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Alexander J. Casson
The University of Manchester, Manchester, U.K.

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Journal article
Published: 06 July 2021 in IEEE Access
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Gelatine based phantoms for electrophysiology are becoming widely used as they allow the controlled validation of new electrode and new instrumentation designs. The phantoms mimic the electrical properties of the human body and allow a pre-recorded electrophysiology signal to be played-out , giving a known signal for the novel electrode or instrumentation to collect. Such controlled testing is not possible with on-person experiments where the signal to be recorded is intrinsically unknown. However, despite the rising interest in gelatine based phantoms there is relatively little public information about their electrical properties and accuracy, how these vary with phantom formulation, and across both frequency and duration of use. This paper investigates ten different phantom configurations, characterising the impedance of the gelatine and electrodes, comparing this to both previously reported electrical models of Ag/AgCl electrodes placed on human skin and to a model made from ex vivo porcine skin. This article shows how the electrical properties of the phantoms can be tuned using different concentrations of gelatine and of sodium chloride (NaCl) added to the mixture, and how these properties vary over the course of seven days for a.c. frequencies in the range 20–1000 Hz. The results demonstrate that gelatine phantoms can accurately mimic the frequency response properties of the body–electrode system to allow for the controlled testing of new electrode and instrumentation designs.

ACS Style

Amani Yousef Owda; Alexander J. Casson. Investigating Gelatine Based Head Phantoms for Electroencephalography Compared to Electrical and Ex Vivo Porcine Skin Models. IEEE Access 2021, 9, 96722 -96738.

AMA Style

Amani Yousef Owda, Alexander J. Casson. Investigating Gelatine Based Head Phantoms for Electroencephalography Compared to Electrical and Ex Vivo Porcine Skin Models. IEEE Access. 2021; 9 ():96722-96738.

Chicago/Turabian Style

Amani Yousef Owda; Alexander J. Casson. 2021. "Investigating Gelatine Based Head Phantoms for Electroencephalography Compared to Electrical and Ex Vivo Porcine Skin Models." IEEE Access 9, no. : 96722-96738.

Original research paper
Published: 24 June 2021 in Healthcare Technology Letters
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This paper presents a new active electrode design for electroencephalogram (EEG) and electrocardiogram (ECG) sensors based on inertial measurement units to remove motion artefacts during signal acquisition. Rather than measuring motion data from a single source for the entire recording unit, inertial measurement units are attached to each individual EEG or ECG electrode to collect local movement data. This data is then used to remove the motion artefact by using normalised least mean square adaptive filtering. Results show that the proposed active electrode design can reduce motion contamination from EEG and ECG signals in chest movement and head swinging motion scenarios. However, it is found that the performance varies, necessitating the need for the algorithm to be paired with more sophisticated signal processing to identify scenarios where it is beneficial in terms of improving signal quality. The new instrumentation hardware allows data driven artefact removal to be performed, providing a new data driven approach compared to widely used blind-source separation methods, and helps enable in the wild EEG recordings to be performed.

ACS Style

Christopher Beach; Mingjie Li; Ertan Balaban; Alexander J. Casson. Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering. Healthcare Technology Letters 2021, 1 .

AMA Style

Christopher Beach, Mingjie Li, Ertan Balaban, Alexander J. Casson. Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering. Healthcare Technology Letters. 2021; ():1.

Chicago/Turabian Style

Christopher Beach; Mingjie Li; Ertan Balaban; Alexander J. Casson. 2021. "Motion artefact removal in electroencephalography and electrocardiography by using multichannel inertial measurement units and adaptive filtering." Healthcare Technology Letters , no. : 1.

Journal article
Published: 02 March 2021 in Sensors
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Diabetic foot ulcers (DFUs) are a life-changing complication of diabetes that can lead to amputation. There is increasing evidence that long-term management with wearables can reduce incidence and recurrence of this condition. Temperature asymmetry measurements can alert to DFU development, but measurements of dynamic information, such as rate of temperature change, are under investigated. We present a new wearable device for temperature monitoring at the foot that is personalised to account for anatomical variations at the foot. We validate this device on 13 participants with diabetes (no neuropathy) (group name D) and 12 control participants (group name C), during sitting and standing. We extract dynamic temperature parameters from four sites on each foot to compare the rate of temperature change. During sitting the time constant of temperature rise after shoe donning was significantly (p < 0.05) faster at the hallux (p = 0.032, 370.4 s (C), 279.1 s (D)) and 5th metatarsal head (p = 0.011, 481.9 s (C), 356.6 s (D)) in participants with diabetes compared to controls. No significant differences at the other sites or during standing were identified. These results suggest that temperature rise time is faster at parts of the foot in those who have developed diabetes. Elevated temperatures are known to be a risk factor of DFUs and measurement of time constants may provide information on their development. This work suggests that temperature rise time measured at the plantar surface may be an indicative biomarker for differences in soft tissue biomechanics and vascularisation during diabetes onset and progression.

ACS Style

Christopher Beach; Glen Cooper; Andrew Weightman; Emma Hodson-Tole; Neil Reeves; Alexander Casson. Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables. Sensors 2021, 21, 1717 .

AMA Style

Christopher Beach, Glen Cooper, Andrew Weightman, Emma Hodson-Tole, Neil Reeves, Alexander Casson. Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables. Sensors. 2021; 21 (5):1717.

Chicago/Turabian Style

Christopher Beach; Glen Cooper; Andrew Weightman; Emma Hodson-Tole; Neil Reeves; Alexander Casson. 2021. "Monitoring of Dynamic Plantar Foot Temperatures in Diabetes with Personalised 3D-Printed Wearables." Sensors 21, no. 5: 1717.

Journal article
Published: 16 November 2020 in IEEE Access
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Wearable devices promise to reduce strain on the healthcare system and to improve quality of life for users. However, adoption in healthcare settings is limited due in part to the need for constant battery maintenance; which leads to reduced adherence, more complex operation and missing sections of data. Energy harvesting can reduce the reliance on batteries, but the harvesting potential varies substantially depending on where the harvester is placed. Few previous studies investigating placement have considered the foot as a harvesting site, despite the significant interest in smart-shoes and the intrinsic social discreteness of wearable devices at the foot. We investigate the amount of power that can be harvested from four sites on the human body (wrist, hip, ankle and foot), with 12 participants walking on a treadmill. We analyse the differences in the frequency spectrum at each of these sites and perform a sweep of inertial energy harvester parameters to identify the optimal parameters for each site on the body. By considering both performing the harvesting at the foot, and the frequency distribution of the input spectrum present for the first time, we identify that harvesting at the foot provides multiple benefits: more power is available in total; greater physical size is available (compared to the wrist); lower $Q$ harvesters can provide better broadband response; and the foot is the least sensitive location for changes in frequency of walking rate. For harvesters sized at 100 mm, we find that there is 4.2, 6.4 and 25.7 times more power at the hip, ankle and foot respectively compared to the wrist. Foot based sensors thus provide a promising approach towards future fully battery-free wearable devices, motivating future work to investigate the sensing modalities that are feasible at the foot.

ACS Style

Christopher Beach; Alexander J. Casson. Inertial Kinetic Energy Harvesters for Wearables: The Benefits of Energy Harvesting at the Foot. IEEE Access 2020, 8, 208136 -208148.

AMA Style

Christopher Beach, Alexander J. Casson. Inertial Kinetic Energy Harvesters for Wearables: The Benefits of Energy Harvesting at the Foot. IEEE Access. 2020; 8 ():208136-208148.

Chicago/Turabian Style

Christopher Beach; Alexander J. Casson. 2020. "Inertial Kinetic Energy Harvesters for Wearables: The Benefits of Energy Harvesting at the Foot." IEEE Access 8, no. : 208136-208148.

Preprint content
Published: 30 October 2020
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One-third of the population in the UK and worldwide struggle with chronic pain. Entraining brain alpha activity through non-invasive visual stimulation has been shown to reduce experimental pain in healthy volunteers. Neural oscillations entrainment offers a potential non-invasive and non-pharmacological intervention for patients with chronic pain, which can be delivered in the home setting and has the potential to reduce use of medications. However, evidence supporting its use in patients with chronic pain is lacking. This study explores whether a) alpha entrainment increase alpha power in patients and b) whether this increase in alpha correlates with analgesia.28 patients with chronic pain sat in a comfortable position and underwent 4-minute visual stimulation using customised goggles at 10 Hz (alpha) and 7 Hz (control) frequency blocks in a randomised cross-over design. 64-channel Electroencephalography (EEG) and 11-point Numeric Rating Scale (NRS) pain intensity and pain unpleasantness scores were recorded before and after stimulation.EEG analysis revealed frontal alpha power was significantly higher when stimulating at 10 Hz when compared to 7 Hz. There was a significant positive correlation between increased frontal alpha and reduction in pain intensity (r=0.33, p<0.05) and pain unpleasantness (r=0.40, p<0.05) in the 10 Hz block.This study provides the first proof of concept that changes in alpha power resulting from entrainment correlate with an analgesic response in patients with chronic pain. Further studies are warranted to investigate dose-response parameters and equivalence to analgesia provided by medications.

ACS Style

Karen Lopez-Diaz; James Henshaw; Alex Casson; Christopher Brown; Jason R. Taylor; Nelson Trujillo-Barreto; Laura J. Arendsen; Anthony K. P. Jones; Manoj Sivan. Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients. A proof-of-concept controlled study. 2020, 1 .

AMA Style

Karen Lopez-Diaz, James Henshaw, Alex Casson, Christopher Brown, Jason R. Taylor, Nelson Trujillo-Barreto, Laura J. Arendsen, Anthony K. P. Jones, Manoj Sivan. Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients. A proof-of-concept controlled study. . 2020; ():1.

Chicago/Turabian Style

Karen Lopez-Diaz; James Henshaw; Alex Casson; Christopher Brown; Jason R. Taylor; Nelson Trujillo-Barreto; Laura J. Arendsen; Anthony K. P. Jones; Manoj Sivan. 2020. "Alpha entrainment drives pain relief using visual stimulation in a sample of chronic pain patients. A proof-of-concept controlled study." , no. : 1.

Journal article
Published: 20 August 2020 in IEEE Sensors Journal
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Deep learning models are used to process and fuse raw data of gait-induced ground reaction force (GRF) for Parkinson’s disease (PD) patients and healthy subjects with the aim to categorize PD severity. This is achieved by learning automatically, end-to-end, the spatiotemporal GRF signals, resulting in an effective PD severity classification with mean performance F1-score of 95.5% and F1-score standard errors of 0.28%. Layer-wise relevance propagation (LRP) is used to interpret the models’ output and provide insight into which features in the spatiotemporal gait GRF signals are most significant for the models’ predictions. This allows their assignment to gait events, implying that while for the classification of healthy gait the heel strike and body balance are the most indicative gait elements, foot landing and body balance are those most affected in advanced stages of PD. The proposed models are resilient to noise and are computationally efficient for processing and classification of large longitudinal GRF signal datasets, therefore they can be useful for detecting deterioration in the postural balance and rating PD severity.

ACS Style

Abdullah S. Alharthi; Alexander J. Casson; Krikor B. Ozanyan. Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating. IEEE Sensors Journal 2020, 21, 1838 -1848.

AMA Style

Abdullah S. Alharthi, Alexander J. Casson, Krikor B. Ozanyan. Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating. IEEE Sensors Journal. 2020; 21 (2):1838-1848.

Chicago/Turabian Style

Abdullah S. Alharthi; Alexander J. Casson; Krikor B. Ozanyan. 2020. "Gait Spatiotemporal Signal Analysis for Parkinson’s Disease Detection and Severity Rating." IEEE Sensors Journal 21, no. 2: 1838-1848.

Conference
Published: 01 July 2020 in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
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Parkinson’s disease is diagnosed based on expert clinical observation of movements. One important clinical feature is decrement, whereby the range of finger motion decreases over the course of the observation. This decrement has been assumed to be linear but has not been examined closely.We previously developed a method to extract a time series representation of a finger-tapping clinical test from 137 smart- phone video recordings. Here, we show how the signal can be processed to visualize archetypal progression of decrement. We use k-means with features derived from dynamic time warping to compare similarity of time series. To generate the archetypal time series corresponding to each cluster, we apply both a simple arithmetic mean, and dynamic time warping barycenter averaging to the time series belonging to each cluster.Visual inspection of the cluster-average time series showed two main trends. These corresponded well with participants with no bradykinesia and participants with severe bradykinesia. The visualizations support the concept that decrement tends to present as a linear decrease in range of motion over time.Clinical relevance— Our work visually presents the archetypal types of bradykinesia amplitude decrement, as seen in the Parkinson’s finger-tapping test. We found two main patterns, one corresponding to no bradykinesia, and the other showing linear decrement over time.

ACS Style

Zhibin Zhao; Hui Fang; Stefan Williams; Samuel D. Relton; Jane Alty; Alex Casson; David Wong. Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2020, 2020, 780 -783.

AMA Style

Zhibin Zhao, Hui Fang, Stefan Williams, Samuel D. Relton, Jane Alty, Alex Casson, David Wong. Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia. 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020; 2020 ():780-783.

Chicago/Turabian Style

Zhibin Zhao; Hui Fang; Stefan Williams; Samuel D. Relton; Jane Alty; Alex Casson; David Wong. 2020. "Time series clustering to examine presence of decrement in Parkinson’s finger-tapping bradykinesia." 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) 2020, no. : 780-783.

Review
Published: 30 June 2020 in European Journal of Pain
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ACS Style

Kajal Patel; Heather Sutherland; James Henshaw; Jason R. Taylor; Christopher A. Brown; Alexander J. Casson; Nelson J. Trujillo‐Barreton; Anthony K. P. Jones; Manoj Sivan. Effects of neurofeedback in the management of chronic pain: A systematic review and meta‐analysis of clinical trials. European Journal of Pain 2020, 24, 1440 -1457.

AMA Style

Kajal Patel, Heather Sutherland, James Henshaw, Jason R. Taylor, Christopher A. Brown, Alexander J. Casson, Nelson J. Trujillo‐Barreton, Anthony K. P. Jones, Manoj Sivan. Effects of neurofeedback in the management of chronic pain: A systematic review and meta‐analysis of clinical trials. European Journal of Pain. 2020; 24 (8):1440-1457.

Chicago/Turabian Style

Kajal Patel; Heather Sutherland; James Henshaw; Jason R. Taylor; Christopher A. Brown; Alexander J. Casson; Nelson J. Trujillo‐Barreton; Anthony K. P. Jones; Manoj Sivan. 2020. "Effects of neurofeedback in the management of chronic pain: A systematic review and meta‐analysis of clinical trials." European Journal of Pain 24, no. 8: 1440-1457.

Preprint content
Published: 31 May 2020 in bioRxiv
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Gelatine based phantoms for electrophysiology are becoming widely used as they allow the controlled validation of new electrode and new instrumentation designs. The phantoms mimic the electrical properties of the human body and allow a pre-recorded electrophysiology signal to be played-out, giving a known signal for the novel electrode or instrumentation to collect. Such controlled testing is not possible with on-person experiments where the signal to be recorded is intrinsically unknown. However, despite the rising interest in gelatine based phantoms there is relatively little public information about their electrical properties and accuracy, how these vary with phantom formulation, and across both time and frequency. This paper investigates ten different phantom configurations, characterising the impedance path through the phantom and comparing this impedance path to both previously reported electrical models of Ag/AgCl electrodes placed on skin and to a model made from ex vivo porcine skin. This article shows how the electrical properties of the phantoms can be tuned using different concentrations of gelatine and of sodium chloride (NaCl) added to the mixture, and how these properties vary over the course of seven days for a.c. frequencies in the range 20–1000 Hz. The results demonstrate that gelatine phantoms can accurately mimic the frequency response properties of the body–electrode system to allow for the controlled testing of new electrode and instrumentation designs.

ACS Style

Amani Yousef Owda; Alexander J. Casson. Electrical properties, accuracy, and multi-day performance of gelatine phantoms for electrophysiology. bioRxiv 2020, 1 .

AMA Style

Amani Yousef Owda, Alexander J. Casson. Electrical properties, accuracy, and multi-day performance of gelatine phantoms for electrophysiology. bioRxiv. 2020; ():1.

Chicago/Turabian Style

Amani Yousef Owda; Alexander J. Casson. 2020. "Electrical properties, accuracy, and multi-day performance of gelatine phantoms for electrophysiology." bioRxiv , no. : 1.

Preprint content
Published: 28 April 2020
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Entraining alpha activity with rhythmic visual, auditory, and electrical stimulation can reduce experimentally induced pain. However, evidence for alpha entrainment and pain reduction in patients with chronic pain is limited. This feasibility study investigated whether visual alpha stimulation can increase alpha power in patients with chronic musculoskeletal pain and secondarily, if chronic pain was reduced following stimulation. In a within-subject design, 22 patients underwent 4-minute periods of stimulation at 10 Hz (alpha), 7 Hz (high-theta, control), and 1 Hz (control) in a pseudo-randomized order. Patients underwent stimulation both sitting and standing and verbally rated their pain before and after each stimulation block on a 0-10 numerical rating scale. Global alpha power was significantly higher during 10 Hz compared to 1 Hz stimulation when patients were standing (t = −6.08, p <.001). On a more regional level, a significant increase of alpha power was found in the right-middle and left-posterior region when patients were sitting. With respect to our secondary aim, no significant reduction of pain intensity and unpleasantness was found. However, only the alpha stimulation resulted in a minimal clinically important difference in at least 50% of participants for pain intensity (50%) and unpleasantness ratings (65%) in the sitting condition. This study provides initial evidence for the potential of visual stimulation as a means to enhance alpha activity in patients with chronic musculoskeletal pain. The brief period of stimulation was insufficient to reduce chronic pain. This study is the first to provide evidence that a brief period of visual stimulation at alpha frequency can significantly increase alpha power in patients with chronic musculoskeletal pain. Further study is warranted to investigate optimal dose and individual stimulation parameters to achieve pain relief in these patients.

ACS Style

Laura J. Arendsen; James Henshaw; Christopher A. Brown; Manoj Sivan; Jason R. Taylor; Nelson J. Trujillo-Barreto; Alexander J. Casson; Anthony K. P. Jones. Entraining alpha activity using visual stimulation in patients with chronic musculoskeletal pain. A feasibility study. 2020, 1 .

AMA Style

Laura J. Arendsen, James Henshaw, Christopher A. Brown, Manoj Sivan, Jason R. Taylor, Nelson J. Trujillo-Barreto, Alexander J. Casson, Anthony K. P. Jones. Entraining alpha activity using visual stimulation in patients with chronic musculoskeletal pain. A feasibility study. . 2020; ():1.

Chicago/Turabian Style

Laura J. Arendsen; James Henshaw; Christopher A. Brown; Manoj Sivan; Jason R. Taylor; Nelson J. Trujillo-Barreto; Alexander J. Casson; Anthony K. P. Jones. 2020. "Entraining alpha activity using visual stimulation in patients with chronic musculoskeletal pain. A feasibility study." , no. : 1.

Journal article
Published: 08 March 2020 in Sensors
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The millimeter-wave band is an ideal part of the electromagnetic radiation to diagnose human skin conditions because this radiation interacts only with tissue down to a depth of a millimetre or less over the band range from 30 GHz to 300 GHz. In this paper, radiometry is used as a non-contact sensor for measuring the human skin reflectance under normal and wet skin conditions. The mean reflectance of the skin of a sample of 50 healthy participants over the (80–100) GHz band was found to be ~0.615 with a standard deviation of ~0.088, and an experimental measurement uncertainty of ±0.005. The thinner skin regions of the back of the hand, the volar forearms and the inner wrist had reflectances 0.068, 0.068 and 0.062 higher than the thicker skin regions of the palm of the hand, the dorsal forearm and the outer wrist skin. Experimental measurements of human skin reflectance in a normal and a wet state on the back of the hand and the palm of the hand regions indicated that the mean differences in the reflectance before and after the application of water were ~0.078 and ~0.152, respectively. These differences were found to be statistically significant as assessed using t-tests (34 paired t-tests and six independent t-tests were performed to assess the significance level of the mean differences in the reflectance of the skin). Radiometric measurements in this paper show the quantitative variations in the skin reflectance between locations, sexes, and individuals. The study reveals that these variations are related to the skin thickness and water content, a capability that has the potential to allow radiometry to be used as a non-contact sensor to detect and monitor skin conditions such as eczema, psoriasis, malignancy, and burn wounds.

ACS Style

Amani Yousef Owda; Neil Salmon; Alexander J Casson; Majdi Owda. The Reflectance of Human Skin in the Millimeter-Wave Band. Sensors 2020, 20, 1480 .

AMA Style

Amani Yousef Owda, Neil Salmon, Alexander J Casson, Majdi Owda. The Reflectance of Human Skin in the Millimeter-Wave Band. Sensors. 2020; 20 (5):1480.

Chicago/Turabian Style

Amani Yousef Owda; Neil Salmon; Alexander J Casson; Majdi Owda. 2020. "The Reflectance of Human Skin in the Millimeter-Wave Band." Sensors 20, no. 5: 1480.

Research article
Published: 21 February 2020 in British Journal of Pain
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Background: Brainwave entrainment (BWE) using rhythmic visual or auditory stimulation has many potential clinical applications, including the management of chronic pain, where there is a pressing need for novel, safe and effective treatments. The aim of this study was to gain qualitative feedback on the acceptability and usability of a novel BWE smartphone application, to ensure it meets the needs and wishes of end users. Methods: Fifteen participants with chronic pain used the application at home for 4 weeks. Semi-structured telephone interviews were then carried out. A template analysis approach was used to interpret the findings, with an initial coding template structured around the constructs of a theoretical framework for assessing acceptability of healthcare interventions. Structured data analysis generated a final modified coding structure, capturing themes generated across participants’ accounts. Results: The four main themes were ‘approach to trying out the app: affective attitude and ethicality’, ‘perceived effectiveness’, ‘opportunity costs and burden’ and ‘intervention coherence and self-efficacy’. All participants were willing to engage with the technology and welcomed it as an alternative approach to medications. Participants appreciated the simplicity of design and the ability to choose between visual or auditory stimulation. All the participants felt confident in using the application. Conclusion: The findings demonstrate preliminary support for the acceptability and usability of the BWE application. This is the first qualitative study of BWE to systematically assess these issues.

ACS Style

Helen N Locke; Joanna Brooks; Laura J Arendsen; Nikhil Kurian Jacob; Alex Casson; Anthony Kp Jones; Manoj Sivan. Acceptability and usability of smartphone-based brainwave entrainment technology used by individuals with chronic pain in a home setting. British Journal of Pain 2020, 14, 161 -170.

AMA Style

Helen N Locke, Joanna Brooks, Laura J Arendsen, Nikhil Kurian Jacob, Alex Casson, Anthony Kp Jones, Manoj Sivan. Acceptability and usability of smartphone-based brainwave entrainment technology used by individuals with chronic pain in a home setting. British Journal of Pain. 2020; 14 (3):161-170.

Chicago/Turabian Style

Helen N Locke; Joanna Brooks; Laura J Arendsen; Nikhil Kurian Jacob; Alex Casson; Anthony Kp Jones; Manoj Sivan. 2020. "Acceptability and usability of smartphone-based brainwave entrainment technology used by individuals with chronic pain in a home setting." British Journal of Pain 14, no. 3: 161-170.

Journal article
Published: 24 December 2019 in Sleep
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Study Objectives Closed-loop auditory stimulation (CLAS) is a method for enhancing slow oscillations (SOs) through the presentation of auditory clicks during sleep. CLAS boosts SOs amplitude and sleep spindle power, but the optimal timing for click delivery remains unclear. Here, we determine the optimal time to present auditory clicks to maximize the enhancement of SO amplitude and spindle likelihood. Methods We examined the main factors predicting SO amplitude and sleep spindles in a dataset of 21 young and 17 older subjects. The participants received CLAS during slow-wave-sleep in two experimental conditions: sham and auditory stimulation. Post-stimulus SOs and spindles were evaluated according to the click phase on the SOs and compared between and within conditions. Results We revealed that auditory clicks applied anywhere on the positive portion of the SO increased SO amplitudes and spindle likelihood, although the interval of opportunity was shorter in the older group. For both groups, analyses showed that the optimal timing for click delivery is close to the SO peak phase. Click phase on the SO wave was the main factor determining the impact of auditory stimulation on spindle likelihood for young subjects, whereas for older participants, the temporal lag since the last spindle was a better predictor of spindle likelihood. Conclusions Our data suggest that CLAS can more effectively boost SOs during specific phase windows, and these differ between young and older participants. It is possible that this is due to the fluctuation of sensory inputs modulated by the thalamocortical networks during the SO.

ACS Style

Miguel Navarrete; Jules Schneider; Hong-Viet V Ngo; Mario Valderrama; Alexander J Casson; Penelope A Lewis. Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults. Sleep 2019, 43, 1 .

AMA Style

Miguel Navarrete, Jules Schneider, Hong-Viet V Ngo, Mario Valderrama, Alexander J Casson, Penelope A Lewis. Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults. Sleep. 2019; 43 (6):1.

Chicago/Turabian Style

Miguel Navarrete; Jules Schneider; Hong-Viet V Ngo; Mario Valderrama; Alexander J Casson; Penelope A Lewis. 2019. "Examining the optimal timing for closed-loop auditory stimulation of slow-wave sleep in young and older adults." Sleep 43, no. 6: 1.

Accepted manuscript
Published: 18 November 2019 in Journal of Neural Engineering
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Electroencephalography (EEG) recorded during transcranial alternating current simulation (tACS) is highly desirable in order to investigate brain dynamics during stimulation, but is corrupted by large amplitude stimulation artefacts. Artefact removal algorithms have been presented previously, but with substantial debates on their performance, utility, and the presence of any residual artefacts. This paper investigates whether machine learning can be used to validate artefact removal algorithms. The postulation is that residual artefacts in the EEG after cleaning would be independent of the experiment performed, making it impossible to differentiate between different parts of an EEG+tACS experiment, or between different behavioural tasks performed. Ten participates undertook two tasks (nBack and backwards digital recall) during simultaneous EEG+tACS, exercising different aspects of working memory. Stimulations during no task and sham conditions were also performed. A previously reported tACS artefact removal algorithm from our group was used to clean the EEG and a linear discriminant analysis was trained on the cleaned EEG to differentiate different parts of the experiment. Baseline, baseline during tACS, working memory task without tACS, and working memory task with tACS data segments could be differentiated with accuracies ranging from 65%-94%, far exceeding chance levels. EEG from the nBack and backwards digital recall tasks could be separated during stimulation, with an accuracy exceeding 72%. If residual tACS artefacts remained after the EEG cleaning these did not dominate the classification process. This helps in building confidence that true EEG information is present after artefact removal. Our methodology presents a new approach to validating tACS artefact removal approaches.

ACS Style

Siddharth Kohli; Alexander J Casson. Machine learning validation of EEG+tACS artefact removal. Journal of Neural Engineering 2019, 17, 016034 .

AMA Style

Siddharth Kohli, Alexander J Casson. Machine learning validation of EEG+tACS artefact removal. Journal of Neural Engineering. 2019; 17 (1):016034.

Chicago/Turabian Style

Siddharth Kohli; Alexander J Casson. 2019. "Machine learning validation of EEG+tACS artefact removal." Journal of Neural Engineering 17, no. 1: 016034.

Research article
Published: 07 November 2019 in PLOS ONE
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Advances in neuroimaging, genomic, motion tracking, eye-tracking and many other technology-based data collection methods have led to a torrent of high dimensional datasets, which commonly have a small number of samples because of the intrinsic high cost of data collection involving human participants. High dimensional data with a small number of samples is of critical importance for identifying biomarkers and conducting feasibility and pilot work, however it can lead to biased machine learning (ML) performance estimates. Our review of studies which have applied ML to predict autistic from non-autistic individuals showed that small sample size is associated with higher reported classification accuracy. Thus, we have investigated whether this bias could be caused by the use of validation methods which do not sufficiently control overfitting. Our simulations show that K-fold Cross-Validation (CV) produces strongly biased performance estimates with small sample sizes, and the bias is still evident with sample size of 1000. Nested CV and train/test split approaches produce robust and unbiased performance estimates regardless of sample size. We also show that feature selection if performed on pooled training and testing data is contributing to bias considerably more than parameter tuning. In addition, the contribution to bias by data dimensionality, hyper-parameter space and number of CV folds was explored, and validation methods were compared with discriminable data. The results suggest how to design robust testing methodologies when working with small datasets and how to interpret the results of other studies based on what validation method was used.

ACS Style

Andrius Vabalas; Emma Gowen; Ellen Poliakoff; Alex Casson. Machine learning algorithm validation with a limited sample size. PLOS ONE 2019, 14, e0224365 .

AMA Style

Andrius Vabalas, Emma Gowen, Ellen Poliakoff, Alex Casson. Machine learning algorithm validation with a limited sample size. PLOS ONE. 2019; 14 (11):e0224365.

Chicago/Turabian Style

Andrius Vabalas; Emma Gowen; Ellen Poliakoff; Alex Casson. 2019. "Machine learning algorithm validation with a limited sample size." PLOS ONE 14, no. 11: e0224365.

Conference paper
Published: 01 October 2019 in 2019 IEEE Sensors Applications Symposium (SAS)
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Long-Short Term Memory models (LSTMs) are data-driven routines that classify Human Activity Recognition (HAR) with minimum human input. The price to pay for analysing large sequences of on-body sensor measurements with LSTMs are high processing power and battery requirements. In this paper, we recognize that sensor data packets have differing information value to classify HAR and propose to quantify it with cross entropy (CrossEn), Kullback Leibler (KL) divergence and sample entropy (SampEn). Both, CrossEn and SampEn have the potential to guide dropping redundant data packets without compromising HAR. However, we do not find substantial improvements in dropping rates when downsampling by CrossEn and SampEn over computationally cheaper random and uniform alternatives. Our results show that the KL divergence, evaluated at training time is equivalent to the classification accuracy criteria that involves a testing set. The computational requirements to compute the KL in real-time could well guide sensor node design to downsample wearable measurements near the user.

ACS Style

Miguel A. G. Belmonte; Alex Casson; Niels Peek. Downsampling wearable sensor data packets by measuring their information value. 2019 IEEE Sensors Applications Symposium (SAS) 2019, 1 -4.

AMA Style

Miguel A. G. Belmonte, Alex Casson, Niels Peek. Downsampling wearable sensor data packets by measuring their information value. 2019 IEEE Sensors Applications Symposium (SAS). 2019; ():1-4.

Chicago/Turabian Style

Miguel A. G. Belmonte; Alex Casson; Niels Peek. 2019. "Downsampling wearable sensor data packets by measuring their information value." 2019 IEEE Sensors Applications Symposium (SAS) , no. : 1-4.

Journal article
Published: 16 September 2019 in IEEE Access
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Edge computing aims to integrate computing into everyday settings, enabling the system to be context-aware and private to the user. With the increasing success and popularity of deep learning methods, there is an increased demand to leverage these techniques in mobile and wearable computing scenarios. In this paper, we present an assessment of a deep human activity recognition system’s memory and execution time requirements, when implemented on a mid-range smartphone class hardware and the memory implications for embedded hardware. This paper presents the design of a convolutional neural network (CNN) in the context of human activity recognition scenario. Here, layers of CNN automate the feature learning and the influence of various hyper-parameters such as the number of filters and filter size on the performance of CNN. The proposed CNN showed increased robustness with better capability of detecting activities with temporal dependence compared to models using statistical machine learning techniques. The model obtained an accuracy of 96.4% in a five-class static and dynamic activity recognition scenario. We calculated the proposed model memory consumption and execution time requirements needed for using it on a mid-range smartphone. Per-channel quantization of weights and per-layer quantization of activation to 8-bits of precision post-training produces classification accuracy within 2% of floating-point networks for dense, convolutional neural network architecture. Almost all the size and execution time reduction in the optimized model was achieved due to weight quantization. We achieved more than four times reduction in model size when optimized to 8-bit, which ensured a feasible model capable of fast on-device inference.

ACS Style

Tahmina Zebin; Patricia J. Scully; Niels Peek; Alexander J. Casson; Krikor B. Ozanyan. Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition. IEEE Access 2019, 7, 133509 -133520.

AMA Style

Tahmina Zebin, Patricia J. Scully, Niels Peek, Alexander J. Casson, Krikor B. Ozanyan. Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition. IEEE Access. 2019; 7 (99):133509-133520.

Chicago/Turabian Style

Tahmina Zebin; Patricia J. Scully; Niels Peek; Alexander J. Casson; Krikor B. Ozanyan. 2019. "Design and Implementation of a Convolutional Neural Network on an Edge Computing Smartphone for Human Activity Recognition." IEEE Access 7, no. 99: 133509-133520.

Conference
Published: 01 July 2019 in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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We present a new wearable electrooculogram (EOG) monitor for measuring eye movements. We fabricated conductive and flexible graphene-based textiles from nylon to use as a sensing electrode, which we then integrated into a commercially available eye mask held in place only with the standard elastic strap. We tested this mask on 4 participants to quantify the noise floor and show that we can detect eye blinks to a high SNR of over 16 dB. We also identify that the material can detect other eye movements in cases when the noise floor is low. As our system is held in place with only an elastic strap it offers the same level of comfort as when wearing a normal eye mask. Our sensors offer an increased level of comfort over conventional gelled electrodes traditionally used in EOG monitoring and may be of use for comfortable eye movement experiments. This is particularly important during sleep studies where the EOG is routinely monitored, but using bulky instrumentation.

ACS Style

Christopher Beach; Nazmul Karim; Alex Casson. A Graphene-Based Sleep Mask for Comfortable Wearable Eye Tracking. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, 2019, 6693 -6696.

AMA Style

Christopher Beach, Nazmul Karim, Alex Casson. A Graphene-Based Sleep Mask for Comfortable Wearable Eye Tracking. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019; 2019 ():6693-6696.

Chicago/Turabian Style

Christopher Beach; Nazmul Karim; Alex Casson. 2019. "A Graphene-Based Sleep Mask for Comfortable Wearable Eye Tracking." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, no. : 6693-6696.

Conference
Published: 01 July 2019 in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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In recent years there has been substantial interest in wearable devices that measure heart rate via photoplethysmography (PPG) sensors placed at the wrist. This is challenging as the wrist PPG signal is severely corrupted by artefacts during motion, and although a number of algorithms are now available commercially and academically there is still a need for improved performance, especially when examining physical activities other than running. To date, algorithms for motion artefact removal from the PPG have focused on the use of a co-located accelerometer to record the motion. In this work, we introduce co-located accelerometer, gyroscope and magnetometer sensors to allow three, six and nine degrees of freedom estimates of the motion present. Assessed during a bike riding task the results show that the heart rate estimation is improved by up 0.57 beats per minute by using the additional information from these new sensors.

ACS Style

Arturo Vazquez Galvez; Alex Casson. Nine degree of freedom motion estimation for wrist PPG heart rate measurements. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, 2019, 3231 -3234.

AMA Style

Arturo Vazquez Galvez, Alex Casson. Nine degree of freedom motion estimation for wrist PPG heart rate measurements. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019; 2019 ():3231-3234.

Chicago/Turabian Style

Arturo Vazquez Galvez; Alex Casson. 2019. "Nine degree of freedom motion estimation for wrist PPG heart rate measurements." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, no. : 3231-3234.

Conference
Published: 01 July 2019 in 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
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Autism is a developmental condition primarily identified by social and communication deficits. However, over 70% of autistic individuals also show motor function deficits, which are evident even when simple stereotyped movements are performed. In this study, we have asked 24 autistic and 22 non-autistic adults to perform pointing movements between two markers 30 cm apart as quickly and as accurately as they can for 10 seconds. Motion tracking was employed to collect data and calculate kinematic features of the movement and aiming accuracy. At the group level, the results showed that autistic individuals performed pointing movements slower but more accurately compared to non-autistic individuals. At the individual level, we have used Machine Learning methods to predict autism diagnosis. Nested result Cross-Validation was used, which in contrast to commonly used K-fold Cross-Validation avoids pooling training and testing data and provides robust performance estimates. Our developed models achieved a statistically significant classification accuracy of 71% and showed that even a simple and short motor task enables discrimination between autistic and non-autistic individuals.

ACS Style

Andrius Vabalas; Emma Gowen; Ellen Poliakoff; Alex Casson. Kinematic features of a simple and short movement task to predict autism diagnosis. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, 2019, 1421 -1424.

AMA Style

Andrius Vabalas, Emma Gowen, Ellen Poliakoff, Alex Casson. Kinematic features of a simple and short movement task to predict autism diagnosis. 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). 2019; 2019 ():1421-1424.

Chicago/Turabian Style

Andrius Vabalas; Emma Gowen; Ellen Poliakoff; Alex Casson. 2019. "Kinematic features of a simple and short movement task to predict autism diagnosis." 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) 2019, no. : 1421-1424.