On sound-based interpretation of neonatal EEG

Gomez, Sergi; OrSullivan, Mark; Popovici, Emanuel; Mathieson, Sean; Boylan, Geraldine B.; Temko, Andriy

doi:10.1109/issc.2018.8585349

Cited by 4 publications

(2 citation statements)

References 18 publications

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“…Other sound assessment Several recent audio processing methods have been proposed regarding non-cry signals and concerning either pre-linguistic vocalizations (including cooing) (Fuller andHorii, 1986, 1988;Pokorny et al, 2016Pokorny et al, , 2018. Non-voice analyses were also proposed in different contexts such as external noise detection (Raboshchuk et al, 2018a,b), EEG sonification (Gomez et al, 2018) or lung sound assessment (Emmanouilidou et al, 2017).…”

Section: Automatic Cry Segmentationmentioning

confidence: 99%

“…Recently, marginal purposes were investigated through audio processing for example to detect neonatal seizures from EEG or to detect lung sounds abnormalities. EEG signal was converted into an audible audio signal (process is called sonification) in order to hear relative frequency change when a seizure occurred (Gomez et al, 2018). It was shown that sonification methods perform similarly well, with a smaller inter-observer variability in comparison with visual interpretation.…”

Section: Automatic Cry Segmentationmentioning

confidence: 99%

See 1 more Smart Citation

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

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Objective: Video and sound acquisition and processing technologies have seen great improvements in recent decades, with many applications in the biomedical area. The aim of this paper is to review the overall state of the art of advances within these topics in paediatrics and to evaluate their potential application for monitoring in the neonatal intensive care unit (NICU). Approach: For this purpose, more than 150 papers dealing with video and audio processing were reviewed. For both topics, clinical applications are described according to the considered cohorts—full-term newborns, infants and toddlers or preterm newborns. Then, processing methods are presented, in terms of data acquisition, feature extraction and characterization. Main results: The paper first focuses on the exploitation of video recordings; these began to be automatically processed in the 2000s and we show that they have mainly been used to characterize infant motion. Other applications, including respiration and heart rate estimation and facial analysis, are also presented. Audio processing is then reviewed, with a focus on the analysis of crying. The first studies in this field focused on induced-pain cries and the newest ones deal with spontaneous cries; the analyses are mainly based on frequency features. Then, some papers dealing with non-cry signals are also discussed. Significance: Finally, we show that even if recent improvements in digital video and signal processing allow for increased automation of processing, the context of the NICU makes a fully automated analysis of long recordings problematic. A few proposals for overcoming some of the limitations are given.

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Section: Automatic Cry Segmentationmentioning

confidence: 99%

Section: Automatic Cry Segmentationmentioning

confidence: 99%

Video and audio processing in paediatrics: a review

Cabon¹,

Porée²,

Simon³

et al. 2019

Physiol. Meas.

View full text Add to dashboard Cite

show abstract

A method for AI assisted human interpretation of neonatal EEG

Gómez-Quintana

O'Shea

Factor

et al. 2022

Sci Rep

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The study proposes a novel method to empower healthcare professionals to interact and leverage AI decision support in an intuitive manner using auditory senses. The method’s suitability is assessed through acoustic detection of the presence of neonatal seizures in electroencephalography (EEG). Neurophysiologists use EEG recordings to identify seizures visually. However, neurophysiological expertise is expensive and not available 24/7, even in tertiary hospitals. Other neonatal and pediatric medical professionals (nurses, doctors, etc.) can make erroneous interpretations of highly complex EEG signals. While artificial intelligence (AI) has been widely used to provide objective decision support for EEG analysis, AI decisions are not always explainable. This work developed a solution to combine AI algorithms with a human-centric intuitive EEG interpretation method. Specifically, EEG is converted to sound using an AI-driven attention mechanism. The perceptual characteristics of seizure events can be heard using this method, and an hour of EEG can be analysed in five seconds. A survey that has been conducted among targeted end-users on a publicly available dataset has demonstrated that not only does it drastically reduce the burden of reviewing the EEG data, but also the obtained accuracy is on par with experienced neurophysiologists trained to interpret neonatal EEG. It is also shown that the proposed communion of a medical professional and AI outperforms AI alone by empowering the human with little or no experience to leverage AI attention mechanisms to enhance the perceptual characteristics of seizure events.

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