A Review and Comparison of the State-of-the-Art Techniques for Atrial Fibrillation Detection and Skin Hydration

Atrial fibrillation (AF) is one of the most common types of cardiac arrhythmia, with a prevalence of 1–2% in the community, increasing the risk of stroke and myocardial infarction. Early detection of AF, typically causing an irregular and abnormally fast heart rate, can help reduce the risk of strokes that are more common among older people. Intelligent models capable of automatic detection of AF in its earliest possible stages can improve the early diagnosis and treatment. Luckily, this can be made possible with the information about the heart's rhythm and electrical activity provided through electrocardiogram (ECG) and the decision-making machine learning-based autonomous models. In addition, AF has a direct impact on the skin hydration level and, hence, can be used as a measure for detection. In this paper, we present an independent review along with a comparative analysis of the state-of-the-art techniques proposed for AF detection using ECG and skin hydration levels. This paper also highlights the effects of AF on skin hydration level that is missing in most of the previous studies.

Evaluation of anxiety levels amidst Covid-19 pandemic among Chennai population

Anxiety refers to Intense, excessive and persistent worry and fear about everyday situations. Fast heart rate, rapid breathing, sweating and feeling of tiredness is how WHO defines anxiety. COVID 19 pandemic has increased the global anxiety level. The relatives and acquaintances infected with COVID - 19 is a risk factor for increasing the anxiety level. Self-administered questionnaires were designed based on knowledge attitude, and practice. The participants were in the age group of 18 - 60 years and belonged to the Chennai population. The questions were validated and distributed using google forms. After receiving enough responses, the data was collected and statistically analysed. The knowledge and awareness of COVID 19 are high in the Chennai population. Along with an increase in the anxiety level in the context of COVID 19. The results show the need for social support to alleviate stress and improve mental health.

Diagnostics of ventricular tachycardia signals soft computing approach

Purpose The purpose of the paper is to analyze the ventricular tachycardia by soft computing. Ventricular tachycardia is a type of regular and fast heart rate which arises from improper electrical activity in the ventricles of the heart. Design/methodology/approach In this study, a soft computing approach was applied for the ventricular tachycardia detection. The soft computing was used to detect which factors are the most important for the ventricular tachycardia. Findings Three factors were used: brain natriuretic peptide, troponin I which is a part of the troponin complex and C-reactive protein which is an annular (ring-shaped), pentameric protein found in blood plasma. Originality/value It was found that troponin I has the most influence on the ventricular tachycardia prediction.