Abstract
The leading cause of death worldwide is cardiac disease, which kills an estimated 27.9 million people each year and is responsible for 31% of all fatalities. Heart failure is frequently brought on by cardiovascular problems. It can be identified by the heart's inability to deliver enough blood to the body. All of the body's fundamental functions are affected when there is insufficient blood flow. Heart failure is a condition or set of symptoms that weakens the heart. Three important aspects form the foundation of the research study's main results. Given that it essentially measures the efficiency of the heart, this is to be expected. The patient's age is the last factor that is most closely associated. The heart's performance progressively deteriorates with age. The data was modeled using machine learning and ANN with an accuracy of about 80%, showing how effective the framework is at detecting cardiac arrest. Deep learning models' accuracy might rise to 90–95%.
TopIntroduction
Every year, about 500,000 Americans die as a result of cardiac arrest, which occurs when the heart abruptly stops beating. People with cardiac arrest become unresponsive and either choke to death or struggle to breathe, a sign known as arrhythmic respiration (Whig et al., 2022). Early CPR can increase a person's chances of life by doubling or tripling them, but it depends on the presence of a volunteer (Anand et al., 2022).
Medical emergencies happen all the time even outside the hospitals, in the privacy of one’s home (Alkali et al., 2022; Jiwani et al., 2021). According to new findings, amongst the most common locations for cardiogenic shock is in a people's home, when nobody is expected to be present to react and provide assistance (George et al., 2021).
Research from the University of Iowa has developed a novel tool that may detect heart attack or stroke in patients when they are asleep without wanting to connect with anyone (Parihar & Yadav, n.d.; Sinha & Ranjan, 2015). This new update for a voice assistant, such as Google or Amazon’s Alexa, or smartphones recognizes the gasping sound of agonizing respiration and automatically seeks help (Whig & Ahmad, 2019).
The different causes of death are shown in Figure1. The data shows that Sudden Cardiac Arrest is the highest among all other factors, hence it is a very important concern to the discussion in this chapter.
Figure 1. Data Shows different causes of deaths
The solid evidence method, which was developed using genuine agonal respiratory instances obtained from Emergency calls, correctly identified arrhythmic respiratory instances 96% at a distance of up to 20 feet (or 6 meters). In July 2019, the results were reported in the journal npj Electronic Health(Rupani & Sujediya, 2016).
“A lot of people have smart speakers in their homes, and these devices have great capabilities that we can make use of,” “We imagine a contactless device that monitors the bedroom continually and passively for an agonizing breathing incident and informs anybody nearby to come to perform CPR.” If there is no answer, the gadget will immediately dial 911(Channumsin et al., 2015).”
According to 911 call statistics, around 50% of persons who undergo cardiac arrests have agonal breathing, and affected roles who take agonal snorts frequently consume a greater coincidental of survival(Ruchin & Whig, 2015; Shrivastav et al., n.d.). “This type of breathing occurs when a patient has extremely low oxygen levels,” explained the University of Washington. “It's a guttural gasping sound, and its peculiarity makes it a useful auditory biomarker to use to determine if someone is having a cardiac arrest(Asopa et al., 2021).”
Figure 2. Annual Lives saved after sudden cardiac arrest
Key Terms in this Chapter
Disorder: A state of confusion.
Cardiac Arrest: The condition usually results from a problem with your heart's electrical system, which disrupts your heart's pumping action and stops blood flow to your body.
Symptoms: A physical or mental problem that a person experiences that may indicate a disease or condition. Symptoms cannot be seen and do not show up on medical tests.
Deep Learning: Deep learning is a type of machine learning and artificial intelligence (AI) that imitates the way humans gain certain types of knowledge.
CNN: Convolution is a mathematical operation that allows the merging of two sets of information. In the case of CNN, convolution is applied to the input data to filter the information and produce a feature map. This filter is also called a kernel, or feature detector.
Healthcare: Health care or healthcare is the improvement of health via the prevention, diagnosis, treatment, amelioration, or cure of disease, illness, injury, and other physical and mental impairments in people. Health care is delivered by health professionals and allied health fields.