Automatic segmentation for neonatal phonocardiogram

Gómez-Quintana, Sergi; Shelevytsky, Ihor; Shelevytska, V. A.; Popovici, Emanuel; Temko, Andriy

doi:10.1109/embc46164.2021.9630574

Cited by 6 publications

(7 citation statements)

References 19 publications

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“…In the reviewed studies, DS AI was used to improve sound quality, isolate sounds of interest, estimate vital signs, segment PCG heart sounds, and detect indicators of pathological conditions (e.g., murmurs, RD). [9][10][11][12]28,30,32,34,36,37,39,[43][44][45][46][47] These findings lay the groundwork for a plethora of possible AI tools and opportunities that could be developed to further advance automatic neonatal sound analysis. Such advancements have the potential to improve the timeliness of diagnosis and management for various neonatal medical conditions.…”

Section: Telemedicinementioning

confidence: 88%

“…A key advantage lies in the integration of AI within DS technology, enabling the improved acquisition of clear neonatal sounds and the automatic detection, characterization, and classification of neonatal sounds. [9][10][11][12]28,30,32,34,36,37,39,[43][44][45][46][47] This surpasses the capabilities of traditional acoustic stethoscopes and eliminates listener subjectivity. In the reviewed studies, DS AI was used to improve sound quality, isolate sounds of interest, estimate vital signs, segment PCG heart sounds, and detect indicators of pathological conditions (e.g., murmurs, RD).…”

Section: Telemedicinementioning

confidence: 99%

“…However, software refinement is needed due to limitations in accuracy arising from small datasets and low-quality recordings. 30,32,34,36,37,39…”

Section: Cardiac Soundsmentioning

confidence: 99%

“…Digital stethoscopes offer other benefits, including their application in neonatal telemedicine for infection control, their potential to enhance our scientific understanding of neonatal physiology, and the utilization of PCGs to capture additional cardiac features. 29,30,[32][33][34]36,37,39,41,42,[48][49][50][51] Several limitations in the literature impacted the accuracy and reliability of findings, thereby hindering the clinical application of DSs in neonates. Major limitations included small sample sizes, findings with an unclear clinical relevance, and low-quality recordings.…”

Section: Telemedicinementioning

confidence: 99%

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Digital Stethoscope Use in Neonates: A Systematic Review

Roff,

Slifirski,

Grooby

et al. 2023

Newborn

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Aim: To assess the evidence for the use of digital stethoscopes in neonates and evaluate whether they are effective, appropriate, and advantageous for neonatal auscultation. Methods: A systematic review and narrative synthesis of studies published between January 1, 1990 and May 29, 2023 was conducted following searches of MEDLINE, Embase, PubMed, Scopus, and Google Scholar databases, as well as trial registries. Results: Of 3,852 records identified, a total of 41 papers were eligible and included in the narrative synthesis. Thirteen records were non-full-text articles, either in the form of journal letters or conference abstracts, and these were included separately for completion purposes but may be unreliable. Twenty eight papers were full-text articles and were included in a full qualitative analysis. Digital stethoscopes have been studied in neonatology across various clinical areas, including artificial intelligence for sound quality assessment and chest sound separation (n = 5), cardiovascular sounds (n = 11), respiratory sounds (n = 4), bowel sounds (n = 4), swallowing sounds (n = 2), and telemedicine (n = 2). This paper discusses the potential utility of digital stethoscope technology for the interpretation of neonatal sounds for both humans and artificial intelligence. The limitations of current devices are also assessed. Conclusions:The utilization of digital stethoscopes in neonatology is an emerging field with a wide range of potential applications, which has the capacity to advance neonatal auscultation. Artificial intelligence and digital stethoscope technology offer novel objective avenues for automatic pathological sound detection. Further, digital stethoscopes may improve our scientific understanding of normal neonatal physiology and can be employed in telemedicine to facilitate remote medical access. Digital stethoscopes can also provide phonocardiograms, enabling enhanced interpretation of neonatal cardiac sounds. However, current digital stethoscopes necessitate refinement as they consistently produce low-quality sounds when used on neonates.

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Section: Telemedicinementioning

confidence: 88%

Section: Telemedicinementioning

confidence: 99%

“…However, software refinement is needed due to limitations in accuracy arising from small datasets and low-quality recordings. 30,32,34,36,37,39…”

Section: Cardiac Soundsmentioning

confidence: 99%

Section: Telemedicinementioning

confidence: 99%

See 2 more Smart Citations

Digital Stethoscope Use in Neonates: A Systematic Review

Roff,

Slifirski,

Grooby

et al. 2023

Newborn

View full text Add to dashboard Cite

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“…Some research has focused on utilizing Artificial Intelligence (AI) techniques, although more work is required in this field. Furthermore, there is a need for ample data for testing and training the machine and deep learning algorithms [19], [20]. This study tackles these problems by employing an AI algorithm for FHR detection dependent on PCG signals and providing sufficient data for training and evaluating the proposed work.…”

Section: Introductionmentioning

confidence: 99%

Adopting Artificial Intelligence Algorithms for Remote Fetal Heart Rate Monitoring and Classification using Wearable Fetal Phonocardiography

ABBURI¹,

Hatai²,

Samanta³

2023

Preprint

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<p>This work utilizes a wearable phonocardiogram belt for acquiring signals from remote areas. Four sequential methodologies are undergone in the proposed work for resilience, and effectiveness. Intelligent, and accurate FHR monitoring. At first, the noise and artifacts are removed by performing two levels pre-processing methodology method. The low frequency noises are removed by Chebyshev II High Pass Filter and high frequency noises are removed by a hybrid of EC2EMDAN- PS-MODWT filters respectively. In the next step, the denoised signals are segmented for reducing the complexity in which the segmentation is performed using Multi Agent Deep Q-Learning (MA-DQL) algorithm based on several constraints. Further, the segmented signal is provided for reducing the redundancies in cardiac cycles using Artificial Humming Bird Optimization (AHBO) algorithm. The segmented and non-redundant signals are converted into 3D spectrograms using a machine learning algorithm named Variational Auto Encoder-General Adversarial Networks (VAE-GAN) for analyzing the signals with better visual interpretation in 3D space. Finally, utilizing the 3D spectrogram analysis the feature extraction and classification is taken place by adopting a hybrid of Bidirectional Gated Recurrent Unit (BiGRU) and Multi Boosted Capsule Network (MBCapsNet) into three classes such as normal (110-160 BPM), abnormal (above 180 BPM), and Suspicious (fluctuates between normal and abnormal). The proposed work is implemented and simulated in the MATLAB R2020a tool and the performance is validated by adopting effective validation metrics. The outcomes demonstrate that the suggested work performs better than the current works. </p>

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