Motion artifacts in capacitive ECG monitoring systems: a review of existing models and reduction techniques.

Current research focuses on improving electrocardiogram (ECG) monitoring systems to enable real-time and long-term usage, with a specific focus on facilitating remote monitoring of ECG data. This advancement is crucial for improving cardiovascular health by facilitating early detection and management of cardiovascular disease (CVD). To efficiently meet these demands, user-friendly and comfortable ECG sensors that surpass wet electrodes are essential. This has led to increased interest in ECG capacitive electrodes, which facilitate signal detection without requiring gel preparation or direct conductive contact with the body. This feature makes them suitable for wearables or integrated measurement devices. However, ongoing research is essential as the signals they measure often lack sufficient clinical accuracy due to susceptibility to interferences, particularly Motion Artifacts (MAs). While our primary focus is on studying MAs, we also address other limitations crucial for designing a high Signal-to-Noise Ratio (SNR) circuit and effectively mitigating MAs. The literature on the origins and models of MAs in capacitive electrodes is insufficient, which we aim to address alongside discussing mitigation methods. We bring attention to digital signal processing approaches, especially those using reference signals like Electrode-Tissue Impedance (ETI), as highly promising. Finally, we discuss its challenges, proposed solutions, and offer insights into future research directions.

Use of pager devices in New Zealand public hospitals as a critical communication tool: Barriers & way forward.

Objectives: The aim of this study was to analyse the current use, identify challenges and barriers and propose a way forward for the use of the pager devices in the in-hospital communications. Methods: Initially, 447 studies were identified through database searching. After checking against the eligibility, 39 studies were included. Full-text records were retrieved and reviewed by two authors. After excluding unrelated studies and duplicate records, a total of 12 articles were selected for the final review. Results: The use of pagers often lacks standardisation, content, format, urgency level, and clarity within the message. Some studies reported that medical staff preferred in-person interactions with consults instead of communicating over the phone or pagers. Productive communication can reduce the turnaround time by up to 50%. The key challenges are; (1) data security and privacy, (2) timely acknowledgement of received communication, (3) lack of two-way communications causing issues in critical care situations and (4) there is no standard process for the in-hospital communications. Conclusion: We found that the clinicians' age, experience, speciality and preferences greatly matter and influence the selection of tools and technology in healthcare. With revolutionary advances in technology, smartphones have inevitably become beneficial to healthcare, owing to multiple instant messaging applications (apps) that can streamline encrypted clinical communication between medical teams and could be safely used for in-hospital communications.

Vital signs and early warning score monitoring: perceptions of clinical staff about current practices and introducing an electronic rapid response system.

Aims and objectives: This study investigated clinical staff perceptions of learning about current monitoring practices and the planned introduction of an electronic system for patient monitoring. The aim of this research was to evaluate the perceptions of clinical staff (nurses and doctors) about the perceived strengths and weaknesses of the current state of the rapid response system (RRS) and how those strengths and weakness would be affected by introducing an electronic RRS. Methods: This research applied a descriptive study methodology. Two detailed sessions on demonstration on the electronic RRS for measuring and recording vital signs and the electronic Early Warning System (EWS) were followed by two structured surveys administered through an online portal (SurveyMonkey) for nurses and doctors working at Taranaki District Health Board. The study was planned and conducted between October 2020 and May 2021 at Taranaki Base Hospital, New Plymouth, New Zealand. Results: We found that the perceptions of clinical staff were a combination of key practice issues with current manual monitoring, expectations of improved visibility of vital sign charts, better communication between staff and thus improved patient care with the introduction of an electronic system. A majority (24, 60%) of nurses reported that, when called to assess deteriorating patients, the responders arrive at bedside within 5-30 min and an additional 11 (27%) said the responders arrive within 5 min. That is a collective 87% responder arrival within 30 min. Conclusion: Staff believe that an electronic RRS could improve communication, speed up decision making and have a positive impact on patient outcomes.

Workload involved in vital signs-based monitoring & responding to deteriorating patients: A single site experience from a regional New Zealand hospital.

Objective: This study aimed to quantify the workload involved in patient monitoring by vital signs and early warning scores (EWS), and the time spent by a rapid response team locally known as the Patient-at-Risk (PaR) team in responding to deteriorating patients. Methods: The workload involved in the measurement and the documentation of vital signs and EWS was quantified by time and motion study using electronic stopwatch application in 167 complete sets of vital signs observations taken by nursing staff on general hospital wards at Taranaki Base Hospital, New Plymouth, New Zealand. The workload involved in responding to deteriorating patients was measured by the PaR team in real-time and recorded in an electronic logbook specifically designed for this purpose. Dependent variables were studied using analysis of variance (ANOVA), post hoc Tukey, Kruskal Wallis test, Mann-Whitney test and correlation tests. Results: The mean time to measure and record a complete set of vital signs including interruptions was 4:18 (95% CI: 4:07-4:28) minutes. After excluding interruptions, the mean time taken to measure and record a set of vital signs was 3:24 (95% CI: 3:15-3:33) minutes. We found no statistical difference between the observer, location of the patient, staff characteristics or experience and patient characteristics. PaR nurses' mean time to provide rapid response was 47:36 (95% CI: 44:57-50:15) minutes. Significantly more time was spent on patients having severe degrees of deterioration (higher EWS) < 0.001. No statistical difference was observed between ward specialty, and nursing shifts. Conclusions: Patient monitoring and response to deterioration consumed considerable time. Time spent in monitoring was not affected by independent and random factors studied; however, time spent on the response was greater when patients had higher degrees of deterioration.

Automatic lesion segmentation using atrous convolutional deep neural networks in dermoscopic skin cancer images.

BACKGROUND: Melanoma is the most dangerous and aggressive form among skin cancers, exhibiting a high mortality rate worldwide. Biopsy and histopathological analysis are standard procedures for skin cancer detection and prevention in clinical settings. A significant step in the diagnosis process is the deep understanding of the patterns, size, color, and structure of lesions based on images obtained through dermatoscopes for the infected area. However, the manual segmentation of the lesion region is time-consuming because the lesion evolves and changes its shape over time, making its prediction challenging. Moreover, it is challenging to predict melanoma at the initial stage as it closely resembles other skin cancer types that are not malignant as melanoma; thus, automatic segmentation techniques are required to design a computer-aided system for accurate and timely detection. METHODS: As deep learning approaches have gained significant attention in recent years due to their remarkable performance, therefore, in this work, we proposed a novel design of a convolutional neural network (CNN) framework based on atrous convolutions for automatic lesion segmentation. This architecture is built based on the concept of atrous/dilated convolutions which are effective for semantic segmentation. A deep neural network is designed from scratch employing several building blocks consisting of convolutional, batch normalization, leakyReLU layer, and fine-tuned hyperparameters contributing altogether towards higher performance. CONCLUSION: The network was tested on three benchmark datasets provided by International Skin Imaging Collaboration (ISIC), i.e., ISIC 2016, ISIC 2017, and ISIC 2018. The experimental results showed that the proposed network achieved an average Jaccard index of 90.4% on ISIC 2016, 81.8% on ISIC 2017, and 89.1% on ISIC 2018 datasets, respectively which is recorded as higher than the top three winners of the ISIC challenge and other state-of-the-art methods. Also, the model successfully extracts lesions from the whole image in one pass in less time, requiring no pre-processing step. The conclusions yielded that network is accurate in performing lesion segmentation on adopted datasets.

Failure mode and effect analysis (FMEA) to identify and mitigate failures in a hospital rapid response system (RRS).

We performed FMEA on the existing RRS with the help of routine users of the RRS who acted as subject matter experts and evaluated the failures for their criticality using the Risk Priority Number approach based on their experience of the RRS. The FMEA found 35 potential failure modes and 101 failure mode effects across 13 process steps of the RRS. The afferent limb of RRS was found to be more prone to these failures (62, 61.4%) than the efferent limb of the RRS (39, 38.6%). Modification of calling criteria (12, 11.9%) and calculation of New Zealand Early Warning Scores (NZEWS) calculation (11, 10.9%) steps were found to potentially give rise to the highest number of these failures. Causes of these failures include human error and related factors (35, 34.7%), staff workload/staffing levels (30, 29.7%) and limitations due to paper-based charts and organisational factors (n = 30, 29.7%). The demonstrated electronic system was found to potentially eliminate or reduce the likelihood of 71 (70.2%) failures. The failures not eliminated by the electronic RRS require targeted corrective measures including scenario-based training and education, and revised calling criteria to include triggers for hypothermia and high systolic blood pressure.

Melanoma Classification Using a Novel Deep Convolutional Neural Network with Dermoscopic Images.

Automatic melanoma detection from dermoscopic skin samples is a very challenging task. However, using a deep learning approach as a machine vision tool can overcome some challenges. This research proposes an automated melanoma classifier based on a deep convolutional neural network (DCNN) to accurately classify malignant vs. benign melanoma. The structure of the DCNN is carefully designed by organizing many layers that are responsible for extracting low to high-level features of the skin images in a unique fashion. Other vital criteria in the design of DCNN are the selection of multiple filters and their sizes, employing proper deep learning layers, choosing the depth of the network, and optimizing hyperparameters. The primary objective is to propose a lightweight and less complex DCNN than other state-of-the-art methods to classify melanoma skin cancer with high efficiency. For this study, dermoscopic images containing different cancer samples were obtained from the International Skin Imaging Collaboration datastores (ISIC 2016, ISIC2017, and ISIC 2020). We evaluated the model based on accuracy, precision, recall, specificity, and F1-score. The proposed DCNN classifier achieved accuracies of 81.41%, 88.23%, and 90.42% on the ISIC 2016, 2017, and 2020 datasets, respectively, demonstrating high performance compared with the other state-of-the-art networks. Therefore, this proposed approach could provide a less complex and advanced framework for automating the melanoma diagnostic process and expediting the identification process to save a life.

Real-Time and Offline Evaluation of Myoelectric Pattern Recognition for the Decoding of Hand Movements.

Pattern recognition algorithms have been widely used to map surface electromyographic signals to target movements as a source for prosthetic control. However, most investigations have been conducted offline by performing the analysis on pre-recorded datasets. While real-time data analysis (i.e., classification when new data becomes available, with limits on latency under 200-300 milliseconds) plays an important role in the control of prosthetics, less knowledge has been gained with respect to real-time performance. Recent literature has underscored the differences between offline classification accuracy, the most common performance metric, and the usability of upper limb prostheses. Therefore, a comparative offline and real-time performance analysis between common algorithms had yet to be performed. In this study, we investigated the offline and real-time performance of nine different classification algorithms, decoding ten individual hand and wrist movements. Surface myoelectric signals were recorded from fifteen able-bodied subjects while performing the ten movements. The offline decoding demonstrated that linear discriminant analysis (LDA) and maximum likelihood estimation (MLE) significantly (p < 0.05) outperformed other classifiers, with an average classification accuracy of above 97%. On the other hand, the real-time investigation revealed that, in addition to the LDA and MLE, multilayer perceptron also outperformed the other algorithms and achieved a classification accuracy and completion rate of above 68% and 69%, respectively.

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications.

BACKGROUND: Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle. OBJECTIVES: This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications. METHODS: We developed an artificial intelligence model based on adaptive neuro-fuzzy inference to detect prediabetes and T2DM via individualized monitoring. The key contributing factors to the proposed model include heart rate, heart rate variability, breathing rate, breathing volume, and activity data (steps, cadence, and calories). The data was collected using an advanced wearable body vest and combined with manual recordings of blood glucose, height, weight, age, and sex. The model analyzed the data alongside a clinical knowledgebase. Fuzzy rules were used to establish baseline values via existing interventions, clinical guidelines, and protocols. RESULTS: The proposed model was tested and validated using Kappa analysis and achieved an overall agreement of 91%. CONCLUSION: We also present a 2-year follow-up observation from the prediction results of the original model. Moreover, the diabetic profile of a participant using M-health applications and a wearable vest (smart shirt) improved when compared to the traditional/routine practice.

A systematic review of rapid response applications based on early warning score for early detection of inpatient deterioration.

AIM: The aim of this study was to investigate the effectiveness of current rapid response applications available in acute care settings for escalation of patient deterioration. Current challenges and barriers, as well as key recommendations, were also discussed. METHODS: We adopted PRISMA review methodology and screened a total of 559 articles. After considering the eligibility and selection criteria, we selected 13 articles published between 2015 and 2019. The selection criteria were based on the inclusion of studies that report on the advancement made to the current practice for providing rapid response to the patient deterioration in acute care settings. RESULTS: We found that current rapid response applications are complicated and time-consuming for detecting inpatient deterioration. Existing applications are either siloed or challenging to use, where clinicians are required to move between two or three different applications to complete an end-to-end patient escalation workflow - from vital signs collection to escalation of deteriorating patients. We found significant differences in escalation and responses when using an electronic tool compared to the manual approach. Moreover, encouraging results were reported in extensive documentation of vital signs and timely alerts for patient deterioration. CONCLUSION: The electronic vital signs monitoring applications are proved to be efficient and clinically suitable if they are user-friendly and interoperable. As an outcome, several key recommendations and features were identified that would be crucial to the successful implementation of any rapid response system in all clinical settings.

A Usability and User-Experience Analysis of VitalsAssist: A mHealth Application to Monitor Vital Signs in Acute Care Settings.

M-health applications are playing an important role in current healthcare delivery, individual's health and well-being. Usability of mHealth applications (apps) is a critical factor for the success of the apps, yet this is often overlooked in the current health care solutions in primary care, secondary (acute) care, community care and especially in remote patient monitoring applications. This work aimed to co-design the vital signs monitoring application with end-users and clinicians. The co-design user-experience includes goals and objectives, participant inclusion and exclusion criteria, task list, testing documentation, laboratory-based usability testing and data analysis for identifying gaps and opportunities. The study found two main issues from the usability analysis, presentation of the information such as use of icons, text and graphs and clinical workflow related matters such as the number of mandatory steps required to finish a task.

Evaluation of surface EMG-based recognition algorithms for decoding hand movements.

Myoelectric pattern recognition (MPR) to decode limb movements is an important advancement regarding the control of powered prostheses. However, this technology is not yet in wide clinical use. Improvements in MPR could potentially increase the functionality of powered prostheses. To this purpose, offline accuracy and processing time were measured over 44 features using six classifiers with the aim of determining new configurations of features and classifiers to improve the accuracy and response time of prosthetics control. An efficient feature set (FS: waveform length, correlation coefficient, Hjorth Parameters) was found to improve the motion recognition accuracy. Using the proposed FS significantly increased the performance of linear discriminant analysis, K-nearest neighbor, maximum likelihood estimation (MLE), and support vector machine by 5.5%, 5.7%, 6.3%, and 6.2%, respectively, when compared with the Hudgins' set. Using the FS with MLE provided the largest improvement in offline accuracy over the Hudgins feature set, with minimal effect on the processing time. Among the 44 features tested, logarithmic root mean square and normalized logarithmic energy yielded the highest recognition rates (above 95%). We anticipate that this work will contribute to the development of more accurate surface EMG-based motor decoding systems for the control prosthetic hands.

Classification and Identification of Industrial Gases Based on Electronic Nose Technology.

Rapid detection and identification of industrial gases is a challenging problem. They have a complex composition and different specifications. This paper presents a method based on the kernel discriminant analysis (KDA) algorithm to identify industrial gases. The smell prints of four typical industrial gases were collected by an electronic nose. The extracted features of the collected gases were employed for gas identification using different classification algorithms, including principal component analysis (PCA), linear discriminant analysis (LDA), PCA + LDA, and KDA. In order to obtain better classification results, we reduced the dimensions of the original high-dimensional data, and chose a good classifier. The KDA algorithm provided a high classification accuracy of 100% by selecting the offset of the kernel function c = 10 and the degree of freedom d = 5. It was found that this accuracy was 4.17% higher than the one obtained using PCA. In the case of standard deviation, the KDA algorithm has the highest recognition rate and the least time consumption.

Non-invasive continuous blood pressure monitoring systems: current and proposed technology issues and challenges.

High blood pressure (BP) or hypertension is the single most crucial adjustable risk factor for cardiovascular diseases (CVDs) and monitoring the arterial blood pressure (ABP) is an efficient way to detect and control the prevalence of the cardiovascular health of patients. Therefore, monitoring the regulation of BP during patients' daily life plays a critical role in the ambulatory setting and the latest mobile health technology. In recent years, many studies have been conducted to explore the feasibility and performance of such techniques in the health care system. The ultimate aim of these studies is to find and develop an alternative to conventional BP monitoring by using cuff-less, easy-to-use, fast, and cost-effective devices for controlling and lowering the physical harm of CVDs to the human body. However, most of the current studies are at the prototype phase and face a range of issues and challenges to meet clinical standards. This review focuses on the description and analysis of the latest continuous and cuff-less methods along with their key challenges and barriers. Particularly, most advanced and standard technologies including pulse transit time (PTT), ultrasound, pulse arrival time (PAT), and machine learning are investigated. The accuracy, portability, and comfort of use of these technologies, and the ability to integrate to the wearable healthcare system are discussed. Finally, the future directions for further study are suggested.

Obesity Risk Assessment Model Using Wearable Technology with Personalized Activity, Calorie Expenditure and Health Profile.

There is a worldwide increase in the rate of obesity and its related long-term conditions, emphasizing an immediate need to address this modern-age global epidemic of healthy living. Moreover, healthcare spending on long-term or chronic care conditions such as obesity is increasing to the point that requires effective interventions and advancements to reduce the burden of the healthcare. This research focuses on the early risk assessment of overweight/obesity using wearable technology. We establish an individualised health profile that identifies the level of activity and current health status of an individual using real-time activity and vital signs. We developed an algorithm to assess the risk of obesity using the individual's current activity and calorie expenditure. The algorithm was deployed on a smartphone application to collect wearable device data, and user reported data. Based on the collected data, the proposed application assesses the risk of obesity/overweight, measures the current activity level and recommends an optimized calorie plan.

A Novel Medical Device for Early Detection of Melanoma.

Melanoma is the deadliest form of skin cancer. Early detection of melanoma is vital, as it helps in decreasing the death rate as well as treatment costs. Dermatologists are using image-based diagnostic tools to assist them in decision-making and detecting melanoma at an early stage. We aim to develop a novel handheld medical scanning device dedicated to early detection of melanoma at the primary healthcare with low cost and high performance. However, developing this particular device is very challenging due to the complicated computations required by the embedded diagnosis system. In this paper, we propose a hardware-friendly design for implementing an embedded system by exploiting the recent hardware advances in reconfigurable computing. The developed embedded system achieved optimized implementation results for the hardware resource utilization, power consumption, detection speed and processing time with high classification accuracy rate using real data for melanoma detection. Consequently, the proposed embedded diagnosis system meets the critical embedded systems constraints, which is capable for integration towards a cost- and energy-efficient medical device for early detection of melanoma.

Estimating Systolic Blood Pressure Using Convolutional Neural Networks.

Continuous blood pressure (BP) monitoring can produce a significant amount of digital data, which increases the chance of early diagnosis and improve the rate of survival for people diagnosed with hypertension and Cardiovascular diseases (CVDs). However, mining and processing this vast amount of data are challenging. This research is aimed to address this challenge by proposing a deep learning technique, convolutional neural network (CNN), to estimate the systolic blood pressure (SBP) using electrocardiogram (ECG) and photoplethysmography (PPG) signals. Two different methods are investigated and compared in this research. In the first method, continuous wavelet transform (CWT) and CNN have been employed to estimate the SBP. For the second method, we used random sampling within the stochastic gradient descent (SGD) optimization of CNN and the raw ECG and PPG signals for training the network. The Medical Information Mart for Intensive Care (MIMIC III) database is used for both methods, which split to two parts, 70% for training our network and the remaining used for testing the performance of the network. Both methods are capable of learning how to extract relevant features from the signals. Therefore, there is no need for engineered feature extraction compared to previous works. Our experimental results show high accuracy for both CNN-based methods which make them promising and reliable architectures for SBP estimation.

A Systematic Review of Wearable Sensors and IoT-Based Monitoring Applications for Older Adults - a Focus on Ageing Population and Independent Living.

This review aims to present current advancements in wearable technologies and IoT-based applications to support independent living. The secondary aim was to investigate the barriers and challenges of wearable sensors and Internet-of-Things (IoT) monitoring solutions for older adults. For this work, we considered falls and activity of daily life (ADLs) for the ageing population (older adults). A total of 327 articles were screened, and 14 articles were selected for this review. This review considered recent studies published between 2015 and 2019. The research articles were selected based on the inclusion and exclusion criteria, and studies that support or present a vision to provide advancement to the current space of ADLs, independent living and supporting the ageing population. Most studies focused on the system aspects of wearable sensors and IoT monitoring solutions including advanced sensors, wireless data collection, communication platform and usability. Moderate to low usability/ user-friendly approach is reported in most of the studies. Other issues found were inaccurate sensors, battery/ power issues, restricting the users within the monitoring area/ space and lack of interoperability. The advancement of wearable technology and the possibilities of using advanced IoT technology to assist older adults with their ADLs and independent living is the subject of many recent research and investigation.

A Cox-Based Risk Prediction Model for Early Detection of Cardiovascular Disease: Identification of Key Risk Factors for the Development of a 10-Year CVD Risk Prediction.

BACKGROUND AND OBJECTIVE: Current cardiovascular disease (CVD) risk models are typically based on traditional laboratory-based predictors. The objective of this research was to identify key risk factors that affect the CVD risk prediction and to develop a 10-year CVD risk prediction model using the identified risk factors. METHODS: A Cox proportional hazard regression method was applied to generate the proposed risk model. We used the dataset from Framingham Original Cohort of 5079 men and women aged 30-62 years, who had no overt symptoms of CVD at the baseline; among the selected cohort 3189 had a CVD event. RESULTS: A 10-year CVD risk model based on multiple risk factors (such as age, sex, body mass index (BMI), hypertension, systolic blood pressure (SBP), cigarettes per day, pulse rate, and diabetes) was developed in which heart rate was identified as one of the novel risk factors. The proposed model achieved a good discrimination and calibration ability with C-index (receiver operating characteristic (ROC)) being 0.71 in the validation dataset. We validated the model via statistical and empirical validation. CONCLUSION: The proposed CVD risk prediction model is based on standard risk factors, which could help reduce the cost and time required for conducting the clinical/laboratory tests. Healthcare providers, clinicians, and patients can use this tool to see the 10-year risk of CVD for an individual. Heart rate was incorporated as a novel predictor, which extends the predictive ability of the past existing risk equations.

Clinical decision support systems in hospital care using ubiquitous devices: Current issues and challenges.

Supporting clinicians in decision making using advanced technologies has been an active research area in biomedical engineering during the past years. Among a wide range of ubiquitous systems, smartphone applications have been increasingly developed in healthcare settings to help clinicians as well as patients. Today, many smartphone applications, from basic data analysis to advanced patient monitoring, are available to clinicians and patients. Such applications are now increasingly integrating into healthcare for clinical decision support, and therefore, concerns around accuracy, stability, and dependency of these applications are rising. In addition, lack of attention to the clinicians' acceptability, as well as the low impact on the medical professionals' decision making, are posing more serious issues on the acceptability of smartphone applications. This article reviews smartphone-based decision support applications, focusing on hospital care settings and their overall impact of these applications on the wider clinical workflow. Additionally, key challenges and barriers of the current ubiquitous device-based healthcare applications are identified. Finally, this article addresses current challenges, future directions, and the adoption of mobile healthcare applications.