Descrizione del progetto
Test vocali per valutare il rischio di un arresto cardiaco improvviso
L’arresto cardiaco si verifica quando il cuore smette all’improvviso di pompare il sangue nel corpo. I sintomi si manifestano bruscamente e non è presente molto tempo per i test: l’arresto cardiaco improvviso è potenzialmente letale. Tuttavia, i progressi compiuti in tecnologie quali il monitoraggio a distanza e senza contatto del paziente possono contribuire a prevedere se (e quando) si verrà colpiti da un arresto cardiaco. Il progetto ContactlessFramework, del programma di azioni Marie Skłodowska-Curie, svilupperà un quadro di previsione del rischio, VCardiac, per diagnosticare e identificare le fasi precoci delle cardiopatie e prevedere le condizioni cardiache in anticipo, avvalendosi della voce umana. Sarà progettato un set di dati personalizzato contenente 2 000 campioni di voce umana per classificare gli eventi acustici del battito cardiaco secondo varie categorie, quali normali, soffio, extrasistole e artefatti, nonché di altro tipo non etichettato.
Obiettivo
"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."
Parole chiave
Programma(i)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Meccanismo di finanziamento
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinatore
35195 Vaxjo
Svezia