Publications
164 results found
Chen G, Imtiaz SA, Aguilar-Pelaez E, et al., 2015, Algorithm for heart rate extraction in a novel wearable acoustic sensor., Healthcare Technology Letters, Vol: 2, Pages: 28-33, ISSN: 2053-3713
Phonocardiography is a widely used method of listening to the heart sounds and indicating the presence of cardiac abnormalities. Each heart cycle consists of two major sounds - S1 and S2 - that can be used to determine the heart rate. The conventional method of acoustic signal acquisition involves placing the sound sensor at the chest where this sound is most audible. Presented is a novel algorithm for the detection of S1 and S2 heart sounds and the use of them to extract the heart rate from signals acquired by a small sensor placed at the neck. This algorithm achieves an accuracy of 90.73 and 90.69%, with respect to heart rate value provided by two commercial devices, evaluated on more than 38 h of data acquired from ten different subjects during sleep in a pilot clinical study. This is the largest dataset for acoustic heart sound classification and heart rate extraction in the literature to date. The algorithm in this study used signals from a sensor designed to monitor breathing. This shows that the same sensor and signal can be used to monitor both breathing and heart rate, making it highly useful for long-term wearable vital signs monitoring.
Saraswat R, Abbasi MU, Rodriguez-Villegas E, 2015, Ultra-Low Power, Low Noise MAC Protocol Compliant Non-PLL based Wearable Transmitter for Dynamic Open Spectrum Sharing, 7th Annual International IEEE EMBS Conference on Neural Engineering (NER), Publisher: IEEE, Pages: 553-556, ISSN: 1948-3546
Abbasi MU, Raikos G, Saraswat R, et al., 2015, A high PSRR, ultra-low power 1.2V curvature corrected Bandgap Reference for Wearable EEG application, 13th IEEE International NEW Circuits and Systems Conference, Publisher: IEEE, ISSN: 2472-467X
Imtiaz SA, Rodriguez-Villegas E, 2014, A low computational cost algorithm for REM sleep detection using single channel EEG., Ann Biomed Eng, Vol: 42, Pages: 2344-2359
The push towards low-power and wearable sleep systems requires using minimum number of recording channels to enhance battery life, keep processing load small and be more comfortable for the user. Since most sleep stages can be identified using EEG traces, enormous power savings could be achieved by using a single channel of EEG. However, detection of REM sleep from one channel EEG is challenging due to its electroencephalographic similarities with N1 and Wake stages. In this paper we investigate a novel feature in sleep EEG that demonstrates high discriminatory ability for detecting REM phases. We then use this feature, that is based on spectral edge frequency (SEF) in the 8-16 Hz frequency band, together with the absolute power and the relative power of the signal, to develop a simple REM detection algorithm. We evaluate the performance of this proposed algorithm with overnight single channel EEG recordings of 5 training and 15 independent test subjects. Our algorithm achieved sensitivity of 83%, specificity of 89% and selectivity of 61% on a test database consisting of 2221 REM epochs. It also achieved sensitivity and selectivity of 81 and 75% on PhysioNet Sleep-EDF database consisting of 8 subjects. These results demonstrate that SEF can be a useful feature for automatic detection of REM stages of sleep from a single channel EEG.
Rodriguez-Villegas E, Chen G, Radcliffe J, et al., 2014, A pilot study of a wearable apnoea detection device, BMJ Open, Vol: 4, Pages: 1-9, ISSN: 2044-6055
Rationale: Current techniques for monitoring patients for apnoea suffer from significant limitations. These include insufficient availability to meet diagnostic needs, cost, accuracy of results in the presence of artefacts and difficulty of use in unsupervised conditions.Objectives: We created and clinically tested a novel miniature medical device that targets overcoming these limitations.Methods: We studied 20 healthy control participants and 10 patients who had been referred for sleep apnoea diagnosis. The performances of the new system and also of the Food and Drug Administration (FDA) approved SOMNO clinical system, conventionally used for sleep apnoea diagnosis were evaluated under the same conditions. Both systems were tested during a normal night of sleep in controls and patients. Their performances were quantified in terms of detection of apnoea and hypopnoea in individual 10 s epochs, which were compared with scoring of signals by a blinded clinician.Main results: For spontaneous apnoeas during natural sleep and considering the clinician scorer as the gold standard, the new wearable apnoea detection device had 88.6% sensitivity and 99.6% specificity. In comparison the SOMNO system had 14.3% sensitivity and 99.3% specificity. The novel device had been specifically designed to detect apnoea, but if apnoea and hypopnoea during sleep were both considered in the assessment, the sensitivity and specificity were 77.1% and 99.7%, respectively, versus 54% and 98.5%, respectively, for the SOMNO.Conclusions: The performance of the novel device compares very well to the scoring by an experienced clinician even in the presence of breathing artefacts, in this small pilot study. This can potentially make it a real solution for apnoea home monitoring.
Chen G, Rodriguez-Villegas E, Casson AJ, 2014, Wearable Algorithms: An Overview of a Truly Multi-Disciplinary Problem, Wearable Sensors: Fundamentals, Implementation and Applications, Pages: 353-382, ISBN: 9780124186620
This chapter introduces and comprehensively overviews emerging wearable algorithms for embedding in to wearable sensor nodes. We begin with an overview of some of the potential benefits of low power real-time signal processing in wearable sensors with a particular focus on increasing the operational lifetime. Measured results from a practical state-of-the-art sensor platform demonstrate and quantify the design trade-offs present and the potential system optimizations available. We then consider the theory behind wearable algorithms and highlight the key properties of power-lifetime trade-off, big data performance testing, and performance-power trade-off that differentiate these new algorithms from conventional signal processing approaches. We conclude by reviewing the state-of-the-art in low power algorithms implemented in hardware and highlight the key design techniques that are now emerging to realize the lowest possible levels of power consumption.
Imtiaz SA, Rodriguez-Villegas E, 2014, Recommendations for Performance Assessment of Automatic Sleep Staging Algorithms, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5044-5047, ISSN: 1557-170X
A number of automatic sleep scoring algorithms have been published in the last few years. These can potentially help save time and reduce costs in sleep monitoring. However, the use of both R&K and AASM classification, different databases and varying performance metrics makes it extremely difficult to compare these algorithms. In this paper, we describe some readily available polysomnography databases and propose a set of recommendations and performance metrics to promote uniform testing and direct comparison of different algorithms. We use two different polysomnography databases with a simple sleep staging algorithm to demonstrate the usage of all recommendations and presentation of performance results. We also illustrate how seemingly similar results using two different databases can have contrasting accuracies in different sleep stages. Finally, we show how selection of different training and test subjects from the same database can alter the final performance results.
Chen G, de la Cruz I, Rodriguez Villegas E, 2014, Automatic lung tidal volumes estimation from tracheal sounds, Engineering in Medicine and Biology Society (EMBC)
Rodriguez-Villegas E, Logesparan L, Casson AJ, 2014, A Low Power Linear Phase Programmable Long Delay Circuit, IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, Vol: 8, Pages: 432-441, ISSN: 1932-4545
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- Citations: 4
Imtiaz SA, Casson AJ, Rodriguez-Villegas E, 2014, Compression in Wearable Sensor Nodes: Impacts of Node Topology, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, Vol: 61, Pages: 1080-1090, ISSN: 0018-9294
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- Citations: 19
Casson AJ, Rodriguez-Villegas E, 2014, Nanowatt multi-scale continuous wavelet transform chip, ELECTRONICS LETTERS, Vol: 50, Pages: 153-154, ISSN: 0013-5194
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- Citations: 6
Hizon JRE, Rodriguez-Villegas E, 2014, A Reconfigurable FGMOS based OTA-C Filter, IEEE International Symposium on Circuits and Systems (ISCAS), Publisher: IEEE, Pages: 2093-2096, ISSN: 0271-4302
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- Citations: 1
Saraswat R, Rodriguez-Villegas E, 2014, Chaotic Inductively Coupled Non-PLL Low Emission Transmitter for Implanted Devices, IEEE 5th Latin American Symposium on Circuits and Systems (LASCAS), Publisher: IEEE, ISSN: 2330-9954
Saraswat R, Rodriguez-Villegas E, 2014, Low Power EMI Mitigation Strategies Utilizing Non-PLL Based Periodic and Chaotic SSC Profiles for Implanted Devices, 2014 8TH INTERNATIONAL SYMPOSIUM ON MEDICAL INFORMATION AND COMMUNICATION TECHNOLOGY (ISMICT), ISSN: 2326-828X
Imtiaz SA, Rodriguez-Villegas E, 2014, Evaluating the use of line length for automatic sleep spindle detection, 36th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 5024-5027, ISSN: 1557-170X
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- Citations: 4
Imtiaz SA, Saremi-Yarahmadi S, Rodriguez-Villegas E, 2013, Automatic detection of sleep spindles using Teager energy and spectral edge frequency, IEEE Biomedical Circuits and Systems Conference (BioCAS), Publisher: IEEE, Pages: 262-265
Sleep spindles are the hallmark of N2 stage of sleep. They are transient waveforms observed on sleep electroencephalogram and their identification is required for sleep staging. Due to the large number of sleep spindles appearing on an overnight sleep EEG, automating the detection of sleep spindles would be desirable, not only to save specialist time but also for fully automated sleep staging systems. A simple algorithm for automatic sleep spindle detection is presented in this paper using only one channel of EEG input. This algorithm uses Teager energy and spectral edge frequency to mark sleep spindles and results in a sensitivity of 80% and specificity of about 98%. It is also shown that more than 91% of spindles detected by the algorithm were in N2 and N3 stages combined.
Logesparan L, Casson AJ, Imtiaz SA, et al., 2013, Discriminating between best performing features for seizure detection and data selection, 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 1692-1695, ISSN: 1557-170X
Seizure detection algorithms have been developed to solve specific problems, such as seizure onset detection, occurrence detection, termination detection and data selection. It is thus inherent that each type of seizure detection algorithm would detect a different EEG characteristic (feature). However most feature comparison studies do not specify the seizure detection problem for which their respective features have been evaluated. This paper shows that the best features/algorithm bases are not the same for all types of algorithms but depend on the type of seizure detection algorithm wanted. To demonstrate this, 65 features previously evaluated for online seizure data selection are re-evaluated here for seizure occurrence detection, using performance metrics pertinent to each seizure detection type whilst keeping the testing methodology the same. The results show that the best performing features/algorithm bases for data selection and occurrence detection algorithms are different and that it is more challenging to achieve high detection accuracy for the former seizure detection type. This paper also provides a comprehensive evaluation of the performance of 65 features for seizure occurrence detection to aid future researchers in choosing the best performing feature(s) to improve seizure detection accuracy.
Kafal i Ö, Bromuri S, Sindlar M, et al., 2013, Commodity 12: A smart e-health environment for diabetes management, Journal of Ambient Intelligence and Smart Environments, Vol: 5, Pages: 479-502
Rajagopal MK, Rodriguez-Villegas E, 2013, Towards Wearable Sleep Diagnostic Systems for Point-of-Care Applications, 1st IEEE-EMBS Special Topic Conference on Point-of-Care (POCT) Healthcare Technologies (PHT), Publisher: IEEE, Pages: 26-29
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- Citations: 1
Saraswat R, Rodriguez-Villegas E, 2013, A Low Emission, Low Power Non-Linear Frequency Modulation Based Transmitter For Implanted Devices, 35th Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (EMBC), Publisher: IEEE, Pages: 826-829, ISSN: 1557-170X
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- Citations: 3
Casson AJ, Rodriguez-Villegas E, 2012, Signal agnostic compressive sensing for Body Area Networks: comparison of signal reconstructions., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 4497-4500, ISSN: 1557-170X
Compressive sensing is a lossy compression technique that is potentially very suitable for use in power constrained sensor nodes and Body Area Networks as the compression process has a low computational complexity. This paper investigates the reconstruction performance of compressive sensing when applied to EEG, ECG, EOG and EMG signals; establishing the performance of a signal agnostic compressive sensing strategy that could be used in a Body Area Network monitoring all of these. The results demonstrate that the EEG, ECG and EOG can all be reconstructed satisfactorily, although large inter- and intra- subject variations are present. EMG signals are not well reconstructed. Compressive sensing may therefore also find use as a novel method for the identification of EMG artefacts in other electro-physiological signals.
Shad A, Rodriguez-Villegas E, 2012, Proof of concept of a shoe based human activity monitor., Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference, Pages: 6398-6401, ISSN: 1557-170X
This paper presents the proof of concept of a low power, low cost, wearable activity monitor. The functionality of the system is based on accurate stride detection from signals generated by two force sensing resistors integrated within a normal shoe. A novel algorithm is proposed that is able to differentiate between walking and non-walking activities with high accuracy. The performance of the proof of concept system was validated in five subjects who underwent five repetitions of three different speed walking tests, and five repetitions of five non-walking artefact generating tests. The system achieved a total sensitivity of 96% with 98% specificity and an overall accuracy of 94%.
Abdulghani AM, Casson AJ, Rodriguez-Villegas E, 2012, Compressive sensing scalp EEG signals: implementations and practical performance, MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, Vol: 50, Pages: 1137-1145, ISSN: 0140-0118
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- Citations: 59
Logesparan L, Imtiaz SA, Casson AJ, et al., 2012, A 1.8 mW 12 channel wireless seizure detector for miniaturized portable EEG systems, 2012 9th International Conference on Ubiquitous Healthcare (u-Healthcare 2012)
Casson AJ, Rodriguez-Villegas E, 2012, Signal agnostic compressive sensing for body area networks: Comparison of signal reconstructions, 34th international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 4497-4500
Compressive sensing is a lossy compression technique that is potentially very suitable for use in power constrained sensor nodes and Body Area Networks as the compression process has a low computational complexity. This paper investigates the reconstruction performance of compressive sensing when applied to EEG, ECG, EOG and EMG signals; establishing the performance of a signal agnostic compressive sensing strategy that could be used in a Body Area Network monitoring all of these. The results demonstrate that the EEG, ECG and EOG can all be reconstructed satisfactorily, although large inter- and intra- subject variations are present. EMG signals are not well reconstructed. Compressive sensing may therefore also find use as a novel method for the identification of EMG artefacts in other electro-physiological signals.
Logesparan L, Casson AJ, Rodriguez-Villegas E, 2012, Improving seizure detection performance reporting: Analysing the duration needed for a detection, 34th international conference of the IEEE Engineering in Medicine and Biology Society, Pages: 1069-1072
Improving seizure detection performance relies not only on novel signal processing approaches but also on new accurate, reliable and comparable performance reporting to give researchers better and fairer tools for understanding the true algorithm operation. This paper investigates the sensitivity of current performance metrics to the duration of data that must be marked as candidate seizure activity before a seizure detection is made. The results demonstrate that not all metrics are insensitive to this high level choice in the algorithm design, and provide new approaches for comparing between reported algorithm performances in a robust and reliable manner.
Logesparan L, Casson AJ, Rodriguez-Villegas E, 2012, Optimal features for online seizure detection, Medical and Biological Engineering and Computing, Vol: 50, Pages: 659-669, ISSN: 0140-0118
This study identifies characteristic features in scalp EEG that simultaneously give the best discrimination between epileptic seizures and background EEG in minimally pre-processed scalp data; and have minimal computational complexity to be suitable for on-line, real-time analysis. The discriminative performance of 65 previously reported features has been evaluated in terms of sensitivity, specificity, Area Under the sensitivity-specificity Curve (AUC), and relative computational complexity, on 47 seizures (split in 2698 2 s sections) in over 172 hours of scalp EEG from 24 adults. The best performing features are line length and relative power in the 12.5–25 Hz band. Relative power has a better seizure detection performance (AUC=0.83; line length AUC=0.77), but is calculated after the Discrete Wavelet Transform and is thus more computationally complex. Hence, relative power achieves the best performance for offline detection, whilst line length would be preferable for online low complexity detection. These results, from the largest systematic study of seizure detection features, aid future researchers in selecting an optimal set of features when designing algorithms for both standard offline detection and new online low computational complexity detectors.
Shad A, Rodriguez-Villegas E, 2012, Proof of concept of a shoe based human activity monitor., Annu Int Conf IEEE Eng Med Biol Soc, Vol: 2012, Pages: 6398-6401
This paper presents the proof of concept of a low power, low cost, wearable activity monitor. The functionality of the system is based on accurate stride detection from signals generated by two force sensing resistors integrated within a normal shoe. A novel algorithm is proposed that is able to differentiate between walking and non-walking activities with high accuracy. The performance of the proof of concept system was validated in five subjects who underwent five repetitions of three different speed walking tests, and five repetitions of five non-walking artefact generating tests. The system achieved a total sensitivity of 96% with 98% specificity and an overall accuracy of 94%.
Hizon JRE, Rodriguez-Villegas E, 2012, A High Transconductance Efficiency FGMOS OTA for g<sub>m</sub>-C Ladder Filters, 55th IEEE International Midwest Symposium on Circuits and Systems (MWSCAS), Publisher: IEEE, Pages: 105-108, ISSN: 1548-3746
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- Citations: 4
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