers the effect of midazolam sedation on the relationship between timing of spontaneous ventilations and Heart Beat. On 2 study days, subjects received either midazolam 0.1 mg kg-1 or saline placebo while recordings were made of ECG and ventilatory impedance pneumography. We observed in all midazolam-treated subjects clear evidence of synchronous or non-synchronous coupling between ventilation and Heart Beat. Non-synchronous coupling was seen in two placebo-treated subjects, one of whom had slept during the recording period. We conclude that cardioventilatory coupling is a major determinant of ventilatory timing in sedated subjects.
