Patient Health Monitoring Research Articles

Frontal EEG correlation based human emotion identification and classification.

Humans express their feelings and intentions of their actions or communication through emotions. Recent advancements in technology involve machines in human communication in day-to-day life. Thus, understanding of human emotions by machines will be very helpful in assisting the user in a far better way. Various physiological and non-physiological signals can be used to make the machines to recognize the emotion of a person. The identification of emotional content in the signals is crucial to understand emotion and the machines act with emotional intelligence at appropriate times, thus providing a better human machine interaction with emotion identification system and mental health monitoring for psychiatric patients. This work includes the creation of an emotion EEG dataset, the development of an algorithm for identifying the emotion elicitation segments in the EEG signal, and the classification of emotions from EEG signals. The EEG signals are divided into 3s segments, and the segments with emotional content are selected based on the decrease in correlation between the frontal electrodes. The selected segments are validated with the facial expressions of the subjects in the appropriate time segments of the face video. EEGNet is used to classify the emotion from the EEG signal. The classification accuracy with the selected emotional EEG segments is higher compared to the accuracy using all the EEG segments. In subject-specific classification, an average accuracy of 80.87% is obtained from the network trained with selected EEG segments, and 70.5% is obtained from training with all EEG segments. In subject-independent classification, the accuracy of classification is 67% and 63.8% with and without segment selection, respectively. The proposed method of selection of EEG segments is validated using the DEAP dataset, and classification accuracies and F1-scores of subject dependent and subject-independent methods are presented.

Remote Patient Health Monitoring Using Iot And Artificial Intelligence

Remote Patient Health Monitoring Using Iot And Artificial Intelligence

Assessment of arteriosclerosis based on lognormal fitting

Objective. Pulse pressure waves contain information about human physiology. There is a need for a simple, accurate way to know cardiovascular health in the clinic, so as to realize the implementation of convenient and effective early health monitoring for patients with arteriosclerosis.Approach. This study proposes an arteriosclerosis assessment method based on fitting a lognormal function, along with improving a conventional electronic sphygmomanometer. During the deflation phase of blood pressure measurement, the cuff pressure was kept constant (40 mmHg) and an additional 10 s of pulse signal was acquired. To derive the pulse pressure waveforms for a single cycle, the acquired pulse data of 101 cases were preprocessed in this study, including filtering for noise removal, onset point identification, removal of baseline drift, and normalization. In this study, an improved pulse resolution algorithm is proposed for the multimodal problem of the pulse wave, combining waveform matching and threshold setting, and finally obtaining the resolution parameters of the lognormal function with an average error less than 1.5%.Main results. According to the correlation analysis, the resolved parametersA1,W2,C2,W3, andC3were significantly correlated with brachial-ankle Pulse Wave Velocity, and the absolute correlation range in 0.17-0.53, which can be used as a reference index for arteriosclerosis. An arteriosclerosis assessment model was constructed based on the support vector mechanism, and the prediction accuracy was 91.1%.Significance. This study provides a new solution idea for the arteriosclerosis assessment method as well as the pulse resolution algorithm, which has a greater reference value.

IoT-Based Smart System for Continuous Patient Health Monitoring

IoT-Based Smart System for Continuous Patient Health Monitoring

Blockchain Factors in the Design of Smart-Media for E-Healthcare Management.

According to the current situation of deep aging globally, how to provide low-cost and high-quality medical services has become a problem that the whole society needs to consider. To address these challenges, we propose an e-healthcare management system leveraging the integration of the Internet of Things (IoT) and blockchain technologies. Our system aims to provide comprehensive, reliable, and secure one-stop services for patients. Specifically, we introduce a blockchain-based searchable encryption scheme for decentralized storage and real-time updates of electronic health records (EHRs). This approach ensures secure and efficient data traceability across medical equipment, drug supply chains, patient health monitoring, and medical big data management. By improving information processing capabilities, our system aspires to advance the digital transformation of e-healthcare services.

Elderly care and health monitoring using smart healthcare technology: An improved routing scheme for wireless body area networks

AbstractHypertensive patients need regular checkups and constant monitoring for taking time critical decisions by the medical experts. Unfortunately, it is hard to maintain uninterrupted patient health surveillance due to limited medical staff resulting in an increasing mortality rate annually. Thanks to recent developments in wireless sensor networking, we can monitor constantly and efficiently diverse parameters of a network. Similarly, Wireless Body Area Networks (WBANs) have become a well‐known sub‐branch of Wireless Sensor Networks. Such sensor networks can be leveraged for patient health monitoring, minimising the medical staff workload. Wireless Body Area Networks require tiny sensor nodes with limited battery power. Therefore, it is always desirable to design effective routing schemes that can enhance network lifetime, and reduce packet drop ratio. In this paper, we re‐simulate and explain in detail the results of a selected published journal article for WBANs and provide some modifications to improve the network's overall performance. Based on these amendments, the modified protocol successfully extends the operational time of the network than the original. Our performance evaluation parameters are dead nodes, throughput, residual energy, and path loss versus the number of rounds. These analyses support effective solutions that improve network performance and data delivery ratio.

Biosensor applications in the monitoring of elderly patients

Nurse-based patient monitoring is prone to errors due to manual measurements and documentation, leading to potential inaccuracies in care. The use of biosensors offers a promising solution by enabling real-time and continuous monitoring of patient health. Categorizing patient care reports as critical or non-critical using mobile recording systems based on biosensor data can help prevent errors and improve care. The use of biosensors can significantly reduce morbidity and mortality, especially after emergencies and accidents. These devices improve the quality of care and increase the satisfaction of older people, their families and healthcare professionals. Wearable biosensors make it easier for older people to monitor their health, which can help reduce hospital admissions. Chronic diseases such as cardiovascular disease, cancer, diabetes, dementia and stroke pose challenges to healthcare delivery and interpretation of results. Integrating biosensors into health monitoring and measurement is an innovative approach to managing these chronic conditions more effectively. To improve self-management of chronic diseases in older people, it is essential to educate healthcare professionals and promote research in this area. As a result, the use of biosensors to monitor the daily activities and health parameters of elderly patients is expanding, highlighting the importance of multidisciplinary research in biotechnology, chemistry, engineering and nursing.

CCL2-mediated endothelial injury drives cardiac dysfunction in long COVID.

Evidence linking the endothelium to cardiac injury in long coronavirus disease (COVID) is well documented, but the underlying mechanisms remain unknown. Here we show that cytokines released by endothelial cells (ECs) contribute to long-COVID-associated cardiac dysfunction. Using thrombotic vascular tissues from patients with long COVID and induced pluripotent stem cell-derived ECs (iPSC-ECs), we modeled endotheliitis and observed similar dysfunction and cytokine upregulation, notably CCL2. Cardiac organoids comprising iPSC-ECs and iPSC-derived cardiomyocytes showed cardiac dysfunction after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) exposure, driven by CCL2. Profiling of chromatin accessibility and gene expression at a single-cell resolution linked CCL2 to 'phenotype switching' and cardiac dysfunction, validated by high-throughput proteomics. Disease modeling of cardiac organoids and exposure of human ACE2 transgenic mice to SARS-CoV-2 spike proteins revealed that CCL2-induced oxidative stress promoted post-translational modification of cardiac proteins, leading to cardiac dysfunction. These findings suggest that EC-released cytokines contribute to cardiac dysfunction in long COVID, highlighting the importance of early vascular health monitoring in patients with long COVID.

Associations between wearable device metrics and performance status in adult patients with cancer.

427 Background: Wearable devices present a promising method for monitoring physical performance in cancer patients, potentially bridging the gap between patient experiences at home and clinical evaluations. This study assesses associations between performance status, home-based wearable device metrics, and patient-reported outcomes (PROs) among adults with cancer. (NCT03952767). Methods: This prospective cohort study enrolled 62 adults undergoing treatment for cancer, irrespective of primary site. Participants wore GENEActiv wrist accelerometer sensors (Activinsights Ltd., Kimbolton, UK) on the lumbar region and non-dominant wrist for 2 weeks, tracking gait speed and activity levels. Patients concurrently completed PRO questionnaires on physical function (PF) and fatigue (PROMIS Fatigue 7a & PROMIS Physical Function 10a). Study participants were stratified by baseline self-reported Karnofsky performance status (KPS; 100, 80-90, & ≤70). Linear regression models were used to assess associations between (1) KPS and device metrics and (2) device metrics and PROs. Covariates included age and a binary indicator for COVID period (pre/post 3/1/2020). Results: Average device wear time across the cohort was 22.2 hours/day. Significant differences in gait speed and moderate activity time were observed between the KPS≤70 group and the higher KPS groups ( Table ). Significant associations were also observed between device metrics and PROs. Each 1-hour increment moderate activity was associated with 5.5 point greater PROMIS PF score (95% CI: 3.3, 7.7; p<0.0001) and 4.6 point lower PROMIS Fatigue score (-6.7, -2.5; p<0.0001). Each 0.1 meter/sec increment in gait speed was associated with 4.6 point greater PF score (95% CI: 2.2, 7.1; p=0.0004) and 4.8 point lower Fatigue score (-6.9, -2.7; p<0.0001). Similar associations were observed between in-clinic assessments of gait speed and physical function scores. Conclusions: The associations between wearable device metrics and KPS indicate that wearable technology is both feasible and reliable for remote health monitoring in cancer patients. The associations between device metrics and PROs demonstrate that these metrics reflect patient experiences and outcomes relevant to both patients and regulatory authorities. The associations with physical function, a key PRO in the FDA’s Patient-Focused Drug Development guidance, are particularly noteworthy. Clinical trial information: NCT03952767 . Group 1(KPS 100) Group 2(KPS 80-90) Group 3(KPS ≤70) p (Group 1 vs 3) p (2 vs 3) Gait Speed (m/s) - adjusted mean, 95% CI 0.74 (0.70, 0.78) 0.72 (0.69, 0.74) 0.65 (0.60, 0.70) 0.0058 0.0210 Sedentary time (hours) 11.2 (9.8, 12.6) 10.2 (9.3, 11.0) 10.7 (9.1, 12.2) 0.5912 0.5647 Light activity (hours) 1.4 (1.0, 1.7) 1.3 (1.1, 1.5) 0.9 (0.5, 1.3) 0.0570 0.0474 Moderate activity (hours) 1.4 (1.0, 1.8) 1.2 (0.9, 1.5) 0.5 (0.0, 1.0) 0.0068 0.0159 Vigorous activity (hours) 0.1 (0.0, 0.2) 0.0 (0.0, 0.1) 0.0 (0.0, 0.1) 0.1201 0.2835

An Optimised Task Scheduling of Remote Sensing Data Processing for Smart Patient Health Monitoring

An Optimised Task Scheduling of Remote Sensing Data Processing for Smart Patient Health Monitoring

Patient Health Monitoring Using Fibrillation Detection in Electrocardiogram Signal

Fibrillation (Fib) is the most well-known sort of sporadic heartbeat. The sporadic termination of electrical impulses causes the atria (the top chambers in the heart) to tremble (or fibrillate). Cardiovascular arrhythmias happen in older people with coronary disease. Now and again, individuals with Fib have no manifestations, and their condition is just recognizable upon actual assessment. The issues include inconsistent heartbeats when the heart is reliably in a sporadic mood, as anticipated through the patient well-being observation framework (PHMS). The PHMS proposed calculation is ready to anticipate the inconsistent pulses by the location of the R top in electrocardiogram signals (ECG) and the unpredictable beat anticipated by utilizing the P wave along with the QRS complex with the T wave for one heart cycle. Hence, this paper proposes a hybrid Convolutional Neural Network (CNN) and Recurrent Neural Networks (RNN), termed hybrid C-RNN, to proceed with the characterization and determination of the arrhythmic beats. This work also analyzes the QRS peak variations, followed by classification. The practicability of the heartbeat characterization utilizing hybrid C-RNN is completed by metrics like accuracy, sensitivity, and specificity. The proposed model achieved an accuracy of 98.57%, sensitivity of 1%, and specificity of 9.565217e-01%. This is compared best with some existing approaches in the result section.

Nanowire-based biosensors for solving biomedical problems.

The review considers modern achievements and prospects of using nanowire biosensors, principles of their operation, methods of fabrication, and the influence of the Debye effect, which plays a key role in improving the biosensor characteristics. Special attention is paid to the practical application of such biosensors for the detection of a variety of biomolecules, demonstrating their capabilities and potential in the detection of a wide range of biomarkers of various diseases. Nanowire biosensors also show excellent results in such areas as early disease diagnostics, patient health monitoring, and personalized medicine due to their high sensitivity and specificity. Taking into consideration their high efficiency and diverse applications, nanowire-based biosensors demonstrate significant promise for commercialization and widespread application in medicine and related fields, making them an important area for future research and development.

Innovative IoT Solutions for Enhanced Patient Health Monitoring and Healthcare Efficiency in Hospitals

Abstract: Hospitals worldwide have been grappling with overcrowding, a challenge that became particularly acute during the COVID-19 pandemic. Implementing IoT devices can enhance hospital efficiency and reduce the risk of infection for healthcare providers. With IoT, remote health monitoring becomes possible, leading to more timely and improved patient care. The research employs a stratified sampling method, with survey questionnaires distributed to gather data. Based on this data, a proposed system will be developed to evaluate its feasibility and effectiveness. As IoT technology advances, the system is expected to evolve, providing users with greater confidence and ease of use. The goal of this research and proposal is to implement a solution that enhances health monitoring in hospitals, ultimately offering patients better and more timely healthcare.

Enhancing Hospital Efficiency through IoT and AI: A Smart Healthcare System

In the rapidly evolving healthcare landscape, the integration of Internet of Things (IoT) and Artificial Intelligence (AI) is transforming hospital efficiency. This study explores how these technologies can enhance hospital operations by optimizing resource management, improving patient care, and reducing operational costs. IoT devices enable real-time monitoring of patient health and hospital assets, facilitating timely interventions and maintenance. Concurrently, AI-driven analytics improve decision-making processes by predicting patient needs and optimizing resource allocation. The synergy between IoT and AI creates a smart healthcare system that offers advanced data processing and actionable insights, leading to improved patient outcomes. Despite challenges such as data privacy concerns and infrastructure investments, the potential benefits of IoT and AI in healthcare are substantial. This paper presents a comprehensive framework for integrating these technologies into hospital operations, highlighting their impact on efficiency and patient care. The findings suggest that IoT and AI can significantly enhance hospital performance, paving the way for a smarter healthcare system.

Secure Patient Data Monitoring and Efficient Routing Optimization using a Hyperelliptic Curve Cryptography with Fuzzy-based Priority in WBSN

Aims and Background: Wireless Body Sensor Network (WBSN) technology is one of the major research areas in the medical and entertainment industries. A wireless sensor network (WSN) is a dense sensor network that senses environmental conditions, processes, and outgoing data at the sink node. A WBSN develops patient monitoring systems that provide the flexibility and mobility needed to monitor patient health. In data communications, it is difficult to find flexible optical routing paths, switching capabilities, and packet processing in the composition of optical networks. Information-centric networks (ICNs) are a new network model and are different from information- centric models. The priority of the information-centric model is the communication network. Objectives: In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. Methodology: A fuzzy–based Priority Aware Data Sharing (FPADS) method is introduced to schedule the priority data and monitor the transmission length. The child node adjusts the transmission speed of the cluster head with the help of a fuzzy logic controller (FLC). Results: The proposed model estimated the traffic load of the child node and the priority of the different amounts of data to be transmitted. The principle of scheduling data packets to be developed is based on the precedence of the data with the lowest transmit length in the network. Conclusion: The proposed FPADS performance increases in terms of scheduling time utilisation, traffic distribution, and mean delay. Simulations have been done using NS2, and the outcomes have shown that the proposed methodology is efficient and improves the overall QoS of the system.

Classification of Laboratory Test Outcomes for Maintenance Hemodialysis Patients Using Cellular Bioelectrical Measurements.

End-stage kidney disease (ESKD) patients often face complications like anemia, malnutrition, and cardiovascular issues. Serological tests, which are uncomfortable and not frequently conducted, assist in medical assessments. A non-invasive, convenient method for determining these test results would be beneficial for monitoring patient health. This study develops machine learning models to estimate key serological test results using non-invasive cellular bioelectrical impedance measurements, a routine procedure for ESKD patients. The study employs two machine learning models, Support Vector Machine (SVM) and Random Forest (RF), to determine key serological tests by classifying cell bioelectrical indicators. Data from 688 patients, comprising 3,872 biochemical-bioelectrical records, were used for model validation. Both SVM and RF models effectively categorized key serological results (albumin, phosphorus, parathyroid hormone) into low, normal, and high. RF generally outperformed SVM, except in classifying calcium levels in women. The machine learning models effectively classified serological test results for maintenance hemodialysis patients using cellular bioelectrical indicators, therefore can help in making judgments about physicochemical indicators using electrical signals, thereby reducing the frequency of serological tests.

Contributing Factors to the Challenges of Patient Health Monitoring in Rural Healthcare Units: Basis for a Proposed Integrated Patient Management Portal

In the current era of abundant information, technological advancements have greatly impacted various fields particularly the healthcare sector. This research explores the difficulties encountered by rural healthcare facilities in Barangay Poblacion, Bacuag, Surigao del Norte, in monitoring patient health and suggests implementing a remote patient management platform to enhance healthcare delivery. Using a mixed-method approach, the study involved 37 participants, predominantly young females. The research identified key challenges such as limited access to healthcare facilities, a shortage of medical staff, communication issues, and insufficient healthcare resources. Although patient registration and monitoring of vital signs were relatively effective, challenges remained in scheduling appointments, availability of medical tests, and patient education. The overall positive perception of the current system (average weighted mean of 3.59) indicates room for improvement through the proposed remote patient management platform. This platform aims to improve real-time monitoring, data accuracy, and communication between patients and healthcare providers, thereby enhancing healthcare outcomes in rural areas

IoT and AI Based Remote Patient Health Monitoring System

IoT and AI Based Remote Patient Health Monitoring System

Wireless Body Sensor Networks for Real-Time Healthcare Monitoring: A Cost-Effective and Energy-Efficient Approach

Background: The continuous and accurate monitoring of physiological signals is critical for timely medical interventions, particularly in the context of chronic disease management and elderly care. The advent of Wireless Body Sensor Networks (WBSN) and Patient Health Monitoring Systems (PHMS) promises to revolutionize healthcare by integrating real-time data collection with advanced processing and communication technologies, enabling proactive medical responses through the Internet of Things (IoT). Methods: In this study, a WBSN-PHMS framework is proposed, leveraging a network of biomedical sensors to continuously monitor vital signs such as temperature and heart rate. The system includes a gateway node that wirelessly transmits sensor data to a cloud-based storage and processing system. Physiological data are processed in real-time using advanced algorithms to detect anomalies and trigger alerts. The health status is modeled using a function g(v,w(v),∅−1(v(w))−∂i(v,∂))g(v, w(v), \varnothing^{-1}(v(w)) - \partial_i(v, \partial))g(v,w(v),∅−1(v(w))−∂i?(v,∂)), which integrates patient-specific factors and real-time physiological states. The processed data are communicated efficiently through IoT platforms, ensuring that healthcare professionals receive timely alerts via mobile applications and specialized dashboards. Results: The WBSN-PHMS demonstrated high accuracy in detecting abnormal temperature and heart rate patterns, achieving detection rates of 98.61% and 99.43%, respectively. Additionally, the system improved overall patient health monitoring by 98.42%, offering a significant advancement in patient safety and healthcare efficiency. The cost-effectiveness of the system was established with a 96.21% improvement, while energy consumption was reduced by 23%, underscoring the sustainability of the technology. Conclusion: The WBSN-PHMS proves to be a reliable and efficient healthcare monitoring system that enhances patient outcomes through real-time data analysis and rapid medical responses. The system's ability to continuously monitor and analyze physiological signals in a non-intrusive manner positions it as a vital tool in modern medical practice, with the potential to revolutionize remote patient care and monitoring.

ChatGPT in orthodontics: limitations and possibilities

Abstract Artificial Intelligence (AI) simulates human intelligence in machines capable of rational response and actions. AI’s growth in healthcare, particularly in dentistry, has been remarkable in aiding clinical practices from appointment scheduling to treatment outcome predictions. In orthodontics, AI effectively handles tasks such as automated landmark detection, diagnosing orthodontic needs, assessing maxillary constriction, and selecting appliances. ChatGPT, an AI-based program by OpenAI, excels in generating human-like responses, making it useful across many applications, including those in dentistry. AI enhances personalised patient communication, supports diagnosis and decision-making, digitises patient data, and aids in disease prevention and treatment. In addition, ChatGPT offers support in personalised care, billing, and patient health monitoring. Patients benefit from its help in finding providers, scheduling initial appointments, accessing health information, improving healthcare access and service effectiveness. However, there are limitations. ChatGPT sometimes produces nonsensical responses and poses privacy risks associated with patient data. Generated medical advice might not therefore match professional expertise. Despite these issues, if trained properly, ChatGPT could significantly enhance medical knowledge dissemination. Future studies should weigh the benefits of AI against its risks, and users must verify AI-generated information with reliable sources to ensure effective patient care.