Convolutional Neural Network for ECGbased Virtual Pathology Stethoscope Tracking in Patient Heart Auscultation Cardiac auscultation (CA) is the auditory detection of heart sounds to diagnose abnormalities, a crucial skill that is both efficient and costeffective in medical practice. However, due to increased prevalence and usage of expensive cardiac technologies, many new physicians and trainees have difficulty performing essential cardiac examinations on their patients, particularly diagnosing abnormalities through auscultation using a stethoscope. A virtual pathology stethoscope is a simulationbased solution that train students to perform cardiac examinations by listening to abnormal heart sounds in otherwise healthy standardized patients (SPs). This study reports the accuracy of an electrocardiogram (ECG)based stethoscope tracking method for placing virtual symptoms in correct auscultation regions. A modified stethoscope head with two electrodes was used to pick up ECG signals at the four primary auscultation sites. A onedimensional convolutional neural network (CNN) is then modeled to classify the location of the stethoscope by taking advantage of subtle differences in the ECG signals. A 91% accuracy was obtained, showing promising performance gain over our previous methods. This finding would significantly extend the simulation capabilities of SPs by allowing trainees to perform realistic CA and hear CA in clinical environment.

Deep Wavelets for Heart Sound Classification Cardiovascular diseases have a high morbidity, and remain the leading cause of mortality. In the past two decades, developing an intelligent auscultation system has attracted tremendous efforts from the field of signal processing and machine learning. We propose a novel framework based on wavelet representations and deep recurrent neural networks for recognising three heart sounds, i. e., normal, mild, and severe. The Heart Sounds Shenzhen corpus (n  170) is used to validate the proposed method. The experimental results demonstrate the efficacy of the proposed method in a rigorous subject independent scenario, which can reach an unweighted average recall at 43.0 % (chance level: 33.3%).