3,297 research outputs found
A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era
Heart sound auscultation has been demonstrated to be beneficial in clinical
usage for early screening of cardiovascular diseases. Due to the high
requirement of well-trained professionals for auscultation, automatic
auscultation benefiting from signal processing and machine learning can help
auxiliary diagnosis and reduce the burdens of training professional clinicians.
Nevertheless, classic machine learning is limited to performance improvement in
the era of big data. Deep learning has achieved better performance than classic
machine learning in many research fields, as it employs more complex model
architectures with stronger capability of extracting effective representations.
Deep learning has been successfully applied to heart sound analysis in the past
years. As most review works about heart sound analysis were given before 2017,
the present survey is the first to work on a comprehensive overview to
summarise papers on heart sound analysis with deep learning in the past six
years 2017--2022. We introduce both classic machine learning and deep learning
for comparison, and further offer insights about the advances and future
research directions in deep learning for heart sound analysis
A LightGBM-Based EEG Analysis Method for Driver Mental States Classification
Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography-
(EEG-) based physiological and brain activities for fatigue detection have been increasingly investigated.
However, how to find an effective method or model to timely and efficiently detect the mental states of drivers still remains a
challenge. In this paper, we combine common spatial pattern (CSP) and propose a light-weighted classifier, LightFD, which is
based on gradient boosting framework for EEG mental states identification. ,e comparable results with traditional classifiers,
such as support vector machine (SVM), convolutional neural network (CNN), gated recurrent unit (GRU), and large margin
nearest neighbor (LMNN), show that the proposed model could achieve better classification performance, as well as the decision
efficiency. Furthermore, we also test and validate that LightFD has better transfer learning performance in EEG classification of
driver mental states. In summary, our proposed LightFD classifier has better performance in real-time EEG mental state
prediction, and it is expected to have broad application prospects in practical brain-computer interaction (BCI)
Phonocardiographic sensing using deep learning for abnormal heartbeat detection
Deep learning-based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliable and highly accurate systems, which are robust to the background noise in the heartbeat sound. In this paper, we propose a Recurrent Neural Networks (RNNs)-based automated cardiac auscultation solution. Our choice of RNNs is motivated by their great success of modeling sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models significantly outperform the best reported results in the literature. We also present the run-time complexity of various RNNs, which provides insight about their complexity versus performance trade-offs
Machine listening for heart status monitoring: introducing and benchmarking HSS — the Heart Sounds Shenzhen corpus
An article in IEEE Journal of Biomedical and Health Informatic
- …