2020
DOI: 10.1109/access.2020.2968900
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Machine Learning and End-to-End Deep Learning for the Detection of Chronic Heart Failure From Heart Sounds

Abstract: Chronic heart failure (CHF) affects over 26 million of people worldwide, and its incidence is increasing by 2% annually. Despite the significant burden that CHF poses and despite the ubiquity of sensors in our lives, methods for automatically detecting CHF are surprisingly scarce, even in the research community. We present a method for CHF detection based on heart sounds. The method combines classic Machine-Learning (ML) and end-to-end Deep Learning (DL). The classic ML learns from expert features, and the DL … Show more

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Cited by 99 publications
(52 citation statements)
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References 43 publications
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“…This includes algorithms as Convolutional Neural Network (CNN), Drop Connected Neural Networks (DCNN), Gram polynomials and probabilistic neural networks, AdaBoost classifier, LogitBoost, Random Forest, and a Cost-Sensitive Classifier [11][12][13][14][15]. Martin et al [16] used the deep learning machine during the detection of chronic heart failure disease and to improve classification accuracy. Other approaches achieved an acceptable accuracy using clustering techniques for cardiac sound classification such as the k-nearest neighbors (kNN) algorithm [17], threshold-based methods, and decision trees [18].…”
Section: Introductionmentioning
confidence: 99%
“…This includes algorithms as Convolutional Neural Network (CNN), Drop Connected Neural Networks (DCNN), Gram polynomials and probabilistic neural networks, AdaBoost classifier, LogitBoost, Random Forest, and a Cost-Sensitive Classifier [11][12][13][14][15]. Martin et al [16] used the deep learning machine during the detection of chronic heart failure disease and to improve classification accuracy. Other approaches achieved an acceptable accuracy using clustering techniques for cardiac sound classification such as the k-nearest neighbors (kNN) algorithm [17], threshold-based methods, and decision trees [18].…”
Section: Introductionmentioning
confidence: 99%
“…The spectro-temporal ResNet (STRNet) is a special case of the mid-fusion approach. in previous study on human activity recognition from smartphone sensors [71], for chronic heart failure detection from heart sounds [72], and for blood pressure estimation from photoplethysmogram (PPG) data [73].…”
Section: ) Deep Learing Architecturesmentioning
confidence: 99%
“…This system follows [48] where the ResNet architecture and the openSMILE toolkit are used for audio/acoustic feature extraction while a random forest (RF) is used for classification. The feature sets are described below.…”
Section: System Descriptionmentioning
confidence: 99%