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ABSTRACT: Vagus nerve stimulation (VNS) is a therapeutic option for individuals with refractory epilepsy. Individuals with refractory epilepsy are prone to dysfunction of the autonomic nervous system. Reduced heart rate variability is a marker of dysfunction of the autonomic nervous system. Our goal was to study heart rate variability in children with refractory epilepsy and the influence of VNS on this parameter.
In 17 children (13 male; four female; mean age 7 y 6 mo; age range 3-16 y) with refractory epilepsy, electroencephalographic and electrocardiographic data were obtained before and after implantation of VNS during stage 2 and slow-wave sleep. Time and frequency domain parameters were calculated and the results were compared with an age- and sex-matched group of individuals without refractory epilepsy.
Our results show that autonomic cardiac control is affected in individuals with refractory epilepsy. There is a striking reduction in vagal tone during slow-wave sleep and modulation capacity is smaller than in individuals without refractory epilepsy. Implantation of VNS induces a shift in sympathovagal balance towards sympathetic predominance and an improvement in autonomic modulation.
Heart rate variability is affected in children with refractory epilepsy, and changes after implantation of VNS. The observed changes could be of importance in the cardiac complications of individuals with epilepsy and should be explored in more detail.
Developmental Medicine & Child Neurology 08/2011; 53(12):1143-9. · 2.92 Impact Factor
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ABSTRACT: Intelligent affective computers can have many medical and non-medical applications. However today's affective computers are limited in scope by their transferability to other application environments or that they monitor only one aspect of physiological emotion expression. Here, the use of a wireless EEG system, which can be implemented in a body area network, is used to investigate the potential of monitoring emotional valence in EEG, for application in real-life situations. The results show 82% accuracy for automatic classification of positive, negative and neutral valence based on film clip viewing, using features containing information on both the frequency content of the EEG and how this changes over time.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:2188-91.
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ABSTRACT: DEEP brain stimulation implants have improved life quality for more than 70,000 patients world-wide with diseases like Parkinson's, essential tremor, or obsessive-compulsive disorder where pharmaceutical therapies alone could not offer sufficient relief. Still, optimization and monitoring relies heavily on regular clinical visits, putting a burden on patient's comfort and clinicians. Permanent monitoring and combination with other patient health signals could ultimately lead to a personalized closed-loop therapy with remote quality monitoring. This requires technological improvements on the DBS implants such as integration of recording capabilities for brain activity monitoring, active low-power electronics, rechargeable battery technology, and body sensor networks for integration with e.g. gait, speech, and other vital information sensors on the patient's body and a link to a telemedicine platform using mobile technologies.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:1556-8.
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ABSTRACT: Early mental stress detection can prevent many stress related health problems. This study aimed at using a wearable sensor system to measure physiological signals and detect mental stress. Three different stress conditions were presented to a healthy subject group. During the procedure, ECG, respiration, skin conductance, and EMG of the trapezius muscles were recorded. In total, 19 physiological features were calculated from these signals. After normalization of the feature values and analysis of correlations among these features, a subset of 9 features was selected for further analysis. Principal component analysis reduced these 9 features to 7 principal components (PCs). Using these PCs and different classifiers, a consistent classification accuracy between stress and non stress conditions of almost 80% was found. This suggests that a promising feature subset was found for future development of a personalized stress monitor.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 08/2011; 2011:1798-801.
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Georgios Selimis,
Li Huang,
Fabien Massé,
Ioanna Tsekoura,
Maryam Ashouei,
Francky Catthoor,
Jos Huisken,
Jan Stuyt,
Guido Dolmans, Julien Penders,
Harmke De Groot
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ABSTRACT: In order for wireless body area networks to meet widespread adoption, a number of security implications must be explored to promote and maintain fundamental medical ethical principles and social expectations. As a result, integration of security functionality to sensor nodes is required. Integrating security functionality to a wireless sensor node increases the size of the stored software program in program memory, the required time that the sensor's microprocessor needs to process the data and the wireless network traffic which is exchanged among sensors. This security overhead has dominant impact on the energy dissipation which is strongly related to the lifetime of the sensor, a critical aspect in wireless sensor network (WSN) technology. Strict definition of the security functionality, complete hardware model (microprocessor and radio), WBAN topology and the structure of the medium access control (MAC) frame are required for an accurate estimation of the energy that security introduces into the WBAN. In this work, we define a lightweight security scheme for WBAN, we estimate the additional energy consumption that the security scheme introduces to WBAN based on commercial available off-the-shelf hardware components (microprocessor and radio), the network topology and the MAC frame. Furthermore, we propose a new microcontroller design in order to reduce the energy consumption of the system. Experimental results and comparisons with other works are given.
Journal of Medical Systems 03/2011; 35(5):1289-98. · 1.13 Impact Factor
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Human-Computer Interaction. Interaction Techniques and Environments - 14th International Conference, HCI International 2011, Orlando, FL, USA, July 9-14, 2011, Proceedings, Part II; 01/2011
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Proceedings of Wireless Health 2011, WH 2011, San Diego/La Jolla, CA, USA, October 10-13, 2011; 01/2011
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Proceedings of Wireless Health 2011, WH 2011, San Diego/La Jolla, CA, USA, October 10-13, 2011; 01/2011
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ABSTRACT: The cost of health care in first-world countries is increasing dramatically as a result of advances in medicine, a population that is becoming older and an increasingly unhealthy lifestyle. Personal health care concepts where sensors within and around the body monitor and measure all kind of physiological signals can be an addition to medicare with high benefits. This concept allows patients to stay in their home environment and hence have a better quality of life with lower costs involved. For these reasons research and development is ongoing on many body worn and implantable sensor nodes. In this paper it is shown that application knowledge and understanding the contribution of different components to the system power consumption is the best starting point to make optimal trade-offs in the system design. This will minimize the overall power consumption of a sensor node without losing track of the major functionality needed. Besides the importance of system optimization, it is also shown that new components and circuit techniques need to be developed to achieve orders of magnitude increase in energy efficiency. This is a must to realize ultra-thin electrocardiogram patches as well as more demanding nodes with a small form factor like real-time Electro Encephalogram processing for brain computer interaction or neuro-implants.
14th Euromicro Conference on Digital System Design, Architectures, Methods and Tools, DSD 2011, August 31 - September 2, 2011, Oulu, Finland; 01/2011
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ABSTRACT: Many national health services struggle in the face of financial resource constraints and shortages of skilled labor. The cost
of healthcare delivery is steadily on an upward trend. US health care spending is estimated at approximately 16% of the GDP
[1]. This upward trend is expected to continue, with projections that the healthcare share of the GDP reaches 19.5% by 2017.
Health care spending in other OECD countries is projected to consume up to 16% of GDP. As a result, the pressure on healthcare
systems to step up efforts in cost containment and efficiency improvement keeps growing. Consensus about the main determinants
of expenditure is not complete but revolves generally around cost drivers such as rising income and patient expectations;
demographic change, in particular the aging of population; and new technologies.
12/2010: pages 279-302;
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Proceedings of Wireless Health 2010, WH 2010, San Diego, CA, USA, October 5-7, 2010; 01/2010
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International Conference on Body Sensor Networks, BSN 2010, Singapore, 7-9 June, 2010; 01/2010
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ABSTRACT: Heart rate monitoring has been a significant topic of interest in the areas of healthcare, sports and gaming. Compared to locations such as the neck, ear, or chest, the wrist is a convenient measurement point, as the measurement technology can be integrated into a wristwatch. However, key technical challenges exist, namely a small physiological SNR and large disturbances due to motion artifact. This paper reports early results on a packaging concept to monitor the heartrate during rest and motion using off-the-shelf piezoelectric PVDF film sensors. Evaluation has shown good results at rest and unsatisfactory results during motion. Results from this investigation will nonetheless be used as input for the development of a wrist-based heartrate monitor which could function during activities such as running, walking or typing on a keyboard.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4407-10.
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ABSTRACT: Micro- and nano-technology has enabled development of smaller and smarter wearable devices for medical and lifestyle related applications. In particular, recent advances in EEG monitoring technologies pave the way for wearable, wireless EEG monitoring devices. Here, a low-power wireless EEG sensor platform that measures 8-channels of EEG, is described. The platform is integrated into a wearable headset for ambulatory monitoring of EEG. While using standard EEG electrodes without conductive gel, a first evaluation shows the wireless headset is comparable to the reference system when looking at alpha wave discrimination. This device combines low-noise, and low-power functionality into an easy-to-use wireless headset, providing a first step towards a fully integrated, fully functional wearable wireless EEG monitoring system.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2010; 2010:4197-200.
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Proceedings of Wireless Health 2010, WH 2010, San Diego, CA, USA, October 5-7, 2010; 01/2010
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Sixth International Workshop on Wearable and Implantable Body Sensor Networks, BSN 2009, Berkeley, CA, USA, 3-5 June 2009; 01/2009
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Foundations of Augmented Cognition. Neuroergonomics and Operational Neuroscience, 5th International Conference, FAC 2009 Held as Part of HCI International 2009 San Diego, CA, USA, July 19-24, 2009, Proceedings; 01/2009
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ABSTRACT: Robust beat detection under noisy conditions is required in order to obtain a correct clinical interpretation of the ECG in ambulatory settings. This paper describes the evaluation and optimization of a beat detection algorithm that is robust against high levels of noise. An evaluation protocol is defined in order to study four different characteristics of the algorithm: non-rhythmic patterns, different levels of SNR, exact peak detection and different levels of physical activity. This protocol is based on the MIT/BIH arrhythmia database and additional ECG recordings obtained under different levels of physical activity measured by 2-axis accelerometers. The optimized algorithm obtained a Se=99.65% and +P=99.79% on the MIT/BIH arrhythmia database while keeping a good performance on ECGs with high levels of activity (overall of Se=99.86%, +P=99.91%). In addition, this method was optimized to work in real time, for future implementation in a Wireless ECG sensor based on a microprocessor.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:950-3.
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Li Huang,
Maryam Ashouei,
Refet Firat Yazicioglu, Julien Penders,
Ruud J. M. Vullers,
Guido Dolmans,
Patrick Merken,
Jos Huisken,
Harmke de Groot,
Chris Van Hoof,
Bert Gyselinckx
JDCTA. 01/2009; 3:136-148.
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ABSTRACT: A body area network (BAN) for monitoring the autonomic nervous system responses is reported. The BAN is based on the Human++ UniNode, a small, low power generic wireless sensor node. Physiological signals are monitored using specifically designed ultra low power sensor front ends connected to the UniNodes. Two UniNodes compose the body area network, one on a chest belt to record ECG and respiration, the other on a wrist sensor to record skin conductance and skin temperature. Small, lightweight and low power body area network platform, this platform BAN platform paves the way towards ambulatory, continuous monitoring of autonomic responses in everyday applications.
3rd International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2009, London, UK, April 1-3, 2009; 01/2009
