Non-contact Vital Signs Monitoring in Paediatric Anaesthesia - Current Challenges and Future Direction.

Non-contact vital sign monitoring is an area of increasing interest in the clinical scenario since it offers advantages over traditional monitoring using leads and wires. These advantages include reduction in transmission of infection and more freedom of movement. Yet there is a paucity of studies available in the clinical setting particularly in paediatric anaesthesia. This scoping review aims to investigate why contactless monitoring, specifically with red-green-blue cameras, is not implemented in mainstream practise. The challenges, drawbacks and limitations of non-contact vital sign monitoring, will be outlined, together with future direction on how it can potentially be implemented in the setting of paediatric anaesthesia, and in the critical care scenario.

The Effects of Skin Temperature Changes on the Integrity of Skin Tissue: A Systematic Review.

OBJECTIVE: To determine whether changes in skin temperature can affect the integrity of skin. METHODOLOGY: The authors conducted a systematic literature search as per the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. They searched the CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane, MEDLINE Complete, Academic Search Ultimate, and HyDi databases for articles examining the effects of skin temperature on skin integrity published through April 2020. Two independent reviewers scored the methodologic quality of the 13 included studies. RESULTS: Only 11 studies were included in the qualitative analysis, as the other two articles had a critical risk of bias. There is strong evidence to indicate that an increase in skin temperature leads to changes in skin structure and function. However, ulcer formation was more affected by intrinsic and extrinsic factors, rather than by temperature alone. CONCLUSION: Further high-quality randomized controlled trials are required to investigate the direct effect of skin temperature on ulceration.

A Technical Review of Foot Temperature Measurement Systems.

People suffering from diabetes are at risk of developing foot ulcerations which, if left untreated, could also lead to amputation. Monitoring of the foot temperature can help in the prevention of these foot complications, and various studies have shown that elevated temperatures may be indicative of ulceration. Over the years, there have been various devices that were designed for foot temperature monitoring, for both clinical and home use. The technologies used included infrared thermometry, liquid crystal thermography, infrared thermography, and a vast range of analogue and digital temperature sensors incorporated into different measurement platforms. All these systems are able to collect thermal data from the foot, with some being able to acquire data only when the foot is stationary and others being able to acquire data from the foot in motion, which can give more in-depth insight into any emerging problems. The aim of this review is to evaluate the available literature related to the technologies used in these systems, outlining the benefits of each and what further developments may be required to make the foot temperature analysis more effective.

Principal component analysis of hyperspectral data for early detection of mould in cheeselets.

The application of non-destructive process analytical technologies in the area of food science got a lot of attention the past years. In this work we used hyperspectral imaging to detect mould on milk agar and cheese. Principal component analysis is applied to hyperspectral data to localise and visualise mycelia on the samples' surface. It is also shown that the PCA loadings obtained from a set of training samples can be applied to hyperspectral data from new test samples to detect the presence of mould on these. For both the agar and cheeselets, the first three principal components contained more than 99 % of the total variance. The spatial projection of the second principal component highlights the presence of mould on cheeselets. The proposed analysis methods can be adopted in industry to detect mould on cheeselets at an early stage and with further testing this application may also be extended to other food products.

Bacterial and fungal contaminants in caprine and ovine cheese: A meta-analysis assessment.

The dairy industry is of great importance to the European economy contributing towards € 8.7 billion of the total trade surplus. Caprine and ovine milk amount to 3.1% of the 152 million tonnes of milk produced in Europe, 95% of which is transformed into dairy products such as cheese. This cheese is mostly produced in small holdings from untreated milk, making it a high-risk dairy product for human consumption. A total of 49 foodborne disease outbreaks caused by dairy products were registered in 2017 in Europe. Therefore, these products remain a serious health risk. This meta-analysis examined 30 studies assessing bacterial or fungal contamination of caprine or ovine milk cheeses. The significantly contaminating microbes were found to be Acremonium spp. (19%), Aspergillus spp. (23%), Bacillus spp. (2%), Brucella spp. (34%), Enterobactericae spp. (36%), Enterococcus spp. (28%), Escherichia spp. (15%), Fusarium spp. (21%), Geotrichum spp. (22%), Listeria spp. (11%), Mucor spp. (15%), Penicillium spp. (25%), Phoma spp. (20%), Rhizopus spp. (15%), Salmonella spp. (3%), Scopulariopsis spp. (19%) and Staphylococcus spp. (25%) in caprine and ovine cheese, indicating a variety of food pathogens as well as spoilers. Raw milk is nutritious hence prone to contamination. However, since traditional cheese is often made from untreated milk, it is important to educate cheesemakers of key safety measures and good manufacturing practice allowing for the safe production of these food items.

A review of foot pose and trajectory estimation methods using inertial and auxiliary sensors for kinematic gait analysis.

The use of foot mounted inertial and other auxiliary sensors for kinematic gait analysis has been extensively investigated during the last years. Although, these sensors still yield less accurate results than those obtained employing optical motion capture systems, the miniaturization and their low cost have allowed the estimation of kinematic spatiotemporal parameters in laboratory conditions and real life scenarios. The aim of this work was to present a comprehensive approach of this scientific area through a systematic literature research, breaking down the state-of-the-art methods into three main parts: (1) zero velocity interval detection techniques; (2) assumptions and sensors' utilization; (3) foot pose and trajectory estimation methods. Published articles from 1995 until December of 2018 were searched in the PubMed, IEEE Xplore and Google Scholar databases. The research was focused on two categories: (a) zero velocity interval detection methods; and (b) foot pose and trajectory estimation methods. The employed assumptions and the potential use of the sensors have been identified from the retrieved articles. Technical characteristics, categorized methodologies, application conditions, advantages and disadvantages have been provided, while, for the first time, assumptions and sensors' utilization have been identified, categorized and are presented in this review. Considerable progress has been achieved in gait parameters estimation on constrained laboratory environments taking into account assumptions such as a person walking on a flat floor. On the contrary, methods that rely on less constraining assumptions, and are thus applicable in daily life, led to less accurate results. Rule based methods have been mainly used for the detection of the zero velocity intervals, while more complex techniques have been proposed, which may lead to more accurate gait parameters. The review process has shown that presently the best-performing methods for gait parameter estimation make use of inertial sensors combined with auxiliary sensors such as ultrasonic sensors, proximity sensors and cameras. However, the experimental evaluation protocol was much more thorough, when single inertial sensors were used. Finally, it has been highlighted that the accuracy of setups using auxiliary sensors may further be improved by collecting measurements during the whole foot movement and not only partially as is currently the practice. This review has identified the need for research and development of methods and setups that allow for the robust estimation of kinematic gait parameters in unconstrained environments and under various gait profiles.

Digital orbitoplethysmograph: A new device to study the regional cerebral circulation using extraorbital plethysmography.

BACKGROUND: Noninvasive diagnostic methods utilizing pulse wave measurements on the surface of the head are an important tool in diagnosing various types of cerebrovascular disease. The measurement of extraorbital pressure fluctuations reflects intraocular and intracranial pressure changes and can be used to estimate pressure changes in intracranial arteries and the collateral circulation. NEW METHOD: In this paper, we describe our patented (CZ 305757) digital device for noninvasive measuring and monitoring of orbital movements using pressure detection. We conducted preclinical tests (126 measurements on 42 volunteers) to evaluate the practical capabilities of our device. Two human experts visually assessed the quality of the pressure pulsation and discriminability among various test conditions (specifically, subject lying, sitting, and the Matas carotid occlusion test). RESULTS: The results showed that our device provided clinically relevant outcomes with a sufficient level of detail of the pulse wave and a high reliability (not less than 85%) in all clinically relevant situations. It was possible to record the effect of the Matas carotid occlusion test. COMPARISON WITH EXISTING METHOD(S): Our fully noninvasive, lightweight (185 g), portable, and wireless device provides a considerably cheaper alternative to the current diagnostic methods (e.g., transcranial ultrasound, X-ray, or MRI angiography) for specific assessment of cerebral circulation. Within a minute, it can detect the Willis circle integrity and thus eliminate the potential risks associated with the Matas test using standard EEG. CONCLUSIONS: Our device represents an improvement and a valid alternative to the current methods diagnosing regional cerebral circulation.

A multi-segment modelling approach for foot trajectory estimation using inertial sensors.

BACKGROUND: Kinematic gait analysis employing multi-segment foot models has been mainly conducted in laboratories by means of optical motion capture systems. This type of process requires considerable setup time and is constrained by a limited capture space. A procedure involving the use of multiple inertial measurement units (IMUs) is proposed to overcome these limitations. RESEARCH QUESTION: This study presents a new approach for the estimation of the trajectories of a multi-segment foot model by means of multiple IMUs. METHODS: To test the proposed method, a system consisting of four IMUs attached to the shank, heel, dorsum and toes segments of the foot, was considered. The performance of the proposed method was compared to that of a conventional method using IMUs adopted from the literature. In addition, an optical motion capture system was used as a reference to assess the performance of the implemented methods. RESULTS: Employing the suggested method, all trajectory directions of the shank, heel and dorsum segments, as well as the Z (yaw) direction of the toes segment, have exhibited an error reduction varying between 8% and 55%. However, X (roll) and Y (pitch) direction of the toes segment presented an error increase of 17% and 26%, respectively. The estimation of the vertical displacement, corresponding to the foot clearance, was improved for all segments, resulting in a final mean accuracy and precision of 3.5 ±â€¯2.8 cm, 2.7 ±â€¯2.1 cm, 0.8 ±â€¯0.7 cm and 1.1 ±â€¯0.9 cm for the shank, heel, dorsum and toes segments, respectively. SIGNIFICANCE: It has been demonstrated that as an alternative to tracking each foot segment separately, the fusion of multiple IMU measurements using kinematic equations, considerably improves the estimated trajectories, especially when considering vertical foot displacements. The proposed method could complement the use of smaller and cheaper sensors, while still matching the same performance of other published methods, making the suggested approach very attractive for real life applications.

PCA-driven Detection and Enhancement of Microchanges in Video Data Associated with Heart Rate.

The heart rate is a fundamental measure which can be used to monitor an individual's level of health or fitness, as well as a range of medical conditions. Conventional heart rate devices used in hospitals require continuous contact with specific points on the patient's body, depending on the device being used. Such continuous contact could prove to be a risk for skin irritation or infections and may also be of inconvenience to the patients, potentially restricting movement. A contactless approach for measuring heart rate could thus prove significant benefits over conventional, contact-based devices. This paper presents a method for the contactless extraction of heart rate measurements from a video footage using principal component analysis, with no pre-defined region of interest being required. Three different ways of presenting the outcome from principal component analysis are presented and the results obtained are discussed.

A systematic review investigating the relationship of electroencephalography and magnetoencephalography measurements with sensorimotor upper limb impairments after stroke.

BACKGROUND: Predicting sensorimotor upper limb outcome receives continued attention in stroke. Neurophysiological measures by electroencephalography (EEG) and magnetoencephalography (MEG) could increase the accuracy of predicting sensorimotor upper limb recovery. NEW METHOD: The aim of this systematic review was to summarize the current evidence for EEG/MEG-based measures to index neural activity after stroke and the relationship between abnormal neural activity and sensorimotor upper limb impairment. Relevant papers from databases EMBASE, CINHAL, MEDLINE and pubMED were identified. Methodological quality of selected studies was assessed with the Modified Downs and Black form. Data collected was reported descriptively. RESULTS: Seventeen papers were included; 13 used EEG and 4 used MEG applications. Findings showed that: (a) the presence of somatosensory evoked potentials in the acute stage are related to better outcome of upper limb motor impairment from 10 weeks to 6 months post-stroke; (b) an interhemispheric imbalance of cortical oscillatory signals associated with upper limb impairment; and (c) predictive models including beta oscillatory cortical signal factors with corticospinal integrity and clinical measures could enhance upper limb motor prognosis. COMPARING WITH EXISTING METHOD: The combination of neurological biomarkers with clinical measures results in higher statistical power than using neurological biomarkers alone when predicting motor recovery in stroke. CONCLUSIONS: Alterations in neural activity by means of EEG and MEG are demonstrated from the early post-stroke stage onwards, and related to sensorimotor upper limb impairment. Future work exploring cortical oscillatory signals in the acute stage could provide further insight about prediction of upper limb sensorimotor recovery.

Principal Component Analysis of Dynamic Thermography Data from Pregnant and Non-Pregnant Women.

In this work we propose a novel approach for the analysis ofdynamic thermography data based on the application of principal component analysis to thermal video data. The proposed approach is applied to thermal video recordings of the abdominal region of pregnant and non-pregnant female participants, and reveals consistent temperature trends across participants that to date have not been reported. Both for the pregnant and non-pregnant participants, the first principal component was found to describe approximately 80% of the total variance, and when combined, the first three principal components explained more than 90% of the total variance. The presence of consistent temporal components across participants is indicative of common passive as well as active underlying mechanisms thatgive rise to the observed temperature patterns. The outcome of this investigation supports further development and application of the proposedmethod in obstetrics and other medical fields.

Registration of Dynamic Thermography Data of the Abdomen of Pregnant and Non-Pregnant Women.

To date the use of thermography in the context of obstetrics has been primarily limited to the acquisition and analysis of static thermal images. In contrast, dynamic thermography involves the acquisition of a sequence of thermal images, taking into account temporal variations that would otherise be overlooked. However, dynamic recordings of regions of interest in human participants are likely to be affected by unavoidable participant movement due to breathing and other involuntary movements. In this work, a triangulation-based video registration technique using local affine transformations is proposed to register the abdominal region in dynamic thermal sequences. The proposed method is tested on one hour recordings of thermal data obtained from 10 pregnant and 10 non-pregnant female participants. The results obtained show that the proposed approach can compensate for movements and significantly improve region alignment throughout the thermal image sequence, thereby facilitating subsequent analysis of spatiotemporal temperature data in the considered image sequence.

The Application of Medical Thermography to Discriminate Neuroischemic Toe Ulceration in the Diabetic Foot.

This study aimed to determine whether thermal imaging can detect temperature differences between healthy feet, nonulcerated neuroischemic feet, and neuroischemic feet with toe ulcers in patients with type 2 diabetes mellitus (T2DM). Participants were prospectively divided into 3 groups: T2DM without foot problems; a healthy, nonulcerated neuroischemic group, and an ulcerated neuroischemic group. Thermal images of the feet were obtained with automated segmentation of regions of interest. Thermographic images from 43 neuroischemic feet, 21 healthy feet, and 12 neuroischemic feet with active ulcer in one of the toes were analyzed. There was a significant difference in toe temperatures between the 3 groups ( P = .001), that is, nonulcerated neuroischemic (n = 181; mean temperature = 27.7°C [±2.16 SD]) versus neuroischemic ulcerated (n = 12; mean temperature = 28.7°C [±3.23 SD]), and healthy T2DM group (n = 104; mean temperature = 24.9°C [±5.04 SD]). A post hoc analysis showed a significant difference in toe temperatures between neuroischemic nonulcerated and healthy T2DM groups ( P = .001), neuroischemic ulcerated and healthy groups ( P = .001). However, no significant differences in toe temperatures were identified between the ulcerated neuroischemic and nonulcerated neuroischemic groups ( P = .626). There were no significant differences between the ulcerated toes (n = 12) and the nonulcerated toes (n = 57) of the same foot in the ulcerated neuroischemic group ( P = .331). Toe temperatures were significantly higher in neuroischemic feet with or without ulceration compared with healthy feet in patients with T2DM. There were no significant differences in temperatures of ulcerated toes and the nonulcerated toes of the same foot, implying that all the toes of the same foot could potentially be at risk of developing complications, which can be potentially detected by infrared thermography.

Establishing Differences in Thermographic Patterns between the Various Complications in Diabetic Foot Disease.

AIM: To evaluate the potential of thermography as an assessment tool for the detection of foot complications by understanding the variations in temperature that occur in type 2 diabetes mellitus (DM). METHODS: Participants were categorized according to a medical examination, ankle brachial index, doppler waveform analysis, and 10-gram monofilament testing into five groups: healthy adult, DM with no complications, DM with peripheral neuropathy, DM with neuroischaemia, and DM with peripheral arterial disease (PAD) groups. Thermographic imaging of the toes and forefeet was performed. RESULTS: 43 neuroischaemic feet, 41 neuropathic feet, 58 PAD feet, 21 DM feet without complications, and 126 healthy feet were analyzed. The temperatures of the feet and toes were significantly higher in the complications group when compared to the healthy adult and DM healthy groups. The higher the temperatures of the foot in DM, the higher the probability that it is affected by neuropathy, neuroischaemia, or PAD. CONCLUSIONS: Significant differences in mean temperatures exist between participants who were healthy and those with DM with no known complications when compared to participants with neuroischaemia, neuropathy, or PAD. As foot temperature rises, so does the probability of the presence of complications of neuropathy, neuroischaemia, or peripheral arterial disease.

Working memory alpha-beta band oscillatory signatures in adolescents and young adults.

The timing of neural activity is an intriguing way of exposing behaviorally relevant neural activity, as neural populations exploit transient windows of synchronized activations to exchange dynamic communications in the service of various cognitive operations. The link between neural synchrony and working memory (WM) has been supported at the theoretical and empirical level. However, findings have also shown that WM encoding is also related to significant alpha-beta desynchronization. These findings have been primarily recorded during subsequent memory effect paradigms that compare correct with incorrect encoding trials. The dissociable contribution imparted by various processes to WM performance suggests that incorrect performance may not be directly translatable to unsuccessful encoding. Here, we address the relationship between alpha-beta desynchronization and encoding through the use of an alternative paradigm design by contrasting frontal and parietal human scalp electroencephalography activity during the encoding interval of a delayed matching-to-sample task with that recorded during a control task. The additional use of non-verbal/semantic visual stimulation and recruitment of typically developing adolescent subjects has led us to the conclusion that encoding-relevant alpha-beta decrements can be replicated via a non-verbal/semantic delayed matching-to-sample task and these are also evident in typically developing adolescents, in addition to adults, as has been previously demonstrated. The identification of encoding-related alpha-beta decrements in adolescent subjects performing such WM tasks may open new avenues to explore whether such a rhythmic signature may explain WM and electrophysiological deficits that emerge in various adolescent neuropsychiatric disorders such as attention deficit hyperactivity disorder.

The identification of higher forefoot temperatures associated with peripheral arterial disease in type 2 diabetes mellitus as detected by thermography.

AIMS: The purpose of this study was to investigate whether heat emitted from the feet of patients with type 2 diabetes (DM) and peripheral arterial disease (PAD) differed from those with type 2 diabetes without complications (DM). METHODS: A non-experimental, comparative prospective study design was employed in a tertiary referral hospital. Out of 223 randomly selected participants (430 limbs) who were initially tested, 62 limbs were categorized as DM+PAD and 22 limbs as DM without PAD. Subjects with evidence of peripheral neuropathy were excluded. Participants underwent thermographic imaging. Automatic segmentation of regions of interest extracted the temperature data. RESULTS: A significant difference in temperature in all the toes between the two groups was found (p=0.005, p=0.033, p=0.015, p=0.038 and p=0.02 for toes 1-5 respectively). The mean forefoot temperature in DM+PAD was significantly higher than that in DM (p=.019), with DM+PAD having a higher mean temperature (28.3°C) compared to DM (26.2°C). Similarly, the toes of subjects with DM+PAD were significantly warmer than those of subjects with DM only. CONCLUSIONS: Contrary to expectations the mean toe and forefoot temperatures in DM patients with PAD is higher than in those with DM only. This unexpected result could be attributed to disruption of noradrenergic vasoconstrictor thermoregulatory mechanisms with resulting increased flow through cutaneous vessels and subsequent increased heat emissivity. These results demonstrate that thermography may have potential in detecting PAD and associated temperature differences.

Automated Region Extraction from Thermal Images for Peripheral Vascular Disease Monitoring.

This work develops a method for automatically extracting temperature data from prespecified anatomical regions of interest from thermal images of human hands, feet, and shins for the monitoring of peripheral arterial disease in diabetic patients. Binarisation, morphological operations, and geometric transformations are applied in cascade to automatically extract the required data from 44 predefined regions of interest. The implemented algorithms for region extraction were tested on data from 395 participants. A correct extraction in around 90% of the images was achieved. The process of automatically extracting 44 regions of interest was performed in a total computation time of approximately 1 minute, a substantial improvement over 10 minutes it took for a corresponding manual extraction of the regions by a trained individual. Interrater reliability tests showed that the automatically extracted ROIs are similar to those extracted by humans with minimal temperature difference. This set of algorithms provides a sufficiently accurate and reliable method for temperature extraction from thermal images at par with human raters with a tenfold reduction in time requirement. The automated process may replace the manual human extraction, leading to a faster process, making it feasible to carry out large-scale studies and to increase the regions of interest with minimal cost. The code for the developed algorithms, to extract the 44 ROIs from thermal images of hands, feet, and shins, has been made available online in the form of MATLAB functions and can be accessed from http://www.um.edu.mt/cbc/tipmid.

Phase-based SSVEPs for real-time control of a motorised bed.

Brain-computer interface (BCI) systems have emerged as an augmentative technology that can provide a promising solution for individuals with motor dysfunctions and for the elderly who are experiencing muscle weakness. Steady-state visually evoked potentials (SSVEPs) are widely adopted in BCI systems due to their high speed and accuracy when compared to other BCI paradigms. In this paper, we apply combined magnitude and phase features for class discrimination in a real-time SSVEP-based BCI platform. In the proposed real-time system users gain control of a motorised bed system with seven motion commands and an idle state. Experimental results amongst eight participants demonstrate that the proposed real-time BCI system can successfully discriminate between different SSVEP signals achieving high information transfer rates (ITR) of 82.73 bits/min. The attractive features of the proposed system include noninvasive recording, simple electrode configuration, excellent BCI response and minimal training requirements.

Automated segmentation of regions of interest from thermal images of hands.

Thermal imaging can provide an image of the surface temperature of an object in a non-contact and noninvasive manner, making it particularly appealing for use in medical applications. In applications where it is desirable to extract temperature data from anatomical regions of interest (ROIs) in a standardised and consistent manner, the use of automated segmentation and analysis techniques can provide a faster, more reliable and more consistent approach than manual segmentation of these ROIs. In this paper we present an algorithm which automatically extracts temperature data from eight ROIs in thermal images of the volar aspect of human hands. The algorithm first identifies the hand from the background in the thermal image and then identifies pixels which make up the fingers and the palm. Finally, eight ROIs are extracted from the identified regions. The methods proposed in this work can also be extended for the processing of similar visual images.

EEG-based biometry using steady state visual evoked potentials.

The use of brain signals for person recognition has in recent years attracted considerable interest because of the increased security and privacy these can offer when compared to conventional biometric measures. The main challenge lies in extracting features from the EEG signals that are sufficiently distinct across individuals while also being sufficiently consistent across multiple recording sessions. A range of EEG phenomena including eyes open and eyes closed activity, visual evoked potentials (VEPs) through image presentation, and other mental tasks have been studied for their use in biometry.