Publications
164 results found
Aguilar-Pelaez E, Chen G, Rodriguez-Villegas E, 2012, Technique for interference reduction in battery powered physiological monitoring devices
Hizon JR, 2012, A Compact Linearly Tunable Low Voltage Triode OTA Using Self-Cascodes, IEEE International Symposium on Circuits and Systems
Logesparan L, Casson AJ, Rodriguez-Villegas E, 2011, Performance metrics for characterization of a seizure detection algorithm for offline and online use, 5th International Workshop on Seizure Prediction
Purpose: To select appropriate previously reported performance metrics to evaluate a new seizure detection algorithm for offline and online analysis, and thus quantify any performance variation between these metrics.Methods: Traditional offline algorithms mark out any EEG section (epoch) of a seizure (event), so that neurologists only analyze the detected and adjacent sections. Thus, offline algorithms could be evaluated using number of correctly detected events, or event-based sensitivity (SEVENT), and epoch-based specificity (percentage of incorrectly detected background epochs). In contrast, online seizure detection (especially, data selection) algorithms select for transmission only the detected EEG sections and hence need to detect the entire duration of a seizure. Thus, online algorithms could be evaluated using percentage of correctly detected seizure duration, or epoch-based sensitivity (SEPOCH), and epoch-based specificity. Here, a new seizure detection algorithm is evaluated using the selected performance metrics for epoch duration ranging from 1s to 60s.Results: For 1s epochs, the area under the event-based sensitivity-specificity curve was 0.95 whilst SEPOCH achieves 0.81. This difference is not surprising, as intuitively, detecting any epoch within a seizure is easier than detecting every epoch - especially as seizures evolve over time. For longer epochs of 30s or 60s, SEVENT falls to 0.84 and 0.82 respectively and SEPOCH reduces to 0.76. Here, decreased SEVENT shows that fewer seizures are detected, possibly due to easy-to-detect short seizure sections being masked by surrounding EEG. However, detecting one long epoch constitutes a larger percentage of a seizure than a shorter one and thus SEPOCH does not decrease proportionately.Conclusions: Traditional offline and online seizure detection algorithms require different metrics to effectively evaluate their performance for their respective applications. Using such metrics, it has been shown that a decre
Logesparan L, Casson AJ, Rodriguez-Villegas E, 2011, Assessing the impact of signal normalization: Preliminary results on epileptic seizure detection, Alex Casson, Publisher: IEEE, Pages: 1439-1442
Signal normalization is an essential part of patient independent algorithms, for example to correct for variations in signal amplitude from different parts of the body, prior to applying a fixed threshold for event detection. Multiple methods for providing the required normalization are available. This paper presents a systematic investigation into the effects of five different methods using epileptic seizure detection from the EEG as an illustration case. It is found that, whilst normalization is essential, four of the considered methods actually decrease the ability to detect seizures, counteracting the algorithm aim. For optimal detection performance the effects of the signal normalization illustrated here should be incorporated into future algorithm designs.
Casson AJ, Rodriguez-Villegas E, 2011, Utilising noise to improve an interictal spike detector, J. Neurosci. Methods, Vol: 201, Pages: 262-268
Dithering is the process of intentionally adding artificially generated noise to an otherwise uncorrupted signal to actually improve the performance of an end overall system. This article demonstrates that a dithering procedure can be used to improve the performance of an EEG interictal spike detection algorithm. Using a previously reported algorithm, by adding varying amounts of artificially generated noise to the input EEG signals the effect on the algorithm detection performance is investigated. A new stochastic resonance result is found whereby the spike detection performance improves by up to 4.3% when small amounts of corrupting noise, below 20 $\mu$VRMS, are added to the input data. This result is of use for improving the detection performance of algorithms, and the result also affects the dynamic range required for the hardware implementation of such algorithms in low power, portable EEG systems.
Casson AJ, Rodriguez-Villegas E, 2011, Interfacing biology and circuits: quantification and performance metrics, Integrated Bio-Microsystems, Editors: Iniewski, Publisher: Wiley, Pages: 1-33, ISBN: 9780470641903
Rodriguez-Villegas E, Casson AJ, Corbishley P, 2011, A Subhertz Nanopower Low-Pass Filter, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, Vol: 58, Pages: 351-355, ISSN: 1549-7747
- Author Web Link
- Open Access Link
- Cite
- Citations: 36
Casson AJ, Rodriguez-Villegas E, 2011, "A review and modern approach to LC ladder synthesis,", Journal of Low Power Electronics and Applications, Vol: 1, Pages: 20-44
Casson AJ, Rodriguez-Villegas E, 2011, Algorithms and circuits for truly wearable physiological monitoring, 33rd international conference of the IEEE Engineering in Medicine and Biology Society
Casson AJ, Rodriguez-Villegas E, 2011, A review and modern approach to LC ladder synthesis, Journal of Low Power Electronics and Applications, Vol: 1, Pages: 20-44
Ultra low power circuits require robust and reliable operation despite the unavoidable use of low currents and the weak inversion transistor operation region. For analogue domain filtering doubly terminated LC ladder based filter topologies are thus highly desirable as they have very low sensitivities to component values: non-exact component values have a minimal effect on the realised transfer function. However, not all transfer functions are suitable for implementation via a LC ladder prototype, and even when the transfer function is suitable the synthesis procedure is not trivial. The modern circuit designer can thus benefit from an updated treatment of this synthesis procedure. This paper presents a methodology for the design of doubly terminated LC ladder structures making use of the symbolic maths engines in programs such as MATLAB and MAPLE. The methodology is explained through the detailed synthesis of an example 7th order bandpass filter transfer function for use in electroencephalogram (EEG) analysis.
Casson AJ, Rodriguez-Villegas E, 2011, A 60 pW gmC Continuous Wavelet Transform Circuit for Portable EEG Systems, IEEE Journal of Solid-State Circuits, Vol: 46, Pages: 1406-1415
This paper presents a low power, low voltage and low frequency bandpass filter implementation of a continuous wavelet transform (CWT) for use with physiological signals in the electroencephalogram (EEG) range (1–150 $mu$V, 1–70 Hz bandwidth). Experimental results are presented for a 1 V, 7th order g$_{m}$ C filter based CWT with filter center frequencies ranging from 1 to 64 Hz.
Logesparan L, Rodriguez-Villegas E, 2011, A novel phase congruency based algorithm for online data reduction in ambulatory EEG systems, IEEE Transactions on Biomedical Engineering, Vol: 58, Pages: 2825-2834, ISSN: 0018-9294
Abstract—Real signals are often corrupted by noise with a power spectrum variable over time. In applications involving these signals, it is expected that dynamically estimating and correcting for this noise would increase the amount of useful information extracted from the signal. One such application is scalp EEG monitoring in epilepsy, where electrical activity generated by cranio-facial muscles obscure the measured brainwaves. This paper presents a data selection algorithm based on phase congruency to identify interictal spikes from background EEG; together with a novel statistical method that allows a more comprehensive trade-off based quantitative comparison of two algorithms which have been tested at a fixed threshold in the same database. Here, traditional phase congruency has been modified to incorporate a dynamic estimate of muscle activity present in the input scalp EEG signal. The proposed algorithmachieves 50% data reduction whilst detecting more than 80% of interictal spikes. This represents a significant improvement overthe state-of-the-art denoising method for phase congruency.
Logesparan L, Rodriguez-Villegas E, 2010, Improving phase congruency for EEG data reduction., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 642-645, ISSN: 1557-170X
Real signals are often corrupted by noise. In applications where the noise power spectrum is variable with time, dynamic noise estimation and compensation can potentially improve the performance of signal processing algorithms. One such application is scalp EEG monitoring in epilepsy, where the electrical activity generated by cranio-facial muscle contraction and expansion, often obscures the measured brainwave signals. This work presents a data reduction algorithm which is based on differentiating interictal from normal background activity, in epileptic scalp EEG signals, using a modified phase congruency technique. The modification is based on dynamically estimating muscle activity from the signal and incorporating this estimation in phase congruency computations. The proposed algorithm identifies 90%of interictal spikes whilst transmitting only 45% of EEG data. This is in the order of 15% improvement in data reduction when compared to the performance obtained with the state-of-the-art denoised phase congruency-which calculates a constant noise threshold-applied to the same dataset.
Chen G, Rodriguez-Villegas E, 2010, System-level design trade-offs for truly wearable wireless medical devices., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 1441-1444, ISSN: 1557-170X
Power and current management in emerging wearable medical devices, intended to continuously monitor physiological signals, are crucial design issues. The overall size of the electronic part of these systems is generally going to be dominated by the size of the batteries. Unfortunately, the options of smaller batteries do not only come at the expense of a lower capacity and hence shorter operation time. It also significantly constrains the amount of available current that can be used by different electronic blocks, as well as their operating power supply voltage. This paper discusses all the typical power and current management system level issues in the design of a typical miniature wearable wireless medical device. The discussion is illustrated with experimental results obtained with two devices built using two of the currently most popular low power commercial transceivers in the market, the Texas Instruments (TI) CC2500 and the Nordic Semiconductor nRF24L01+. The numbers presented can be used as a more realistic guidance of the energy per bit required in a real system implementation, as opposed to the ideal figures normally quoted by the manufacturers. Furthermore the analysis in this paper can also be extrapolated to the design of future wireless monitoring wearable devices with further optimized radio transceivers.
Abdulghani AM, Rodriguez-Villegas E, 2010, Compressive sensing: from "compressing while sampling" to "compressing and securing while sampling"., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 1127-1130, ISSN: 1557-170X
In a traditional signal processing system sampling is carried out at a frequency which is at least twice the highest frequency component found in the signal. This is in order to guarantee that complete signal recovery is later on possible. The sampled signal can subsequently be subjected to further processing leading to, for example, encryption and compression. This processing can be computationally intensive and, in the case of battery operated systems, unpractically power hungry. Compressive sensing has recently emerged as a new signal sampling paradigm gaining huge attention from the research community. According to this theory it can potentially be possible to sample certain signals at a lower than Nyquist rate without jeopardizing signal recovery. In practical terms this may provide multi-pronged solutions to reduce some systems computational complexity. In this work, information theoretic analysis of real EEG signals is presented that shows the additional benefits of compressive sensing in preserving data privacy. Through this it can then be established generally that compressive sensing not only compresses but also secures while sampling.
Casson AJ, Rodriguez Villegas E, 2010, Low power signal processing electronics for wearable medical devices, 32nd international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 3439-3440
Custom designed microchips, known as Application Specific Integrated Circuits (ASICs), offer the lowest possible power consumption electronics. However, this comes at the cost of a longer, more complex and more costly design process compared to one using generic, off-the-shelf components. Nevertheless, their use is essential in future truly wearable medical devices that must operate for long periods of time from physically small, energy limited batteries. This presentation will demonstrate the state-of-the-art in ASIC technology for providing online signal processing for use in these wearable medical devices.
Casson AJ, Logesparan L, Rodriguez-Villegas E, 2010, An introduction to future truly wearable medical devices--from application to ASIC., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 3430-3431, ISSN: 1557-170X
This talk will provide an introduction to the "Towards future truly wearable medical devices: from application to ASIC" mini-symposium. For user comfort and acceptance long term physiological sensors must be discrete, comfortable and easy to use. These requirements place stringent limits on all aspects of the system design: from the overall application aim, to power generation issues, to low power electronic design techniques. For successful devices design issues in all of these areas must be solved simultaneously. The work here presents an overview and introduction to these topics.
Abdulghani AM, Rodriguez Villegas E, 2010, Compressive sensing: From "compressing while sampling" to 'compressing and securing while sampling, 32nd Annual International Conference of the IEEE EMBS, Publisher: IEEE, Pages: 1127-1130, ISSN: 1557-170X
In a traditional signal processing system sampling is carried out at a frequency which is at least twice the highest frequency component found in the signal. This is in order to guarantee that complete signal recovery is later on possible. The sampled signal can subsequently be subjected to further processing leading to, for example, encryption and compression. This processing can be computationally intensive and, in the case of battery operated systems, unpractically power hungry. Compressive sensing has recently emerged as a new signal sampling paradigm gaining huge attention from the research community. According to this theory it can potentially be possible to sample certain signals at a lower than Nyquist rate without jeopardizing signal recovery. In practical terms this may provide multi-pronged solutions to reduce some systems computational complexity. In this work, information theoretic analysis of real EEG signals is presented that shows the additional benefits of compressive sensing in preserving data privacy. Through this it can then be established generally that compressive sensing not only compresses but also secures while sampling.
Logesparan L, Rodriguez Villegas E, 2010, Improving phase congruency for EEG data reduction, 32nd Annual International Conference of the IEEE EMBS, Publisher: IEEE, Pages: 642-645, ISSN: 1557-170X
Real signals are often corrupted by noise. In applications where the noise power spectrum is variable with time, dynamic noise estimation and compensation can potentially improve the performance of signal processing algorithms. One such application is scalp EEG monitoring in epilepsy, where the electrical activity generated by cranio-facial muscle contraction and expansion, often obscures the measured brainwave signals. This work presents a data reduction algorithm which is based on differentiating interictal from normal background activity, in epileptic scalp EEG signals, using a modified phase congruency technique. The modification is based on dynamically estimating muscle activity from the signal and incorporating this estimation in phase congruency computations. The proposed algorithm identifies 90%of interictal spikes whilst transmitting only 45% of EEG data. This is in the order of 15% improvement in data reduction when compared to the performance obtained with the state–of–the–art denoised phase congruency—which calculates a constant noise threshold—applied to the same dataset.
Casson AJ, Rodriguez-Villegas E, 2010, Standard filter approximations for low power Continuous Wavelet Transforms., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 646-649, ISSN: 1557-170X
Analogue domain implementations of the Continuous Wavelet Transform (CWT) have proved popular in recent years as they can be implemented at very low power consumption levels. This is essential for use in wearable, long term physiological monitoring systems. Present analogue CWT implementations rely on taking mathematical a approximation of the wanted mother wavelet function to give a filter transfer function that is suitable for circuit implementation. This paper investigates the use of standard filter approximations (Butterworth, Chebyshev, Bessel) as an alternative wavelet approximation technique. This extends the number of approximation techniques available for generating analogue CWT filters. An example ECG analysis shows that signal information can be successfully extracted using these CWT approximations.
Casson AJ, Rodriguez-Villegas E, 2010, Low power signal processing electronics for wearable medical devices., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 3439-3440, ISSN: 1557-170X
Custom designed microchips, known as Application Specific Integrated Circuits (ASICs), offer the lowest possible power consumption electronics. However, this comes at the cost of a longer, more complex and more costly design process compared to one using generic, off-the-shelf components. Nevertheless, their use is essential in future truly wearable medical devices that must operate for long periods of time from physically small, energy limited batteries. This presentation will demonstrate the state-of-the-art in ASIC technology for providing online signal processing for use in these wearable medical devices.
Casson AJ, Rodriguez Villegas E, 2010, Standard filter approximations for low power Continuous Wavelet Transforms, 32nd international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 646-649
Analogue domain implementations of the Continuous Wavelet Transform (CWT) have proved popular in recent years as they can be implemented at very low power consumption levels. This is essential for use in wearable, long term physiological monitoring systems. Present analogue CWT implementations rely on taking mathematical a approximation of the wanted mother wavelet function to give a filter transfer function that is suitable for circuit implementation. This paper investigates the use of standard filter approximations (Butterworth, Chebyshev, Bessel) as an alternative wavelet approximation technique. This extends the number of approximation techniques available for generating analogue CWT filters. An example ECG analysis shows that signal information can be successfully extracted using these CWT approximations.
Logesparan L, Rodriguez-Villegas E, 2010, Improving phase congruency for EEG data reduction, 32nd International Conference of the IEEE Engineering in Medicine and Biology Society, Publisher: IEEE, Pages: 642-645
Real signals are often corrupted by noise. In applications where the noise power spectrum is variable with time, dynamic noise estimation and compensation can potentially improve the performance of signal processing algorithms. One such application is scalp EEG monitoring in epilepsy, where the electrical activity generated by cranio-facial muscle contraction and expansion, often obscures the measured brainwave signals. This work presents a data reduction algorithm which is based on differentiating interictal from normal background activity, in epileptic scalp EEG signals, using a modified phase congruency technique. The modification is based on dynamically estimating muscle activity from the signal and incorporating this estimation in phase congruency computations. The proposed algorithm identifies 90%of interictal spikes whilst transmitting only 45% of EEG data. This is in the order of 15% improvement in data reduction when compared to the performance obtained with the state-of-the-art denoised phase congruency-which calculates a constant noise threshold-applied to the same dataset.
Casson AJ, Logesparan L, Rodriguez Villegas E, 2010, An introduction to future truly wearable medical devices—from application to ASIC, 32nd international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 3430-3431
This talk will provide an introduction to the "Towards future truly wearable medical devices: from application to ASIC" mini-symposium. For user comfort and acceptance long term physiological sensors must be discrete, comfortable and easy to use. These requirements place stringent limits on all aspects of the system design: from the overall application aim, to power generation issues, to low power electronic design techniques. For successful devices design issues in all of these areas must be solved simultaneously. The work here presents an overview and introduction to these topics.
Chen G, Rodriguez Villegas E, 2010, System-level design trade-offs for truly wearable wireless medical devices, The Engineering in Medicine and Biology Conference (EMBC)
Chen G, Rodriguez-Villegas E, 2010, System-Level Design Trade-offs for Truly Wearable Wireless Medical Devices, 2010 ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC), Pages: 1441-1444, ISSN: 1557-170X
- Author Web Link
- Cite
- Citations: 10
Abdulghani, CASSON, RODRIGUEZ VILLEGAS E, 2010, Quantifying the performance of compressive sensing on scalp EEG signals, 3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies (ISABEL)
Compressive sensing is a new data compression paradigm that has shown significant promise in fields such as MRI. However, the practical performance of the theory very much depends on the characteristics of the signal being sensed. As such the utility of the technique cannot be extrapolated from one application to another. Electroencephalography (EEG) is a fundamental tool for the investigation of many neurological disorders and is increasingly also used in many non-medical applications, such as Brain-Computer Interfaces. This paper characterises in detail the practical performance of different implementations of the compressive sensing theory when applied to scalp EEG signals for the first time. The results are of particular interest for wearable EEG communication systems requiring low power, real-time compression of the EEG data.
Casson AJ, Yates DC, Smith SJM, et al., 2010, Wearable Electroencephalography, IEEE Engineering in Medicine and Biology Magazine, Vol: 29, Pages: 44-56
The electroencephalogram (EEG) is a classic noninvasive method for measuring a person's brainwaves and is used in a large number of fields: from epilepsy and sleep disorder diagnosis to brain–computer interfaces (BCIs). Electrodes are placed on the scalp to detect the microvolt-sized signals that result from synchronized neuronal activity within the brain. Current long-term EEG monitoring is generally either carried out as an inpatient in combination with video recording and long cables to an amplifier and recording unit or is ambulatory. In the latter, the EEG recorder is portable but bulky, and in principle, the subject can go about their normal daily life during the recording.
Rodriguez-Villegas E, 2009, A Low-Power Wide-Range I-V Converter for Amperometric Sensing Applications, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 3, Pages: 432-436, ISSN: 1932-4545
- Author Web Link
- Cite
- Citations: 3
Casson AJ, Rodriguez-Villegas E, 2009, Toward Online Data Reduction for Portable Electroencephalography Systems in Epilepsy, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 56, Pages: 2816-2825, ISSN: 0018-9294
- Author Web Link
- Open Access Link
- Cite
- Citations: 36
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.