Project description
Voice tests to assess the risk of sudden cardiac arrest
Cardiac arrest is when your heart suddenly stops pumping blood around your body. The symptoms arise suddenly and there is little time for tests. Sudden cardiac arrest is life-threatening. However, advances in technologies like contactless remote patient monitoring can help predict if (and when) someone will suffer a cardiac arrest. The Marie Skłodowska-Curie Actions project ContactlessFramework will develop a risk prediction framework, VCardiac, to diagnose and identify early stages of heart diseases and forecast cardiac conditions in advance, using the human voice. A customised dataset of 2 000 human voice samples will be designed to classify acoustic heartbeat events as normal, murmur, extra systole and artefact as well as other, unlabelled heartbeat acoustic events.
Objective
"In ancient times, doctors have followed unorganized practices to diagnose cardiac arrest conditions of healthcare patients. The followed medical procedures were not very organized and accurate even though cardiac patients were diagnosed using devices, such as a stethoscope. However, the latest advancements in the technologies such as contactless remote patient monitoring, AIoMT (Artificial Intelligence of Medical Things), advanced big data and cloud-based analytics and alerts have created a paradigm shift in healthcare and provided 24 x 7 connectivity. The study proposed a VCardiac (Contactless Cardiac Classification and Risk Prediction) Framework to diagnose and identify early stages of heart diseases and forecast their cardiac conditions in advance using the human voice. In this study, a customized dataset termed ""Cardiac-2000"" with 2000 human voice samples will be designed to classify acoustic heartbeat events such as normal, murmur, extra systole, artifact, and other unlabeled heartbeat acoustic events. The average duration of the recorded heartbeat acoustic events would be 10 to 12 seconds. The primary reason for designing a customized dataset ""cardiac-2000"" is to balance the total number of samples into categories such as normal and abnormal heartbeat acoustic events. For performance evaluations of the proposed VCardiac Framework, the collected heartbeat acoustic samples will be classified using LSTM-CNN, RNN, LSTM, Bi-LSTM, CNN, K-means Clustering, and SVM methodologies. Furthermore, the proposed VCardiac Framework will also assist in classifying Age and Gender-wise risks using methodologies such as Kaplan-Meier and Cox-regression survival analysis. These methodologies will also assist in identifying the probability risk and the 10-year risk score prediction. In the end, the proposed VCardiac Framework will be tested for various Signal to Noise ratio conditions for achieving better accuracy, effectiveness, and throughput."
Fields of science
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Funding Scheme
HORIZON-AG-UN - HORIZON Unit GrantCoordinator
35195 Vaxjo
Sweden