English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Cardiovascular disease is one of the major social health problems. Heart attacks, in particular, are a major social concern because of the unpredictable and silent way they develop. Recently, it was reported that the incidence of myocardial infarction has decreased significantly (Yeh et al., 2010). However, some unlucky patients are unaware that they are at risk for the life threatening disease. We must acknowledge that while the default setting is good health, there is always an onset to a disease and never to a return to good health. This onset results in “silent“ angina, and finally a “silent“ attack can happen. In fact, Dutch researchers estimated that 43% of heart attacks went unrecognized (de Torbal et al., 2006). Our ultimate aim was to predict a heart attack, or at least to quantitatively analyze the heart condition, based on the belief that it is possible to predict a heart attack by observing fluctuations in heartbeat intervals. Fluctuation analysis first appeared in the physical literature a long time ago (Peng et al., 1995). However, strong empirical evidence of its accuracy and usefulness must still be collected. Traditionally, cardiac studies have employed heart rate variability (HRV) to detect the onset of cardiac problems, including disorders of the autonomic nervous system. Problems arise, however, when patients are previously assumed to be healthy before the appearance of symptoms associated with HRV. An earlier marker is necessary because the early identification of symptoms aids in the prevention of the onset of chronic diseases. Detrended fluctuation analysis (DFA) (Peng et al., 1995) was proposed as a potentially useful method for detecting the signs of cardiovascular disease (See Stanley et al., 1999); although DFA has not yet been developed as a practical medical tool, such as the electrocardiogram (EKG). (We prefer the abbreviation “EKG” to “ECG,” with due respect to the inventor, Dutch physiologist, Nobel laureate, Willem Einthoven.) We recently tested the practical usefulness of DFA by using the heart of crustacean-animal models. In the test, we successfully showed that DFA could distinguish between intact and isolated hearts (Yazawa et al., 2004). In that study, we found out that the scaling exponent of the isolated hearts shifted and approached to 0.5 without exception. In turn, the scaling exponent of the intact hearts showed a value of about 1.0 without exception. As a result, we realized that DFA was reliable and useful because DFA was likely able to accurately reflect

Low Scaling Exponent during Arrhythmia: Detrended Fluctuation Analysis is a Beneficial Biomedical Computation Tool