245 research outputs found
Robust and Interpretable Temporal Convolution Network for Event Detection in Lung Sound Recordings
This paper proposes a novel framework for lung sound event detection,
segmenting continuous lung sound recordings into discrete events and performing
recognition on each event. Exploiting the lightweight nature of Temporal
Convolution Networks (TCNs) and their superior results compared to their
recurrent counterparts, we propose a lightweight, yet robust, and completely
interpretable framework for lung sound event detection. We propose the use of a
multi-branch TCN architecture and exploit a novel fusion strategy to combine
the resultant features from these branches. This not only allows the network to
retain the most salient information across different temporal granularities and
disregards irrelevant information, but also allows our network to process
recordings of arbitrary length. Results: The proposed method is evaluated on
multiple public and in-house benchmarks of irregular and noisy recordings of
the respiratory auscultation process for the identification of numerous
auscultation events including inhalation, exhalation, crackles, wheeze,
stridor, and rhonchi. We exceed the state-of-the-art results in all
evaluations. Furthermore, we empirically analyse the effect of the proposed
multi-branch TCN architecture and the feature fusion strategy and provide
quantitative and qualitative evaluations to illustrate their efficiency.
Moreover, we provide an end-to-end model interpretation pipeline that
interprets the operations of all the components of the proposed framework. Our
analysis of different feature fusion strategies shows that the proposed feature
concatenation method leads to better suppression of non-informative features,
which drastically reduces the classifier overhead resulting in a robust
lightweight network.The lightweight nature of our model allows it to be
deployed in end-user devices such as smartphones, and it has the ability to
generate predictions in real-time.Comment: preprint submitted to JBH
A Robust Interpretable Deep Learning Classifier for Heart Anomaly Detection Without Segmentation
Traditionally, abnormal heart sound classification is framed as a three-stage
process. The first stage involves segmenting the phonocardiogram to detect
fundamental heart sounds; after which features are extracted and classification
is performed. Some researchers in the field argue the segmentation step is an
unwanted computational burden, whereas others embrace it as a prior step to
feature extraction. When comparing accuracies achieved by studies that have
segmented heart sounds before analysis with those who have overlooked that
step, the question of whether to segment heart sounds before feature extraction
is still open. In this study, we explicitly examine the importance of heart
sound segmentation as a prior step for heart sound classification, and then
seek to apply the obtained insights to propose a robust classifier for abnormal
heart sound detection. Furthermore, recognizing the pressing need for
explainable Artificial Intelligence (AI) models in the medical domain, we also
unveil hidden representations learned by the classifier using model
interpretation techniques. Experimental results demonstrate that the
segmentation plays an essential role in abnormal heart sound classification.
Our new classifier is also shown to be robust, stable and most importantly,
explainable, with an accuracy of almost 100% on the widely used PhysioNet
dataset
Deep Attention-based Representation Learning for Heart Sound Classification
Cardiovascular diseases are the leading cause of deaths and severely threaten
human health in daily life. On the one hand, there have been dramatically
increasing demands from both the clinical practice and the smart home
application for monitoring the heart status of subjects suffering from chronic
cardiovascular diseases. On the other hand, experienced physicians who can
perform an efficient auscultation are still lacking in terms of number.
Automatic heart sound classification leveraging the power of advanced signal
processing and machine learning technologies has shown encouraging results.
Nevertheless, human hand-crafted features are expensive and time-consuming. To
this end, we propose a novel deep representation learning method with an
attention mechanism for heart sound classification. In this paradigm,
high-level representations are learnt automatically from the recorded heart
sound data. Particularly, a global attention pooling layer improves the
performance of the learnt representations by estimating the contribution of
each unit in feature maps. The Heart Sounds Shenzhen (HSS) corpus (170 subjects
involved) is used to validate the proposed method. Experimental results
validate that, our approach can achieve an unweighted average recall of 51.2%
for classifying three categories of heart sounds, i. e., normal, mild, and
moderate/severe annotated by cardiologists with the help of Echocardiography
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
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
A Comprehensive Survey on Applications of Transformers for Deep Learning Tasks
Transformer is a deep neural network that employs a self-attention mechanism
to comprehend the contextual relationships within sequential data. Unlike
conventional neural networks or updated versions of Recurrent Neural Networks
(RNNs) such as Long Short-Term Memory (LSTM), transformer models excel in
handling long dependencies between input sequence elements and enable parallel
processing. As a result, transformer-based models have attracted substantial
interest among researchers in the field of artificial intelligence. This can be
attributed to their immense potential and remarkable achievements, not only in
Natural Language Processing (NLP) tasks but also in a wide range of domains,
including computer vision, audio and speech processing, healthcare, and the
Internet of Things (IoT). Although several survey papers have been published
highlighting the transformer's contributions in specific fields, architectural
differences, or performance evaluations, there is still a significant absence
of a comprehensive survey paper encompassing its major applications across
various domains. Therefore, we undertook the task of filling this gap by
conducting an extensive survey of proposed transformer models from 2017 to
2022. Our survey encompasses the identification of the top five application
domains for transformer-based models, namely: NLP, Computer Vision,
Multi-Modality, Audio and Speech Processing, and Signal Processing. We analyze
the impact of highly influential transformer-based models in these domains and
subsequently classify them based on their respective tasks using a proposed
taxonomy. Our aim is to shed light on the existing potential and future
possibilities of transformers for enthusiastic researchers, thus contributing
to the broader understanding of this groundbreaking technology
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