Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study

Wearables for early detection of atrial fibrillation and timely referral for Indigenous people ≥55 years: mixed-methods protocol

INTRODUCTION

Digital health technologies have the potential to provide cost-effective care to remote and underserved populations. To realise this potential, research must involve people not traditionally included. No research focuses on the acceptability and feasibility of older Indigenous people using wearables for early atrial fibrillation (AF) detection. This protocol compares digital augmentation against standard practice to detect AF, evaluate heart health self-efficacy and health literacy changes and identify barriers in collaboration with Aboriginal Community Controlled Health Organisations. It will establish a framework for implementing culturally safe and acceptable wearable programmes for detecting and managing AF in Indigenous adults ≥55 years and older.

METHODS

This mixed-methods research will use the Rambaldini model of collective impact, a user-centred, co-design methodology and yarning circles, a recognised Indigenous research methodology to assess the cultural safety, acceptability, feasibility and efficacy of incorporating wearables into standard care for early AF detection.

ANALYSIS

Qualitative data will be analysed to create composite descriptions of participants' experiences and perspectives related to comfort, cultural safety, convenience, confidence, family reactions and concerns. Quantitative device data will be extracted and analysed via Statistical Product and Service Solutions (SPSS).

CONCLUSION

Prioritising perspectives of older Indigenous adults on using wearables for detecting and monitoring cardiovascular disease will ensure that the findings are effective, relevant and acceptable to those impacted.

ETHICS AND DISSEMINATION

Findings will be published in open-source peer-reviewed journals, shared at professional conferences, described in lay terms and made available to the public. The AHMRC HREC Reference Number approved 1135/15.

Improved 1-year outcomes after active cooling during left atrial radiofrequency ablation

BACKGROUND

Active esophageal cooling during pulmonary vein isolation (PVI) with radiofrequency (RF) ablation for the treatment of atrial fibrillation (AF) is increasingly being utilized to reduce esophageal injury and atrioesophageal fistula formation. Randomized controlled data also show trends towards increased freedom from AF when using active cooling. This study aimed to compare 1-year arrhythmia recurrence rates between patients treated with luminal esophageal temperature (LET) monitoring versus active esophageal cooling during left atrial ablation.

METHOD

Data from two healthcare systems (including 3 hospitals and 4 electrophysiologists) were reviewed for patient rhythm status at 1-year follow-up after receiving PVI for the treatment of AF. Results were compared between patients receiving active esophageal cooling (ensoETM, Attune Medical, Chicago, IL) and those treated with traditional LET monitoring using Kaplan-Meier estimates.

RESULTS

A total of 513 patients were reviewed; 253 received LET monitoring using either single or multi-sensor temperature probes; and 260 received active cooling. The mean age was 66.8 (SD ± 10) years, and 36.8% were female. Arrhythmias were 60.1% paroxysmal AF, 34.3% persistent AF, and 5.6% long-standing persistent AF, with no significant difference between groups. At 1-year follow-up, KM estimates for freedom from AF were 58.2% for LET-monitored patients and 72.2% for actively cooled patients, for an absolute increase in freedom from AF of 14% with active esophageal cooling (p = .03). Adjustment for the confounders of patient age, gender, type of AF, and operator with an inverse probability of treatment weighted Cox proportional hazards model yielded a hazard ratio of 0.6 for the effect of cooling on AF recurrence (p = 0.045).

CONCLUSIONS

In this first study to date of the association between esophageal protection strategy and long-term efficacy of left atrial RF ablation, a clinically and statistically significant improvement in freedom from atrial arrhythmia at 1 year was found in patients treated with active esophageal cooling when compared to patients who received LET monitoring. More rigorous prospective studies or randomized studies are required to validate the findings of the current study.

Heart Rate Variability Analysis of Healthy Individuals and Patients with Ischemia and Arrhythmia

This article presents the results of a study of the cardiac activity of patients diagnosed with arrhythmia and ischemic heart disease. The obtained results were compared with the results obtained from a healthy control group. The studies were conducted on long-term cardiac recordings (approximately 24 h) registered by means of Holter monitoring, and the observations were made in the daily activities of the individuals. All processing, analysis and evaluations on the registered signals were performed by means of an established information demonstration cardiology system. The mathematical analysis included linear, non-linear and graphical methods for estimating and analyzing heart rate variability (HRV). Re-examinations were carried out on some of the observed individuals after six months of treatment. The results show an increase in the main time domain parameters of the HRV, such as the SDNN (from 86.36 ms to 95.47 ms), SDANN (from 74.05 ms to 82.14 ms), RMSSD (from 5.1 ms to 6.92 ms), SDNN index (from 52.4 to 58.91) and HRVTi (from 12.8 to 16.83) in patients with ischemia. In patients with arrhythmia, there were increases in the SDNN (from 88.4 ms to 96.44 ms), SDANN (from 79.12 ms to 83.23 ms), RMSSD (from 6.74 ms to 7.31 ms), SDNN index (from 53.22 to 59.46) and HRVTi (from 16.2 to 19.42). An increase in the non-linear parameter α (from 0.83 to 0.85) was found in arrhythmia; and in α (from 0.80 to 0.83), α1 (from 0.88 to 0.91) and α2 (from 0.86 to 0.89) in ischemia. The presented information system can serve as an auxiliary tool in the diagnosis and treatment of cardiovascular diseases.

Obesity-Related Atrial Fibrillation: Cardiac Manifestation of a Systemic Disease

Atrial fibrillation (AF) is the most common arrhythmia worldwide and is associated with increased morbidity and mortality. The mechanisms underlying AF are complex and multifactorial. Although it is well known that obesity is a strong risk factor for AF, the mechanisms underlying obesity-related AF are not completely understood. Current evidence proposes that in addition to overall hemodynamic changes due to increased body weight, excess adiposity raises systemic inflammation and oxidative stress, which lead to adverse atrial remodeling. This remodeling includes atrial fibrosis, atrial dilation, decreased electrical conduction between atrial myocytes, and altered ionic currents, making atrial tissue more vulnerable to both the initiation and maintenance of AF. However, much remains to be learned about the mechanistic links between obesity and AF. This knowledge will power the development of novel diagnostic tools and treatment options that will help combat the rise of the global AF burden among the obesity epidemic.

A Bibliometric Analysis on Arrhythmia Detection and Classification from 2005 to 2022

Bibliometric analysis is a widely used technique for analyzing large quantities of academic literature and evaluating its impact in a particular academic field. In this paper bibliometric analysis has been used to analyze the academic research on arrhythmia detection and classification from 2005 to 2022. We have followed PRISMA 2020 framework to identify, filter and select the relevant papers. This study has used the Web of Science database to find related publications on arrhythmia detection and classification. "Arrhythmia detection", "arrhythmia classification" and "arrhythmia detection and classification" are three keywords for gathering the relevant articles. 238 publications in total were selected for this research. In this study, two different bibliometric techniques, "performance analysis" and "science mapping", were applied. Different bibliometric parameters such as publication analysis, trend analysis, citation analysis, and networking analysis have been used to evaluate the performance of these articles. According to this analysis, the three countries with the highest number of publications and citations are China, the USA, and India in terms of arrhythmia detection and classification. The three most significant researchers in this field are those named U. R. Acharya, S. Dogan, and P. Plawiak. Machine learning, ECG, and deep learning are the three most frequently used keywords. A further finding of the study indicates that the popular topics for arrhythmia identification are machine learning, ECG, and atrial fibrillation. This research provides insight into the origins, current status, and future direction of arrhythmia detection research.

Evaluation of Mitochondrial Function in Blood Samples Shows Distinct Patterns in Subjects with Thyroid Carcinoma from Those with Hyperplasia

Metabolic adaptations are a hallmark of cancer and may be exploited to develop novel diagnostic and therapeutic tools. Only about 50% of the patients who undergo thyroidectomy due to suspicion of thyroid cancer actually have the disease, highlighting the diagnostic limitations of current tools. We explored the possibility of using non-invasive blood tests to accurately diagnose thyroid cancer. We analyzed blood and thyroid tissue samples from two independent cohorts of patients undergoing thyroidectomy at the Hospital Universitario 12 de Octubre (Madrid, Spain). As expected, histological comparisons of thyroid cancer and hyperplasia revealed higher proliferation and apoptotic rates and enhanced vascular alterations in the former. Notably, they also revealed increased levels of membrane-bound phosphorylated AKT, suggestive of enhanced glycolysis, and alterations in mitochondrial sub-cellular distribution. Both characteristics are common metabolic adaptations in primary tumors. These data together with reduced mtDNA copy number and elevated levels of the mitochondrial antioxidant PRX3 in cancer tissue samples suggest the presence of mitochondrial oxidative stress. In plasma, cancer patients showed higher levels of cfDNA and mtDNA. Of note, mtDNA plasma levels inversely correlated with those in the tissue, suggesting that higher death rates were linked to lower mtDNA copy number. In PBMCs, cancer patients showed higher levels of PGC-1α, a positive regulator of mitochondrial function, but this increase was not associated with a corresponding induction of its target genes, suggesting a reduced activity in cancer patients. We also observed a significant difference in the PRDX3/PFKFB3 correlation at the gene expression level, between carcinoma and hyperplasia patients, also indicative of increased systemic metabolic stress in cancer patients. The correlation of mtDNA levels in tissue and PBMCs further stressed the interconnection between systemic and tumor metabolism. Evaluation of the mitochondrial gene ND1 in plasma, PBMCs and tissue samples, suggested that it could be a good biomarker for systemic oxidative metabolism, with ND1/mtDNA ratio positively correlating in PBMCs and tissue samples. In contrast, ND4 evaluation would be informative of tumor development, with ND4/mtDNA ratio specifically altered in the tumor context. Taken together, our data suggest that metabolic dysregulation in thyroid cancer can be monitored accurately in blood samples and might be exploited for the accurate discrimination of cancer from hyperplasia.

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

In recent years, vital signals monitoring in sports and health have been considered the research focus in the field of wearable sensing technologies. Typical signals include bioelectrical signals, biophysical signals, and biochemical signals, which have applications in the fields of athletic training, medical diagnosis and prevention, and rehabilitation. In particular, since the COVID-19 pandemic, there has been a dramatic increase in real-time interest in personal health. This has created an urgent need for flexible, wearable, portable, and real-time monitoring sensors to remotely monitor these signals in response to health management. To this end, the paper reviews recent advances in flexible wearable sensors for monitoring vital signals in sports and health. More precisely, emerging wearable devices and systems for health and exercise-related vital signals (e.g., ECG, EEG, EMG, inertia, body movements, heart rate, blood, sweat, and interstitial fluid) are reviewed first. Then, the paper creatively presents multidimensional and multimodal wearable sensors and systems. The paper also summarizes the current challenges and limitations and future directions of wearable sensors for vital typical signal detection. Through the review, the paper finds that these signals can be effectively monitored and used for health management (e.g., disease prediction) thanks to advanced manufacturing, flexible electronics, IoT, and artificial intelligence algorithms; however, wearable sensors and systems with multidimensional and multimodal are more compliant.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Clinical significance, challenges and limitations in using artificial intelligence for electrocardiography-based diagnosis

Cardiovascular diseases are one of the leading global causes of mortality. Currently, clinicians rely on their own analyses or automated analyses of the electrocardiogram (ECG) to obtain a diagnosis. However, both approaches can only include a finite number of predictors and are unable to execute complex analyses. Artificial intelligence (AI) has enabled the introduction of machine and deep learning algorithms to compensate for the existing limitations of current ECG analysis methods, with promising results. However, it should be prudent to recognize that these algorithms also associated with their own unique set of challenges and limitations, such as professional liability, systematic bias, surveillance, cybersecurity, as well as technical and logistical challenges. This review aims to increase familiarity with and awareness of AI algorithms used in ECG diagnosis, and to ultimately inform the interested stakeholders on their potential utility in addressing present clinical challenges.