Deep Wavelets for Heart Sound Classification

Qian, Kun; Ren, Zhao; Dong, Fengquan; Lai, Wen-Hsing; Schuller, Björn; Yamamoto, Yoshiharu

doi:10.1109/ispacs48206.2019.8986277

Cited by 10 publications

(3 citation statements)

References 10 publications

(11 reference statements)

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“…The snore sound analysis [47] , [48] , which aims to find the pathological changes in the upper airway, may also facilitate the relevant evaluation of sleep of the COVID-19 patients. The association of the cardiac injury with mortality was found in COVID-19 patients [20] , [49] , which makes the heart sound recognition task useful in an early monitoring process. Among with others, the sound and audio analysis technologies, such as 3-D audio localization [50] and hearing local proximity [51] , can be used for monitoring the social distancing and providing warnings.…”

Section: Background and Motivationmentioning

confidence: 99%

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19

Qian

Schmitt

Zheng

et al. 2021

IEEE Internet Things J.

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Computer audition (CA) has experienced a fast development in the past decades by leveraging advanced signal processing and machine learning techniques. In particular, for its non-invasive and ubiquitous character by nature, CA based applications in healthcare have increasingly attracted attention in recent years. During the tough time of the global crisis caused by the COVID-19 (coronavirus disease 2019), scientists and engineers in data science have collaborated to think of novel ways in prevention, diagnosis, treatment, tracking, and management of this global pandemic. On one hand, we have witnessed the power of 5G, internet of things, big data, computer vision, and artificial intelligence in applications of epidemiology modelling, drug and/or vaccine finding and designing, fast CT screening, and quarantine management. On the other hand, relevant studies in exploring the capacity of CA are extremely lacking and underestimated. To this end, we propose a novel multi-task speech corpus for COVID-19 research usage. We collected 51 confirmed COVID-19 patients' in-the-wild speech data in Wuhan city, China. We define three main tasks in this corpus, i. e., three-category classification tasks for evaluating the physical and/or mental

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Section: Background and Motivationmentioning

confidence: 99%

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19

Qian

Schmitt

Zheng

et al. 2021

IEEE Internet Things J.

Self Cite

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show abstract

“…Heartbeat classification tasks have been successfully completed using wavelet analysis. In the study (Qian et al, 2019), a system founded on wavelet representation and deep recurrent neural networks was proposed to identify three types of heartbeats: normal, mild, and severe. The proposed approach demonstrated promising results in heart sound classification tasks.…”

Section: Deep Learning For Heart Sound Classificationmentioning

confidence: 99%

Artificial intelligence for heart sound classification: A review

Chen,

Guo,

et al. 2024

Expert Systems

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Heart sound signal analysis is very important for the early identification and treatment of cardiovascular illness. With rapid advancements in science and technology, artificial intelligence technologies are providing tremendous opportunities to enhance diagnosis and clinical decision‐making. Instruments can now perform clinical diagnoses that previously could only be handled by human experts more conveniently and efficiently. Despite multiple works on automatic heart sound analysis, there are few summarization and review works. This article attempts to give a thorough overview of various heart sound analysis subtasks and examine the improvements made in each subtask by both machine learning techniques and deep learning algorithms. It goals to highlight the potential of AI to revolutionize cardiovascular healthcare by enabling accurate and automated analysis of heart sounds. The findings of this review are beneficial for researchers, clinicians, and engineers in the development and application of AI‐based solutions for improved heart sound classification and diagnosis.

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“…Different from the above time-frequency representations, herein, we list 2D frequencybased features to include (a) multiple 1D frequency features directly extracted from audio segments rather than window functions in the STFT domain and (b) features computed from time-frequency features. Qian et al utilised wavelets to calculate wavelet energy features from a set of short acoustic segments and further used GRU-RNNs as the classifier [124]. Dong et al extracted log Mel features and corresponding functionals from heart sound segments and implemented classification LSTM-RNNs and GRU-RNNs [92].…”

Section: B Deep Learning For Classificationmentioning

confidence: 99%

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

Zhao¹,

Chen²,

Nguyên³

et al. 2023

Preprint

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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.

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Deep Wavelets for Heart Sound Classification

Cited by 10 publications

References 10 publications

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19

Computer Audition for Fighting the SARS-CoV-2 Corona Crisis—Introducing the Multitask Speech Corpus for COVID-19

Artificial intelligence for heart sound classification: A review

A Comprehensive Survey on Heart Sound Analysis in the Deep Learning Era

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