A large population of people suffer from heart problems that can aggravate
into sudden heart failure. Meanwhile, 50% of all people who die from heart
problems experience sudden onset of heart rhythmic problem. Disturbance in the
heart’s electrical rhythm problem is known as arrythmia. The electrical rhythm
of the heart is also closely related to contraction of heart cells responsible
for pumping blood. However, researchers have struggled to understand the onset
of sudden disturbances in electrical rhythms that cause abrupt heart failure.
A research conducted at the University of Bristol finds potential
pathways of preventing sudden heart failure. The Medical Research Council (MRC)
funded the research that is published in Proceedings of the National
Academy of Sciences (PNAS) journal.
Change in Voltage
The researchers assert that varying the time for voltage change can
help in preventing fatal electrical disturbances. Besides, changing the time
course can also help towards improving the cardiac contraction of the heart.
Cardiac arrhythmias occur due to early after-depolarizations (EADs) at the
cellular level. However, the cause of heart failure after the occurrence of
arrythmia remains unknown. The researchers find that a sodium-calcium exchange
in the heart muscle can oppose the action of EADs. This can in turn help in
reducing the risk of sudden heart failure in individuals.
Release of Ca2+
The researchers assert that the prime focus of cardiologists should be on
improving the release of Ca2+. Release of Ca2+ can help
in restoring repolarisation of AP phase-1. The researchers suggest a completely
different approach to tackle heart arrythmia that can cause sudden heart
failure. The findings of the research shall compel scientists to test new lines
of drugs. The relevance of preventing heart failure in cardiology is behind the
growing popularity of the research.
A new AI neural network technique could help in identifying
congestive heart failure (CHF) with 100% accuracy. The technique could predict
the possibility of heart failure by looking at a single beat on the
electrocardiograph. The use of neuroscience techniques is an integral part of
medical research. However, fool-proof diagnosis through neural techniques is an
unprecedented feat. The new technique, based on convolutional neural networks
(CNN), could change that. This technique could be the first success of the
medical fraternity in predicting heart failures with 100% accuracy.
Need for Improved Diagnosis
Congestive heart failure is a major cause of high mortality
rate across several regions. The condition is characterized by extreme pain and
discomfort. This is because the heart muscle loses its ability to pump blood in
the event of a congestive heart failure. In addition, these factors necessitate
the development of sound systems for preventing and diagnosing heart failure.
The new research was headed by Dr. Sebastiano Massaro from
the University of Survey. The Biomedical Signal Processing and Control
journal published the findings of the research. The research has paved way for
improved diagnostic procedures for CHF. Current methods are based on heart-rate
variability that increases the chances of error. Furthermore, these methods are
obsolete and time-consuming for the medical staff.
Overcoming Limitations of Current Techniques
Current-day techniques for cardiac diagnosis require a
detailed study of the electrocardiogram (ECG) report. The new CNN model uses
machine learning tools and advanced signal processing to study ECG patterns.
The use of these technologies ensures 100% accuracy of results. The researchers
tested the CNN model on subjects with CHF, whilst considering their ECG dataset
history. Additionally, the CNN model can help in gauging the severity of the
Cardiologists are expected to embrace quick-diagnostic
techniques without much ado. This propensity shall help in popularizing
convolutional neural network models for the healthcare fraternity.