Recent advances in heart sound analysis

Clifford, Gari D.; Liu, Chengyu; Moody, Benjamin; Millet, José; Schmidt, Samuel; Liu, Qiao; Silva, Ikaro; Mark, Roger G.

doi:10.1088/1361-6579/aa7ec8

Cited by 92 publications

(59 citation statements)

References 29 publications

(44 reference statements)

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“…Finally, in the Heart Beats (H) Sub-Challenge, normal, mild, and moderate/severe types of heart beats have to be classified. By including these acoustic -albeit non-vocal -signals, we contribute to heart sound analysis [3]; applications are self-evident, e. g., the monitoring of patients with unclear symptoms.…”

Section: Introductionmentioning

confidence: 99%

The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

et al. 2018

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The INTERSPEECH 2018 Computational Paralinguistics Challenge addresses four different problems for the first time in a research competition under well-defined conditions: In the Atypical Affect Sub-Challenge, four basic emotions annotated in the speech of handicapped subjects have to be classified; in the Self-Assessed Affect Sub-Challenge, valence scores given by the speakers themselves are used for a three-class classification problem; in the Crying Sub-Challenge, three types of infant vocalisations have to be told apart; and in the Heart Beats Sub-Challenge, three different types of heart beats have to be determined. We describe the Sub-Challenges, their conditions, and baseline feature extraction and classifiers, which include data-learnt (supervised) feature representations by end-to-end learning, the 'usual' ComParE and BoAW features, and deep unsupervised representation learning using the AUDEEP toolkit for the first time in the challenge series.

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Section: Introductionmentioning

confidence: 99%

The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

et al. 2018

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“…Cardiac auscultation based on heart sound recordings or phonocardiogram (PCG) remains a primary screening tool for diverse heart pathologies. Various algorithms have been developed aiming at accurate automated classification of normal and abnormal PCGs [1]. However, the classification accuracy is still far from being reliable for diagnostics in clinical or non-clinical settings.…”

Section: Introductionmentioning

confidence: 99%

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

Noman

Ting

Salleh

et al. 2019

ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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This paper proposes a framework based on deep convolutional neural networks (CNNs) for automatic heart sound classification using short-segments of individual heart beats. We design a 1D-CNN that directly learns features from raw heart-sound signals, and a 2D-CNN that takes inputs of twodimensional time-frequency feature maps based on Mel-frequency cepstral coefficients (MFCC). We further develop a time-frequency CNN ensemble (TF-ECNN) combining the 1D-CNN and 2D-CNN based on score-level fusion of the class probabilities. On the large PhysioNet CinC challenge 2016 database, the proposed CNN models outperformed traditional classifiers based on support vector machine and hidden Markov models with various hand-crafted time-and frequency-domain features. Best classification scores with 89.22% accuracy and 89.94% sensitivity were achieved by the ECNN, and 91.55% specificity and 88.82% modified accuracy by the 2D-CNN alone on the test set.

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“…Besides, the automated analysis in heart sounds is significant in the clinical research, so the 2016 CinC Challenge [2] has focused on this field and encouraged the development of algorithms for accurate classification [27][28][29]. In the future, some artificial intelligence technologies will also be applied in the system to automatically analyze big heart sound data and provide auxiliary diagnosis functions for clinicians.…”

Section: Limitations and Prospectsmentioning

confidence: 99%

A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm

Zhang

Chang

et al. 2019

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Along with the great performance in diagnosing cardiovascular diseases, current stethoscopes perform unsatisfactorily in controlling undesired noise caused by the surrounding environment and detector operation. In this case, a low-noise-level heart sound system was designed to inhibit noise by a novel thorax-integration head with a flexible electric film. A hardware filter bank and wavelet-based algorithm were employed to enhance the recorded heart sounds from the system. In the experiments, we used the new system and the 3M™ Littmann® Model 3200 Electronic Stethoscope separately to record heart sounds in different noisy environments. The results illustrated that the average estimated noise ratio represented 21.26% and the lowest represented only 12.47% compared to the 3M stethoscope, demonstrating the better performance in denoising ability of this system than state-of-the-art equipment. Furthermore, based on the heart sounds recorded with this system, some diagnosis results were achieved from an expert and compared to echocardiography reports. The diagnoses were correct except for two uncertain items, which greatly confirmed the fact that this system could reserve complete pathological information in the end.

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Recent advances in heart sound analysis

Abstract: Heart sounds have been widely studied and have been demonstrated to have value for detecting pathologies in clinical applications. Over the last few decades, the use of heart sound signals has become increasingly uncommon and its practice in

Cited by 92 publications

References 29 publications

The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

The INTERSPEECH 2018 Computational Paralinguistics Challenge: Atypical & Self-Assessed Affect, Crying & Heart Beats

Short-segment Heart Sound Classification Using an Ensemble of Deep Convolutional Neural Networks

A Low-Noise-Level Heart Sound System Based on Novel Thorax-Integration Head Design and Wavelet Denoising Algorithm

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