214 citations · 271 across the 9 of their papers we have counts for
9 papers
A Data-Driven Gaussian Process Filter for Electrocardiogram Denoising
Mircea Dumitru, Qiao Li, Erick Andres Perez Alday +3
Objective: Gaussian Processes (GP)-based filters, which have been effectively used for various applications including electrocardiogram (ECG) filtering can be computationally deman…
Beyond Heart Murmur Detection: Automatic Murmur Grading from Phonocardiogram
Andoni Elola, Elisabete Aramendi, Jorge Oliveira +6
Objective: Murmurs are abnormal heart sounds, identified by experts through cardiac auscultation. The murmur grade, a quantitative measure of the murmur intensity, is strongly corr…
Mythological Medical Machine Learning: Boosting the Performance of a Deep Learning Medical Data Classifier Using Realistic Physiological Models
Ismail Sadiq, Erick A. Perez-Alday, Amit J. Shah +3
Objective: To determine if a realistic, but computationally efficient model of the electrocardiogram can be used to pre-train a deep neural network (DNN) with a wide range of morph…
The CirCor DigiScope Dataset: From Murmur Detection to Murmur Classification
Jorge Oliveira, Francesco Renna, Paulo Dias Costa +12
Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can su…
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
Ayse S. Cakmak, Nina Thigpen, Garrett Honke +9
Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non…
Automated Polysomnography Analysis for Detection of Non-Apneic and Non-Hypopneic Arousals using Feature Engineering and a Bidirectional LSTM Network
Ali Bahrami Rad, Morteza Zabihi, Zheng Zhao +3
Objective: The aim of this study is to develop an automated classification algorithm for polysomnography (PSG) recordings to detect non-apneic and non-hypopneic arousals. Our parti…
1D Convolutional Neural Network Models for Sleep Arousal Detection
Morteza Zabihi, Ali Bahrami Rad, Serkan Kiranyaz +2
Sleep arousals transition the depth of sleep to a more superficial stage. The occurrence of such events is often considered as a protective mechanism to alert the body of harmful s…
Real-time PCG Anomaly Detection by Adaptive 1D Convolutional Neural Networks
Serkan Kiranyaz, Morteza Zabihi, Ali Bahrami Rad +4
The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnosti…
Kalman-based Spectro-Temporal ECG Analysis using Deep Convolutional Networks for Atrial Fibrillation Detection
Zheng Zhao, Simo Särkkä, Ali Bahrami Rad
In this article, we propose a novel ECG classification framework for atrial fibrillation (AF) detection using spectro-temporal representation (i.e., time varying spectrum) and deep…