Study of Predictive Analysis of Hospital Mortality Using ECG Signals from Heart

The hearts electrical signals play an important role in its functioning to collect deoxygenated blood and pump oxygenated blood to the rest of the body. The abnormality in the hearts muscle can cause deficiencies in electrical signal generation or passage through heart valves which can lead to the imbalance of blood flow leading to a wide range of issues from a minor abnormality to a severe outcome such as death. Therefore, it is important to measure the strength of electrical signals on patients prone to heart diseases while admitted in a hospital or remotely using wearables. In this study, I experimented with different variations of ECG signals acquired on patients in a hospital setting to study the ability of advanced deep learning methodology vs. traditional signal processing to predict mortality of a patient in the hospital. The results are based on 198 patient cohort equally split between male and female. The results from deep learning are better than the traditional methods to predict patient mortality at hospital using ECG signals of heart.

Author

• Jose, Roberto Siasoco

• ECG
• heart
• predictive analytics
• deep learning
• machine learning
• hospital mortality

Identifier

hdl:1920/14077

OAI identifier oai:identifier

oai:MARS:1920/14077