Smart ECG Monitoring Patch with Built-in R-Peak Detection for Long-Term HRV Analysis

Usefulness of the heart rate variability test in predicting intradialytic hypotension in patients undergoing chronic haemodialysis

Background

Intradialytic hypotension (IDH) is the primary complication of haemodialysis (HD); however, its diverse pathophysiology and inconsistent definitions complicate its prediction. Despite attempts using the heart rate variability (HRV) test for IDH prediction, studies on its usefulness for predicting IDH diagnosed per the nadir 90 criterion are lacking. We aimed to evaluate HRV test efficacy and reproducibility in predicting IDH based on the nadir 90 criterion.

Methods

Seventy patients undergoing HD participated in this multicentre prospective observational study. The HRV test was performed during non-HD periods and IDH was monitored during 12 HD sessions. IDH was diagnosed according to the nadir 90 criterion, defined as a decrease in systolic blood pressure of ≤90 mmHg during HD. After monitoring, the HRV test was repeated. An HRV-IDH index was developed using multivariate logistic regression analysis employing HRV test parameters. The predictive power of the HRV-IDH index was analysed using the area under the receiver operating characteristics curve (AUROC). Reproducibility was evaluated using correlation analysis of two HRV tests on the same patient.

Results

There were 37 and 33 patients in the IDH and non-IDH groups, respectively. The HRV-IDH index predicted IDH occurrence with AUROCs of 0.776 and 0.803 for patients who had experienced at least one or repeated IDH episodes, respectively. Spearman's correlation coefficient for HRV-IDH indices was 0.859 for the first and second HRV tests.

Conclusions

The HRV test holds promise for predicting IDH, particularly for patients with recurring IDH diagnosed based on the nadir 90 criterion.

Fast Parabolic Fitting: An R-Peak Detection Algorithm for Wearable ECG Devices

Heart diseases rank among the most fatal health concerns globally, with the majority being preventable through early diagnosis and effective treatment. Electrocardiogram (ECG) analysis is critical in detecting heart diseases, as it captures the heart's electrical activities. For continuous monitoring, wearable electrocardiographic devices must ensure user comfort over extended periods, typically 24 to 48 h. These devices demand specialized algorithms with low computational complexity to accommodate memory and power consumption constraints. One of the most crucial aspects of ECG signals is accurately detecting heartbeat intervals, specifically the R peaks. In this study, we introduce a novel algorithm designed for wearable devices, offering two primary attributes: robustness against noise and low computational complexity. Our algorithm entails fitting a least-squares parabola to the ECG signal and adaptively shaping it as it sweeps through the signal. Notably, our proposed algorithm eliminates the need for band-pass filters, which can inadvertently smooth the R peaks, making them more challenging to identify. We compared the algorithm's performance using two extensive databases: the meta-database QT database and the BIH-MIT database. Importantly, our method does not necessitate the precise localization of the ECG signal's isoelectric line, contributing to its low computational complexity. In the analysis of the QT database, our algorithm demonstrated a substantial advantage over the classical Pan-Tompkins algorithm and maintained competitiveness with state-of-the-art approaches. In the case of the BIH-MIT database, the performance results were more conservative; they continued to underscore the real-world utility of our algorithm in clinical contexts.

Automated electrocardiogram signal quality assessment based on Fourier analysis and template matching

We developed and tested a novel template matching approach for signal quality assessment on electrocardiogram (ECG) data. A computational method was developed that uses a sinusoidal approximation to the QRS complex to generate a correlation value at every point of an ECG. The strength of this correlation can be numerically adapted into a 'score' for each segment of an ECG, which can be used to stratify signal quality. The algorithm was tested on lead II ECGs of intensive care unit (ICU) patients admitted to the Mount Sinai Hospital (MSH) from January to July 2020 and on records from the MIT BIH arrhythmia database. The algorithm was found to be 98.9% specific and 99% sensitive on test data from the MSH ICU patients. The routine performs in linear O(n) time and occupies O(1) heap space in runtime. This approach can be used to lower the burden of pre-processing in ECG signal analysis. Given its runtime (O(n)) and memory (O(1)) complexity, there are potential applications for signal quality stratification and arrhythmia detection in wearable devices or smartphones.

Assessment of a new KoMaWo electrode-patch configuration accuracy and review of the literature

INTRODUCTION

Standard 12‑lead electrocardiogram (ECG) is a basic element of routine everyday clinical practice. Traditional cardiac monitoring devices are associated with considerable limitations. Adhesive patches, novel digital solutions, may become a useful diagnostic tool for several cardiovascular diseases.

MATERIALS AND METHODS

We propose a new variation of ECG electrodes positioning called KoMaWo. 15 consecutive patients presenting with ST segment deviations due to coronary artery disease were enrolled. The accuracy and utility of the new configuration was assessed and compared with the Mason-Likar configuration, as well as with a standard 12‑lead ECG recording. The scans were blinded and interpreted by two independent cardiologists.

RESULTS

There were no statistically significant differences in morphology, as well as in the duration of individual waves, complexes, segments, and intervals between the scans obtained using all three methods. In a subgroup analysis, with regard to age, body mass and left ventricle ejection fraction (LVEF), KoMaWo was non-inferior to standard ECG with a 0.2 mm margin.

DISCUSSION

The role of traditional cardiac monitoring devices is recognized as the gold standard of patient management. However, certain limitations should be considered. Adhesive patches are light-weight, well-tolerated and do not interfere with daily activities of patients. These novel devices allow for extended monitoring, facilitating increased diagnostic accuracy, regarding cardiac arrhythmias.

CONCLUSIONS

The KoMaWo configuration is not inferior to standard electrode placement, nor to Mason-Likar configuration, including its ability to capture ST segment deviations. Adhesive patches may become a valid alternative for traditional cardiac monitoring methods.

Consumer Wearables for Patient Monitoring in Otolaryngology: A State of the Art Review

OBJECTIVE

Consumer wearables, such as the Apple Watch or Fitbit devices, have become increasingly commonplace over the past decade. The application of these devices to health care remains an area of significant yet ill-defined promise. This review aims to identify the potential role of consumer wearables for the monitoring of otolaryngology patients.

DATA SOURCES

PubMed.

REVIEW METHODS

A PubMed search was conducted to identify the use of consumer wearables for the assessment of clinical outcomes relevant to otolaryngology. Articles were included if they described the use of wearables that were designed for continuous wear and were available for consumer purchase in the United States. Articles meeting inclusion criteria were synthesized into a final narrative review.

CONCLUSIONS

In the perioperative setting, consumer wearables could facilitate prehabilitation before major surgery and prediction of clinical outcomes. The use of consumer wearables in the inpatient setting could allow for early recognition of parameters suggestive of poor or declining health. The real-time feedback provided by these devices in the remote setting could be incorporated into behavioral interventions to promote patients' engagement with healthy behaviors. Various concerns surrounding the privacy, ownership, and validity of wearable-derived data must be addressed before their widespread adoption in health care.

IMPLICATIONS FOR PRACTICE

Understanding how to leverage the wealth of biometric data collected by consumer wearables to improve health outcomes will become a high-impact area of research and clinical care. Well-designed comparative studies that elucidate the value and clinical applicability of these data are needed.

Towards Pharma 4.0 in clinical trials: A future-orientated perspective

Pharma 4.0, a technology ecosystem in drug development analogous to Industry 4.0 in healthcare, is transforming the traditional approach to drug discovery and development, aligning product quality with less time to market, and creating intelligent stakeholder networks through effective collaborations. The wide range of potential Pharma 4.0 networks have produced several conceptualizations, which have led to a lack of clarity and definition. The main emphasis of this paper is on the clinical trial stage of drug development in the Pharma 4.0 era. It highlights the merged computerized technologies that are currently used in clinical research, and proposes a framework for integrating Pharma 4.0 technologies. The impact of and barriers to employing the proposed framework are discussed, highlighting its potential and some future research applications.

Current Status and Future Challenges of Sleep Monitoring Systems: Systematic Review

Background

Sleep is essential for human health. Considerable effort has been put into academic and industrial research and in the development of wireless body area networks for sleep monitoring in terms of nonintrusiveness, portability, and autonomy. With the help of rapid advances in smart sensing and communication technologies, various sleep monitoring systems (hereafter, sleep monitoring systems) have been developed with advantages such as being low cost, accessible, discreet, contactless, unmanned, and suitable for long-term monitoring.

Objective

This paper aims to review current research in sleep monitoring to serve as a reference for researchers and to provide insights for future work. Specific selection criteria were chosen to include articles in which sleep monitoring systems or devices are covered.

Methods

This review investigates the use of various common sensors in the hardware implementation of current sleep monitoring systems as well as the types of parameters collected, their position in the body, the possible description of sleep phases, and the advantages and drawbacks. In addition, the data processing algorithms and software used in different studies on sleep monitoring systems and their results are presented. This review was not only limited to the study of laboratory research but also investigated the various popular commercial products available for sleep monitoring, presenting their characteristics, advantages, and disadvantages. In particular, we categorized existing research on sleep monitoring systems based on how the sensor is used, including the number and type of sensors, and the preferred position in the body. In addition to focusing on a specific system, issues concerning sleep monitoring systems such as privacy, economic, and social impact are also included. Finally, we presented an original sleep monitoring system solution developed in our laboratory.

Results

By retrieving a large number of articles and abstracts, we found that hotspot techniques such as big data, machine learning, artificial intelligence, and data mining have not been widely applied to the sleep monitoring research area. Accelerometers are the most commonly used sensor in sleep monitoring systems. Most commercial sleep monitoring products cannot provide performance evaluation based on gold standard polysomnography.

Conclusions

Combining hotspot techniques such as big data, machine learning, artificial intelligence, and data mining with sleep monitoring may be a promising research approach and will attract more researchers in the future. Balancing user acceptance and monitoring performance is the biggest challenge in sleep monitoring system research.

Compressed Segmented Beat Modulation Method using Discrete Cosine Transform

Currently used 24-hour electrocardiogram (ECG) monitors have been shown to skip detecting arrhythmias that may not occur frequently or during standardized ECG test. Hence, online ECG processing and wearable sensing applications have been becoming increasingly popular in the past few years to solve a continuous and long-term ECG monitoring problem. With the increase in the usage of online platforms and wearable devices, there arises a need for increased storage capacity to store and transmit lengthy ECG recordings, offline and over the cloud for continuous monitoring by clinicians. In this work, a discrete cosine transform (DCT) compressed segmented beat modulation method (SBMM) is proposed and its applicability in case of ambulatory ECG monitoring is tested using Massachusetts Institute of Technology-Beth Israel Deaconess Medical Center (MIT-BIH) ECG Compression Test Database containing Holter tape normal sinus rhythm ECG recordings. The method is evaluated using signal-to-noise (SNR) and compression ratio (CR) considering varying levels of signal energy in the reconstructed ECG signal. For denoising, an average SNR of 4.56 dB was achieved representing an average overall decline of 1.68 dBs (37.9%) as compared to the uncompressed signal processing while 95 % of signal energy is intact and quantized at 6 bits for signal storage (CR=2) compared to the original 12 bits, hence resulting in 50% reduction in storage size.

Integrating sleep, neuroimaging, and computational approaches for precision psychiatry

In advancing precision psychiatry, we focus on what imaging technology and computational approaches offer for the future of diagnostic subtyping and personalized tailoring of interventions for sleep impairment in mood and anxiety disorders. Current diagnostic criteria for mood and anxiety tend to lump different forms of sleep disturbance together. Parsing the biological features of sleep impairment and brain circuit dysfunction is one approach to identifying subtypes within these disorders that are mechanistically coherent and offer targets for intervention. We focus on two large-scale neural circuits implicated in sleep impairment and in mood and anxiety disorders: the default mode network and negative affective network. Through a synthesis of existing knowledge about these networks, we pose a testable framework for understanding how hyper- versus hypo-engagement of these networks may underlie distinct features of mood and sleep impairment. Within this framework we consider whether poor sleep quality may have an explanatory role in previously observed associations between network dysfunction and mood symptoms. We expand this framework to future directions including the potential for connecting circuit-defined subtypes to more distal features derived from digital phenotyping and wearable technologies, and how new discovery may be advanced through machine learning approaches.

Low Resource Complexity R-peak Detection Based on Triangle Template Matching and Moving Average Filter

A novel R-peak detection algorithm suitable for wearable electrocardiogram (ECG) devices is proposed with four objectives: robustness to noise, low latency processing, low resource complexity, and automatic tuning of parameters. The approach is a two-pronged algorithm comprising (1) triangle template matching to accentuate the slope information of the R-peaks and (2) a single moving average filter to define a dynamic threshold for peak detection. The proposed algorithm was validated on eight ECG public databases. The obtained results not only presented good accuracy, but also low resource complexity, all of which show great potential for detection R-peaks in ECG signals collected from wearable devices.

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.

Predicting intradialytic hypotension using heart rate variability

This study aimed to identify whether a new method using heart rate variability (HRV) could predict intradialytic hypotension (IDH) for one month in advance for patients undergoing prevalent hemodialysis. A total 71 patients were enrolled, and baseline clinical characteristics and laboratory results were collected when HRV was measured, then, the frequency of IDH was collected during the observation period. HRV parameters included heart rate, R-R interval, the standard deviation of N-N interval, the square root of the mean squared differences of successive NN intervals, very low frequency, low frequency, high frequency, total power, and low frequency/high frequency ratio. During the one-month observation period, 28 patients experienced 85 cases of IDH (10.0% of a total 852 dialysis sessions). Among the clinical and laboratory parameters, ultrafiltration rate, prior history of diabetes, coronary artery disease, or congestive heart failure, age, intact parathyroid hormone level, and history of antihypertensive drug use were integrated into the multivariate model, referred to as a basic model, which showed significant ability to predict IDH (the area-under-curve [AUC], 0.726; p = 0.002). In HRV parameters, changes between the early and middle phases of hemodialysis (referred to Δ) were identified as significant independent variables. New models were built from the combination of Δ values with the basic model. Among them, a model with the highest AUC value (AUC, 804; p < 0.001) was compared to the basic model and demonstrated improved performance when HRV parameters were used (p = 0.049). Based on our results, it is possible that future IDH might be predicted more accurately using HRV.

Study of challenges to utilise mobile-based health care monitoring systems: A descriptive literature review

Mobile health encompasses remote and wireless applications to provide health services. Despite the advantages of applying mobile-based monitoring systems, there are challenges and limitations; understanding the challenges may assist in identifying available solutions and optimising decision-making to apply mHealth technologies more practically. This study aimed to investigate the main challenges related to mHealth-based systems for health monitoring purposes. This review was carried out through investigation of English evidence from four databases, including Scopus, PubMed, Embase, and Web of Science, using a defined search strategy from 2013 to 2017. Two independent researchers reviewed the results based on PRISMA guidelines, and data was categorised using a bottom-up approach to reach a framework for the most general challenges. Among the 105 papers obtained, eight works were selected. The revealed challenges were categorised into six main branches across a tree (with 55 nodes, four levels) including user-related, infrastructure, process, management, resource and training challenges. Identifying the resolvable and preventable challenges, such as those related to training, design might play a crucial role in preventing loss of resources and in growing the success rate of a project, particularly if considered in national level projects.

Heart Rate Variability: An Old Metric with New Meaning in the Era of using mHealth Technologies for Health and Exercise Training Guidance. Part One: Physiology and Methods

The autonomic nervous system plays a major role in optimising function of the cardiovascular (CV) system, which in turn has important implications for CV health. Heart rate variability (HRV) is a measurable reflection of this balance between sympathetic and parasympathetic tone and has been used as a marker for cardiac status and predicting CV outcomes. Recently, the availability of commercially available heart rate (HR) monitoring systems has had important CV health implications and permits ambulatory CV monitoring on a scale not achievable with traditional cardiac diagnostics. The focus of the first part of this two-part review is to summarise the physiology of HRV and to describe available technologies for HRV monitoring. Part two will present HRV measures for assessing CV prognosis and athletic training.

Analysis of the Relationship between Road Accidents and Psychophysical State of Drivers through Wearable Devices

A driver’s behavior and their psychophysical state are the most common causes of road accidents. The research presented in the paper proposes a method that allows the identification of highly dangerous road stretches/intersections in advance, based on the localization of stressful/relaxing situations measured on drivers. These were measured through the collection of physiological parameters using wearable devices. A correlation between stressful/relaxing situations and locations with high accident rates, based on a historical statistical database (black spots), was investigated. A series of driving tests was conducted in the city of Milan. The first set was mostly oriented to the research and validation of the parameters related to the driver’s psychophysical state. Subsequent tests allowed the definition of a correlation between black spots and relaxing/stressful areas. The results showed that the most stressful areas for drivers fell mainly within those with high accident rates. Furthermore, 80% of the most dangerous zones of the route were identified using this method, thus confirming the validity of the approach as a support tool for a priori preventive analysis for road safety. The wearable devices allowed the study and the integration of specific elements relating to human behavior in the field of road safety, which typically involves a technical-engineering approach.

A review of physiological and behavioral monitoring with digital sensors for neuropsychiatric illnesses

Physiological, behavioral, and psychological changes associated with neuropsychiatric illness are reflected in several related signals, including actigraphy, location, word sentiment, voice tone, social activity, heart rate, and responses to standardized questionnaires. These signals can be passively monitored using sensors in smartphones, wearable accelerometers, Holter monitors, and multimodal sensing approaches that fuse multiple data types. Connection of these devices to the internet has made large scale studies feasible and is enabling a revolution in neuropsychiatric monitoring. Currently, evaluation and diagnosis of neuropsychiatric disorders relies on clinical visits, which are infrequent and out of the context of a patient's home environment. Moreover, the demand for clinical care far exceeds the supply of providers. The growing prevalence of context-aware and physiologically relevant digital sensors in consumer technology could help address these challenges, enable objective indexing of patient severity, and inform rapid adjustment of treatment in real-time. Here we review recent studies utilizing such sensors in the context of neuropsychiatric illnesses including stress and depression, bipolar disorder, schizophrenia, post traumatic stress disorder, Alzheimer's disease, and Parkinson's disease.

Wakefulness evaluation during sleep for healthy subjects and OSA patients using a patch-type device

OBJECTIVES

Obstructive sleep apnea (OSA) is a major sleep disorder that causes insufficient sleep, which is linked with daytime fatigue and accidents. Long-term sleep monitoring can provide meaningful information for patients with OSA to prevent and manage their symptoms. Even though various methods have been proposed to objectively measure sleep in ambulatory environments, less reliable information was provided in comparison with standard polysomnography (PSG). Therefore, this paper proposes an algorithm for distinguishing wakefulness from sleep using a patch-type device, which is applicable for both healthy individuals and patients with OSA.

METHODS

Electrocardiogram (ECG) and 3-axis accelerometer signals were gathered from the single device. Wakefulness was determined with six parallel methods based on information about movement and autonomic nervous activity. The performance evaluation was conducted with five-fold cross validation using the data from 15 subjects with a low respiratory disturbance index (RDI) and 10 subjects with high RDI. In addition, wakefulness information, including total sleep time (TST), sleep efficiency (SE), sleep onset latency (SOL), and wake after sleep onset (WASO), were extracted from the proposed algorithm and compared with those from PSG.

RESULTS

According to epoch-by-epoch (30 s) analysis, the performance results of detecting wakefulness were an average Cohen's kappa of 0.60, accuracy of 91.24%, sensitivity of 64.12%, and specificity of 95.73%. Moreover, significant correlations were observed in TST, SE, SOL, and WASO between the proposed algorithm and PSG (p < 0.001).

CONCLUSIONS

Wakefulness-related information was successfully provided using data from the patch-type device. In addition, the performance results of the proposed algorithm for wakefulness detection were competitive with those from previous studies. Therefore, the proposed system could be an appropriate solution for long-term objective sleep monitoring in both healthy individuals and patients with OSA.

Autonomic nerve activity indexed using 24-h heart rate variability in patients with burns

BACKGROUND

Heart rate variability (HRV) is a noninvasive method used to quantify fluctuations in the time interval between normal heart beats. The purpose of this study was to compare the autonomic nervous system functioning of patients with burns to healthy participants after their burn scars had been re-epithelialized.

MATERIALS AND METHODS

The authors prospectively performed 24-h HRV monitoring in 60 patients with electrical burns, those with other major burns, those with other minor burns, and 10 healthy participants. Analysis of HRV in the time and frequency domain was performed.

RESULTS

The difference in sympathetic nerve measures (standard deviation of NN intervals [SDNN], total power [TP] and a low frequency [LF] band) and parasympathetic nerve measures (Root mean square successive difference [RMSSD], the number of interval differences of successive NN intervals greater than 50ms [NN50], the percentage of differences between following RR intervals greater than 50ms [pNN50] and a high frequency [HF] band) in patients with burns was significantly decreased during the daytime and the nighttime. the difference in parasympathetic nerve measures were more significantly decreased during the nighttime compared with measures of HRV in healthy participants. The groups of other burns showed significantly lower HRV than the electrical burn group indexed by a very low frequency (VLF) measure and TP during the daytime.

CONCLUSION

We hypothesized that HRV is a surrogate for autonomic nervous system dysfunction in patients with burns. The patients with burns were observed a sympathetic predominance during daytime and a decreased parasympathetic activity during nighttime. These results of patients with other major burns were more predominant compared with the results of patients with other groups.

A Systematic Review of Wearable Patient Monitoring Systems - Current Challenges and Opportunities for Clinical Adoption

The aim of this review is to investigate barriers and challenges of wearable patient monitoring (WPM) solutions adopted by clinicians in acute, as well as in community, care settings. Currently, healthcare providers are coping with ever-growing healthcare challenges including an ageing population, chronic diseases, the cost of hospitalization, and the risk of medical errors. WPM systems are a potential solution for addressing some of these challenges by enabling advanced sensors, wearable technology, and secure and effective communication platforms between the clinicians and patients. A total of 791 articles were screened and 20 were selected for this review. The most common publication venue was conference proceedings (13, 54%). This review only considered recent studies published between 2015 and 2017. The identified studies involved chronic conditions (6, 30%), rehabilitation (7, 35%), cardiovascular diseases (4, 20%), falls (2, 10%) and mental health (1, 5%). Most studies focussed on the system aspects of WPM solutions including advanced sensors, wireless data collection, communication platform and clinical usability based on a specific area or disease. The current studies are progressing with localized sensor-software integration to solve a specific use-case/health area using non-scalable and 'silo' solutions. There is further work required regarding interoperability and clinical acceptance challenges. The advancement of wearable technology and possibilities of using machine learning and artificial intelligence in healthcare is a concept that has been investigated by many studies. We believe future patient monitoring and medical treatments will build upon efficient and affordable solutions of wearable technology.