Continuous Monitoring Of Patients Research Articles

Machine Learning Techniques for Developing Remotely Monitored Central Nervous System Biomarkers Using Wearable Sensors: A Narrative Literature Review.

Central nervous system (CNS) disorders benefit from ongoing monitoring to assess disease progression and treatment efficacy. Mobile health (mHealth) technologies offer a means for the remote and continuous symptom monitoring of patients. Machine Learning (ML) techniques can process and engineer mHealth data into a precise and multidimensional biomarker of disease activity. This narrative literature review aims to provide an overview of the current landscape of biomarker development using mHealth technologies and ML. Additionally, it proposes recommendations to ensure the accuracy, reliability, and interpretability of these biomarkers. This review extracted relevant publications from databases such as PubMed, IEEE, and CTTI. The ML methods employed across the selected publications were then extracted, aggregated, and reviewed. This review synthesized and presented the diverse approaches of 66 publications that address creating mHealth-based biomarkers using ML. The reviewed publications provide a foundation for effective biomarker development and offer recommendations for creating representative, reproducible, and interpretable biomarkers for future clinical trials. mHealth-based and ML-derived biomarkers have great potential for the remote monitoring of CNS disorders. However, further research and standardization of study designs are needed to advance this field. With continued innovation, mHealth-based biomarkers hold promise for improving the monitoring of CNS disorders.

Effectiveness of electronic patient reporting outcomes, by a digital telemonitoring platform, for prostate cancer care: the Protecty study.

The benefits of Electronic Patient -Reported Outcomes (e-PRO) for telemonitoring are well established, allowing early detection of illnesses and continuous monitoring of patients. The primary objective of the PROTECTY study was to assess the compliance with patient use of the telemonitoring platform Cureety. An exploratory objective was to assess if the first-month health status is a prognostic factor of progression free-survival (PFS) and overall survival (OS) for prostate cancer patient. This prospective study was conducted at the Military Hospital Bégin on prostate cancer patients. Patients were allowed to respond to a symptomatology questionnaire based on CTCAE v.5.0, personalized to their pathology and treatment. An algorithm evaluates the health status of the patient based on the reported adverse events, with a classification into 2 different states: Good Health Status (GHS) and Poor Health status (PHS). Sixty-one patients were enrolled between July 1st, 2020 and September 30th, 2021. The median age was 74.0 (range 58.0-94.0). 78% presented a metastatic stage, and the most represented cancer was mHSPC. Overall, 2,457 questionnaires were completed by the patients, 4.0% resulted in a health classification in to monitor or critical state. 87% of patients were classified in the GHS group. The compliance was 72% in the overall population during the first month, 71% in GHS group and 75% in PHS group. The median follow-up was 8 months. PFS at 6 months was 84% in GHS group vs. 57% in PHS group, p = 0.19. OS at 6 months was 98% in GHS group vs. 83% in PHS group, p = 0.31. Our study showed that compliance was satisfactory. The feasibility of remote monitoring for prostate cancer patients means that they should benefit from its implementation. Our study is also the first to assess the correlation between treatment tolerance and survival. The initial results suggest that e-PRO assessment could help identify in the early stages the patients that require further health assessment and potential therapeutic changes. While further follow-up of more patients will be required, our study highlights the importance of e-PRO in cancer patient care.

InSEption: A Robust Mechanism for Predicting FoG Episodes in PD Patients

The integration of IoT and deep learning provides the opportunity for continuous monitoring and evaluation of patients’ health status, leading to more personalized treatment and improved quality of life. This study explores the potential of deep learning to predict episodes of freezing of gait (FoG) in Parkinson’s disease (PD) patients. Initially, a literature review was conducted to determine the state of the art; then, two inception-based models, namely LN-Inception and InSEption, were introduced and tested using the Daphnet dataset and an additional novel medium-sized dataset collected from an IMU (inertial measuring unit) sensor. The results show that both models performed very well, outperforming or achieving performance comparable to the state-of-the-art. In particular, the InSEption network showed exceptional performance, achieving a 6% increase in macro F1 score compared to the inception-only-based counterpart on the Daphnet dataset. In a newly introduced IMU dataset, InSEption scored 97.2% and 98.6% in terms of F1 and AUC, respectively. This can be attributed to the added squeeze and excitation blocks and the domain-specific oversampling methods used for training. The benefits of using the Inception mechanism for signal data and its potential for integration into wearable IoT are validated.

Advanced Critical Care Monitoring with Alert and Auto Intelligence System

Abstract: With great advancements in lifestyle and practices along with pandemic outbreaks such as COVID 19, the number of patients in hospitals is increasing which is a major concern globally. To overcome this situation, an IoT platform likely to provide the scalability required for this challenge is support the continuous monitoring of patients on a global scale. The technical specifications used are Arduino IDE, ThingSpeak Cloud, Max 30100, Temperature sensor, and ESP8266. Arduino kit is an open source platform that has an easy to use software and hardware. It can read inputs from any device. ThingSpeak is an IOT analytics service that allows the collection of data, picturing, and examine live data streams in the cloud. ESP8266 is a WiFi Module that gives the microcontroller access to the Wi-Fi network. LM35 is a series of devices used for measuring temperature with accurate precision. Its output voltage is linearly proportional to the temperature in Centigrade units. Hence, this project paper is focused to analyse the vitals of patients by using wearable, safe, and modern sensors to observe various patients who are affected by cardiovascular disease.

An effective ECG signal compression algorithm with self controlled reconstruction quality

Electrocardiogram (ECG) signals are frequently used in the continuous monitoring of heart patients. These recordings generate huge data, which is difficult to store or transmit in telehealth applications. In the above context, this work proposes an efficient novel compression algorithm by integrating the tunable-Q wavelet transform (TQWT) with coronavirus herd immunity optimizer (CHIO). Additionally, this algorithm facilitates the self-adaptive nature to regulate the reconstruction quality by limiting the error parameter. CHIO is a human perception-based algorithm, used to select optimum TQWT parameters, where decomposition level of TQWT is optimized for the first time in the field of ECG compression. The obtained transform coefficients are then thresholded, quantized, and encoded to improve the compression further. The proposed work is tested on MIT-BIH arrhythmia database. The compression and optimization performance using CHIO is also compared with well-established optimization algorithms. The compression performance is measured in terms of compression ratio, signal-to-noise ratio, percent root mean square difference, quality score, and correlation coefficient.

Synergy through integration of digital cognitive tests and wearable devices for mild cognitive impairment screening.

Advances in mobile computing platforms and the rapid development of wearable devices have made possible the continuous monitoring of patients with mild cognitive impairment (MCI) and their daily activities. Such rich data can reveal more subtle changes in patients' behavioral and physiological characteristics, providing new ways to detect MCI anytime, anywhere. Therefore, we aimed to investigate the feasibility and validity of digital cognitive tests and physiological sensors applied to MCI assessment. We collected photoplethysmography (PPG), electrodermal activity (EDA) and electroencephalogram (EEG) signals from 120 participants (61 MCI patients, 59 healthy controls) during rest and cognitive testing. The features extracted from these physiological signals involved the time domain, frequency domain, time-frequency domain and statistics. Time and score features during the cognitive test are automatically recorded by the system. In addition, selected features of all modalities were classified by tenfold cross-validation using five different classifiers. The experimental results showed that the weighted soft voting strategy combining five classifiers achieved the highest classification accuracy (88.9%), precision (89.9%), recall (88.2%), and F1 score (89.0%). Compared to healthy controls, the MCI group typically took longer to recall, draw, and drag. Moreover, during cognitive testing, MCI patients showed lower heart rate variability, higher electrodermal activity values, and stronger brain activity in the alpha and beta bands. It was found that patients' classification performance improved when combining features from multiple modalities compared to using only tablet parameters or physiological features, indicating that our scheme could reveal MCI-related discriminative information. Furthermore, the best classification results on the digital span test across all tasks suggest that MCI patients may have deficits in attention and short-term memory that came to the fore earlier. Finally, integrating tablet cognitive tests and wearable sensors would provide a new direction for creating an easy-to-use and at-home self-check MCI screening tool.

Deep Learning Prediction Model for Patient Survival Outcomes in Palliative Care Using Actigraphy Data and Clinical Information.

(1) Background: Predicting the survival of patients in end-of-life care is crucial, and evaluating their performance status is a key factor in determining their likelihood of survival. However, the current traditional methods for predicting survival are limited due to their subjective nature. Wearable technology that provides continuous patient monitoring is a more favorable approach for predicting survival outcomes among palliative care patients. (2) Aims and objectives: In this study, we aimed to explore the potential of using deep learning (DL) model approaches to predict the survival outcomes of end-stage cancer patients. Furthermore, we also aimed to compare the accuracy of our proposed activity monitoring and survival prediction model with traditional prognostic tools, such as the Karnofsky Performance Scale (KPS) and the Palliative Performance Index (PPI). (3) Method: This study recruited 78 patients from the Taipei Medical University Hospital's palliative care unit, with 66 (39 male and 27 female) patients eventually being included in our DL model for predicting their survival outcomes. (4) Results: The KPS and PPI demonstrated an overall accuracy of 0.833 and 0.615, respectively. In comparison, the actigraphy data exhibited a higher accuracy at 0.893, while the accuracy of the wearable data combined with clinical information was even better, at 0.924. (5) Conclusion: Our study highlights the significance of incorporating clinical data alongside wearable sensors to predict prognosis. Our findings suggest that 48 h of data is sufficient for accurate predictions. The integration of wearable technology and the prediction model in palliative care has the potential to improve decision making for healthcare providers and can provide better support for patients and their families. The outcomes of this study can possibly contribute to the development of personalized and patient-centered end-of-life care plans in clinical practice.

Simultaneous measurements of multiple vital signs using non-contact frequency modulated continuous wave radar monitoring

In this study, a short-range mmWave radar is proposed for obtaining non-contact, non-invasive, and high-accuracy measurements of multiple vital signs. The system can extract the heart rate (HR), respiration rate (RR), and blood pressure (BP). A compact radar sensing unit operating at 122 GHz was used to transmit and receive signals reflected off a patient’s chest under breathing and non-breathing (breath-holding) conditions. Algorithms were developed for extracting the BP values. The BP values were determined as 129/80 and 129/83 for the breathing and non-breathing signals, respectively. Furthermore, the extracted data were used to calculate the HR and RR based on the period of each data cycle and a fast Fourier transform (FFT). Both techniques yielded an RR of 12 breaths per minute. The HR values for breathing and non-breathing were 68 and 53 beats per minute, respectively. Furthermore, the HRs computed from the FFT of the breathing and non-breathing signals were 55 and 53 beats per minute, respectively. All values were consistent with the reference values. Thus, the system shows feasibility for low-cost, highly accurate, and continuous patient monitoring.

Towards continuous EIT monitoring for hemorrhagic stroke patients.

The practical implementation of continuous monitoring of stroke patients by Electrical Impedance Tomography (EIT) is addressed. In a previous paper, we have demonstrated EIT sensitivity to cerebral hemodynamics, using scalp-mounted electrodes, very low-noise measurements, and a novel image reconstruction method. In the present paper, we investigate the potential to adapt that system for clinical application, by using 50% fewer electrodes and by incorporating into the measurement protocol an additional high-frequency measurement to provide an effective reference. Previously published image reconstruction methods for multi-frequency EIT are substantially improved by exploiting the forward calculations enabled by the detailed head model, particularly to make the referencing method more robust and to attempt to remove the effects of modelling error. Images are presented from simulation of a typical hemorrhagic stroke and its growth. These results are encouraging for exploration of the potential clinical benefit of the methodology in long-term monitoring of hemorrhagic stroke.

Инфекционный эндокардит, тромбоэмболия легочной артерии и прогрессирование трикуспидальной недостаточности — грозные осложнения имплантируемого внутрисердечного устройства (клиническое наблюдение)

INTRODUCTION: The number of patients with implantable cardiac devices (CDs) is steadily growing in the world and, in parallel with this, the number of complications associated with these devices, including infectious endocarditis (IE), arterial embolisms and mechanical damage to the heart valves. A case of patient P., 51 year old, diagnosed with IE of CD, pulmonary embolism and progression of tricuspid regurgitation to the 3rd degree with a previously implanted Sensia SR VVI pacemaker is presented. The peculiarities of this clinical case are highly probable existence of the source of infection (the patient suffered acute paraproctitis with unauthorized opening of the abscess without antibiotic therapy), severe comorbid pathology, the absence of fever throughout the entire follow-up period, the absence of visualized involvement of the valve apparatus with the presence of blood clots around the pacemaker electrodes, and the inability to remove infected pacemaker electrodes in the acute period for technical reasons. The difficulties of diagnosis and management of such patients in the conditions of the Regional Vascular Center without the technical possibilities for surgical treatment are discussed. CONCLUSION: This clinical case actualizes: the importance of informing patients with CDs about the need for both antibiotic prophylaxis in high-risk invasive interventions and antibiotic therapy in infectious diseases of any location and complexity; the importance of continuous monitoring of patients with CDs by a cardiologist and/or therapist with the maximum possible correction of all comorbidities; the reasonability of high attention of doctors of various specialties to the presence of IE in the patient even in the absence of fever and other typical clinical manifestations of the disease; the need for an effectively functioning logistics system for timely hospitalization of patients with IE in Cardiovascular Centers performing surgical interventions in the acute period of IE.

LTE-NBP with holistic UWB-WBAN approach for the energy efficient biomedical application.

Increased use of ultra-wideband (UWB) in biomedical applications based on wireless body area networks (WBAN) opens a variety of options in the field of biomedical research. WBAN may aid in the continuous health monitoring of patients while they go about their everyday lives. Many studies and researchers were conducted several experimentations in the same field for the performance improvement. This study covered the hybridization of UWB technology, as well as on-body, off-body, and human-body ultra-wideband communication (HB-UWB). In this paper, the parameters considered are throughput, energy consumption, energy efficiency, energy used, network survival and delay. An improved model for design and assessment of power-saving UWB-WBAN was developed in this paper. A novel protocol model was introduced in this paper, namely low-power traffic-aware emergency based narrowband protocol (LTE-NBP) to overcome the major drawbacks of emergency, critical data transmission, reliability and the power issues in UWB-WBAN. It's the emergency-based low-power traffic-aware narrowband protocol. It is based on the dual-band physical layer technology. The suggested protocol considered an aware traffic model and an emergency medium access control (MAC) protocol. The proposed model's performance was evaluated and compared with the related algorithms on different performance parameters. The improved model is found to be efficient in throughput, energy efficiency, energy consumption, and delay.

Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.

An ICU is a critical care unit that provides advanced medical support and continuous monitoring for patients with severe illnesses or injuries. Predicting the mortality rate of ICU patients can not only improve patient outcomes, but also optimize resource allocation. Many studies have attempted to create scoring systems and models that predict the mortality of ICU patients using large amounts of structured clinical data. However, unstructured clinical data recorded during patient admission, such as notes made by physicians, is often overlooked. This study used the MIMIC-III database to predict mortality in ICU patients. In the first part of the study, only eight structured variables were used, including the six basic vital signs, the GCS, and the patient's age at admission. In the second part, unstructured predictor variables were extracted from the initial diagnosis made by physicians when the patients were admitted to the hospital and analyzed using Latent Dirichlet Allocation techniques. The structured and unstructured data were combined using machine learning methods to create a mortality risk prediction model for ICU patients. The results showed that combining structured and unstructured data improved the accuracy of the prediction of clinical outcomes in ICU patients over time. The model achieved an AUROC of 0.88, indicating accurate prediction of patient vital status. Additionally, the model was able to predict patient clinical outcomes over time, successfully identifying important variables. This study demonstrated that a small number of easily collectible structured variables, combined with unstructured data and analyzed using LDA topic modeling, can significantly improve the predictive performance of a mortality risk prediction model for ICU patients. These results suggest that initial clinical observations and diagnoses of ICU patients contain valuable information that can aid ICU medical and nursing staff in making important clinical decisions.

A Blueprint design of optical-based wristband for non-invasive and continuous health status monitoring of sickle cell disease patients

Sickle cell disease is a group of health disorders that affect hemoglobin in red blood cells. Sickle cell disease causes multiple manifestations and complications, including infections, delayed growth, pain crisis, and multi-organs damage. These complications can disturb the patients' vital signs, including body temperature, heart rate, and oxygen saturation. The decline in hemoglobin level is another indication of sickle cell disease due to abnormal hemoglobin that distorts red blood cells resulting in sickle cell anemia. RBCs, also called erythrocytes, are found in the blood and responsible for carrying oxygen to the body tissues attached to the hemoglobin. Optical microscopy can detect and monitor sickle cell disease, but it requires a blood sample and offline analysis, which is a time-consuming process. There is currently no known technology available to provide non-invasive monitoring solutions for sickle cell disease patients' health. Therefore, a need arises for a non-invasive and continuous monitoring solution to continuously check on sickle cell disease patients, which can be transformed into a wearable monitoring device. The proposed optical wristband consists of optical sensors that provide non-invasive and continuous health status monitoring of sickle cell disease patients using key vital signs and hemoglobin levels. A comparative study was performed among 21 participants and equally divided into three groups (non-anemic, anemic, and sickle cell disease patients). The data was collected from optical sensors, Arduino used as a processor, and continuously monitors the patient's vital signs and hemoglobin levels. Abnormal reading of any parameter alerts the user of any unhealthy status, and the parameter's trend assists the clinician in patient assessment. The body temperature and oxygen saturation levels of the anemic patients were found in the normal ranges, but the heart rate of three patients and hemoglobin of all the participants were found in aberrant ranges. The vital signs and hemoglobin levels of all sickle cell disease patients were beyond the normal ranges and significantly different (p>0.001) than non-anemic and anemic groups. Therefore, these physiological parameters monitoring has clinical importance for sickle cell disease management and early treatment.

Correlation of Acceleration Curves in Gravitational Direction for Different Body Segments during High-Impact Jumping Exercises.

Osteoporosis is a common disease of old age. However, in many cases, it can be very well prevented and counteracted with physical activity, especially high-impact exercises. Wearables have the potential to provide data that can help with continuous monitoring of patients during therapy phases or preventive exercise programs in everyday life. This study aimed to determine the accuracy and reliability of measured acceleration data at different body positions compared to accelerations at the pelvis during different jumping exercises. Accelerations at the hips have been investigated in previous studies with regard to osteoporosis prevention. Data were collected using an IMU-based motion capture system (Xsens) consisting of 17 sensors. Forty-nine subjects were included in this study. The analysis shows the correlation between impacts and the corresponding drop height, which are dependent on the respective exercise. Very high correlations (0.83-0.94) were found between accelerations at the pelvis and the other measured segments at the upper body. The foot sensors provided very weak correlations (0.20-0.27). Accelerations measured at the pelvis during jumping exercises can be tracked very well on the upper body and upper extremities, including locations where smart devices are typically worn, which gives possibilities for remote and continuous monitoring of programs.

Wearable Vital Signs Monitoring for Patients With Asthma: A Review.

Worldwide,an estimated 461 000 people die from asthma attacks each year. While there remain treatments to alleviate asthma symptoms and reduce deaths, patient deterioration needs to be identified in sufficient time. To prevent asthma deterioration, patients need to be aware of personal and environmental triggers and monitor their asthma symptoms. The aim of this article is to provide a comprehensive review of the current state-of-the-art wearable sensors and devices that use vital signs for asthma patient monitoring and management. Among all vital signs, breathing rate and airflow sound are key indicators of asthmatic patients' health that can be measured directly using wearable sensors to provide continuous and constant patient monitoring or indirectly by estimations based on proven algorithms using electrocardiogram (ECG), photoplethysmogram (PPG), and chest movements. ECG and PPG signals are widely used in smart watches and chest bands, enabling easy integration of a more extensive body sensor framework for asthmatic exacerbation prediction. Other vital signs used in asthma patient monitoring include blood oxygen saturation, temperature, blood pressure, verbal sound, and pain responses. The use of wearable vital signs enabled a broad range of wearable sensor application scenarios for asthma monitoring and management.

Flexible, Implantable, Pulse Oximetry Sensors: Toward Long-Term Monitoring of Blood Oxygen Saturations

Pulse oximetry is the most widespread method of monitoring heart rate and blood oxygen levels in both clinical and non-clinical settings. Current devices are ridged, bulky, and suitable only for short-term use. While the next generation of devices are moving toward wearable technologies, this focus on being unobtrusive is insufficient. To enable continuous high quality long-term monitoring, implanted devices are required. These will eliminate interference from light and sensitivity to skin pigmentation, allow enhanced performance during movement, and have no concerns around percussive damage. Here, an inexpensive, ultra-flexible pulse oximetry probe is demonstrated. The hybrid devices are fabricated on 5 µm Parylene C using laser ablation to define the circuit, and integrate small, rigid optoelectronic components. These are demonstrated in vivo on anesthetized pigs. Both transmission mode, and the novel reflection mode, are shown to be effective geometries for this. The heart rate measured by these devices shows < 2% variance from concurrent peripheral pulse oximetry measurements, along with an average variance of around 2.5% attributed predominantly to differences between central and peripheral oxygen saturations. In addition, it is shown that when implemented directly on the femoral artery, these devices record a more acute response to the variation in oxygen intake compared to the peripheral measurements. Finally, the same devices are shown to have the potential for use in monitoring venous oxygen content. This could open up the possibility of continuous monitoring of arteriovenous oxygen difference. These devices are straightforward to produce, biocompatible, and can be easily implanted during cardiovascular surgery, offering a route toward long-term implantation for continuous patient monitoring.

Balancing Technological Advances with User Needs: User-centered Principles for AI-Driven Smart City Healthcare Monitoring

In recent years, the integration of artificial intelligence (AI) technologies has greatly benefited smart city healthcare, meeting the growing demand for affordable, efficient, and real-time healthcare services. Patient monitoring is one area where artificial intelligence has shown great promise. Improved health outcomes have been made possible by the advancement of AI-based monitoring systems, which enable more personalized and continuous patient monitoring. However, to fully maximize the benefits of these systems, a user-centered approach is essential, which prioritizes patients' needs and experiences while ensuring their privacy and autonomy are respected. This study focuses on the application of user-centered design principles in the development and deployment of AI-driven monitoring systems in smart city healthcare. Addressing the challenges and opportunities of AI-driven monitoring systems, the article considers issues such as privacy and security concerns, data accuracy, and user acceptance. Finally, some possible future directions to the challenges are suggested. A user-centered approach to AI monitoring systems is recommended for healthcare providers to enhance patient experience in smart city healthcare.

One-year survey of methadone-induced cardiac complications.

Methadone is one of the most useful opioids that can be used to achieve many therapeutic goals, and also it may be abused as an illicit drug. Methadone can cause different gastrointestinal, neurological, and cardiac complications. This study was performed to obtain a better understanding of the cardiac side effects of methadone in patients with methadone poisoning. This cross-sectional study was performed on 210 samples in Sina Hospital of Hamadan in a one-year period from March 2019 to March 2020. After assessing patients who had methadone poisoning and completing their demographic information and evaluation of changes in patients' EKGs, the data was collected and analyzed by SPSS 16 software. Out of 210 participants, 178 (84.8%) were males and the rest were females. The average age of the studied patients was 39.56 years old. The study found that 6.1% of methadone-poisoned patients were illiterate. It was found in this study that the most common cardiac complications of methadone intoxication were sinus tachycardia (20%), QT interval prolongation (6.64%), and sinus bradycardia (4.3%), respectively; nevertheless, 66.2% of patients did not have any EKG abnormalities. According to the findings, it is necessary to have continuous cardiac monitoring for patients with methadone intoxication and by transferring such findings to medical centers, steps can be taken to use methadone more intelligently.

A feasibility study using radio-frequency sensors to collect respiratory metrics in patients with chronic obstructive pulmonary disease.

Background: The symptom of dyspnea is commonly encountered in patients with underlying serious illness and can lead to distress and poor quality of life. In patients with chronic obstructive lung disease (COPD), the prevalence is reported in up to 95% of patients. With the growth in sensor technologies, continuous monitoring of respiratory metrics provides an opportunity to better understand the relationship between patient-reported dyspnea and objective respiratory measures. Aims: To assess the feasibility of implementing a radio-frequency (RF) sensor in patients with COPD and describe the relationship between dyspnea and respiratory metrics in patients with COPD when compared to healthy controls. Methods: A prospective cohort study was conducted to collect data on dyspnea scores and respiratory metrics in patients with COPD and healthy controls while conducting a walking test using a wearable RF sensor. Results: Of the 12 COPD patients and 15 healthy controls recruited, all participants completed the modified incremental shuttle walking test while wearing the RF sensor; there was no attrition. For every one-point increase in the dyspnea score, there was a mean 1.94 increase in the respiratory rate per minute in the COPD group as compared to a 1.09 increase in respiratory rate in the healthy control group. Conclusion: Preliminary data demonstrate the potential of using the RF sensors to track respiratory metrics in COPD patients and healthy adults. As this technology develops, it shows considerable promise and could provide significant implications regarding the use of non-invasive continuous monitoring for patients with lung disease.

Clinical Fusion for Real-Time Complex QRS Pattern Detection in Wearable ECG Using the Pan-Tompkins Algorithm

This scientific paper presents a novel approach of real-time signal analysis in electrocardiogram (ECG) monitoring systems, focusing on the integration of device design,algorithm implementation for accurate measurement and interpretation of heart activity. The proposed system leverages a low-cost framework, employing a microcontroller and Arduino programming language for raw ECG data acquisition, while utilizing the AD8232 sensor and ESP8266 Node MCU for continuous patient monitoring. The acquired data is processed, stored, and analyzed using the Pan-Tompkins algorithm, which effectively filters and analyzes heart signals, including noise reduction and QRS complex detection. Two case studies involving a healthy individual and a patient with Myocarditis were conducted to demonstrate the effectiveness of the system. The integration of device design and algorithm development in ECG analysis is emphasized, highlighting the affordability, wearability, and potential for continuous monitoring and early detection of heart conditions. By successfully mitigating noise-related challenges, the implementation of the Pan algorithm enables accurate signal analysis. This interdisciplinary research contributes to the advancement of ECG interpretation and underscores the significance of clinical fusion between designed systems and applied algorithms on real cases. The performance of two Pan-Tompkins based QRS complex detection algorithms was systematically analyzed, offering valuable insights for their reasonable utilization.