Description du projet
Des tests vocaux pour évaluer le risque d’arrêt cardiaque soudain
Un arrêt cardiaque se produit lorsque le cœur cesse soudainement de pomper le sang dans le corps. Les symptômes apparaissent soudainement et il n’y a guère de temps pour effectuer des tests. L’arrêt cardiaque soudain peut s’avérer fatal. Les progrès des technologies tels que la surveillance à distance sans contact des patients peuvent toutefois aider à prédire si (et quand) une personne va subir un arrêt cardiaque. Le projet ContactlessFramework, financé par le programme Actions Marie Skłodowska-Curie, développera un cadre de prédiction des risques, baptisé VCardiac, pour diagnostiquer et identifier les stades précoces des maladies cardiaques et prévoir les conditions cardiaques, en analysant la voix humaine. Un ensemble de données personnalisé de 2 000 échantillons de voix humaines sera mis au point afin de classer les événements acoustiques de battements de cœur en différentes catégories: normaux, murmures, extrasystoles, artefacts, ou autres événements acoustiques de battements de cœur non catalogués.
Objectif
"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."
Mots‑clés
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinateur
35195 Vaxjo
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