Apnea bradycardia detection based on new coupled hidden semi Markov model

Ghahjaverestan, Nasim Montazeri; Shamsollahi, Mohammad Bagher; Ge, Di; Beuchée, Alain; Hernández, Alfredo

doi:10.1007/s11517-020-02277-8

Cited by 6 publications

(5 citation statements)

References 22 publications

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“…Heart rate alarms are currently the most reliable way to detect the cardio-respiratory events associated with prematurity. The choice of the fusion method has the advantage to be easy to implement and embeddable into devices with low computational resources, when compared to other more sophisticated methods 16 , 17 , 19 – 21 . Portet et al also observed a small improvement in the time delay for detection of bradycardia using a decision tree approach 20 .…”

Section: Discussionmentioning

confidence: 99%

“…Portet et al also observed a small improvement in the time delay for detection of bradycardia using a decision tree approach 20 . Our group has proposed a set of hidden and coupled semi-Markovian methods that also provide earlier detection delays with increased performance levels 16 , 17 , 21 . However, none of these works have evaluated performance sensitivity with respect to the different definitions of bradycardia and under real-life conditions.…”

Section: Discussionmentioning

confidence: 99%

“…In both of these works, data from 10 newborns have been processed off-line, in batch mode, following a phase of manual verification for the rejection of artifacts due to movement, disconnection, or erroneous peak detections. A series of papers from our team have been focused on the early detection of apnea bradycardia events, but based on original Markov model architectures 16 , 17 . These methods have been evaluated using data from 32 preterm newborns, again, using a batch, post-processing mode.…”

Section: Introductionmentioning

confidence: 99%

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Early bradycardia detection and therapeutic interventions in preterm infant monitoring

Doyen

Hernández

Flamant

et al. 2021

Sci Rep

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In very preterm infants, cardio-respiratory events and associated hypoxemia occurring during early postnatal life have been associated with risks of retinopathy, growth alteration and neurodevelopment impairment. These events are commonly detected by continuous cardio-respiratory monitoring in neonatal intensive care units (NICU), through the associated bradycardia. NICU nurse interventions are mainly triggered by these alarms. In this work, we acquired data from 52 preterm infants during NICU monitoring, in order to propose an early bradycardia detector which is based on a decentralized fusion of three detectors. The main objective is to improve automatic detection under real-life conditions without altering performance with respect to that of a monitor commonly used in NICU. We used heart rate lower than 80 bpm during at least 10 sec to define bradycardia. With this definition we observed a high rate of false alarms (64%) in real-life and that 29% of the relevant alarms were not followed by manual interventions. Concerning the proposed detection method, when compared to current monitors, it provided a significant decrease of the detection delay of 2.9 seconds, without alteration of the sensitivity (97.6% vs 95.2%) and false alarm rate (63.7% vs 64.1%). We expect that such an early detection will improve the response of the newborn to the intervention and allow for the development of new automatic therapeutic strategies which could complement manual intervention and decrease the sepsis risk.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Early bradycardia detection and therapeutic interventions in preterm infant monitoring

Doyen

Hernández

Flamant

et al. 2021

Sci Rep

Self Cite

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“…For simulated data, the proposed algorithm was able to detect the desired dynamics with 96.67% sensitivity and 98.98% specificity. e results show that the CHSMM-based algorithm is a robust tool for monitoring apnea remission in preterm infants [4]. Zhang started with the influence of cultural context on Chinese-English translation and discusses the context in Chinese-English translation combined with practical work experience as well as the understanding and practice of translation activities from the perspective of cultural translation.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Zhang

2022

Security and Communication Networks

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Speech recognition is the most important research direction in human-computer interaction. It is the key to the connection between human beings and machines and the expression of intelligence and automation in the information society. Taking English as the research object, using the related knowledge of speech recognition, it is based on the hidden Markov model technology of deep learning and clustering analysis algorithm and evaluated according to the cross-language English phonemic recognition system of sparse autoencoder (SA) method. By studying the speech recognition algorithm of the English translation, the influence of the speech recognition environment on the accuracy of speech recognition is confirmed. This provides a direction for humans to study speech recognition at a deeper level. Based on the language model of Transformer and the language model based on Seq2Seq, it sets different vocabularies, and the data are collected in the laboratory and outdoors, respectively, and the posttest template library is formed after collection. In the task of restoring phonetic symbols to English characters when phonemes are modeling units, the error rate is the lowest. The error rate on the test set reached 9.54%, which was 6.97 percentage points higher than that of the syllable modeling unit.

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“…Additionally, episodes of apnea-bradycardia have been observed in various studies [32], [33], including one using Coupled Hidden Semi-Markov Models (CHSMM) [34] and another employing layered HMMs, both based on electrocardiographic data. This detection contributes to the rapid diagnosis of this phenomenon in premature infants, a population where it occurs most frequently, thus avoiding neurological developmental issues and mortality [32], [33], [35].…”

Section: Research Backgroundmentioning

confidence: 99%

Hidden Markov Models for early detection of cardiovascular diseases

Núñez Mejía

2023

ing. Solidar

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Introduction: This article, developed between 2022 and 2023 within the framework of Applied Stochastic Processes by the SciBas group at the Universidad Distrital Francisco José de Caldas, focuses on the role of Hidden Markov Models (HMM) in predicting cardiovascular diseases. Problem: The addressed issue is the need to enhance the early detection of heart diseases, emphasizing how HMM can address uncertainty in clinical data and detect complex patterns. Objective: To evaluate the use of Hidden Markov Models (HMM) in the analysis of electrocardiograms (ECG) for the early detection of cardiovascular diseases. Methodology: The methodology comprises a literature review concerning the relationship between HMM and cardiovascular diseases, followed by the application of HMM to prevent heart attacks and address uncertainty in clinical data. Results: The findings indicate that HMM is effective in preventing heart diseases, yet its effectiveness is contingent upon data quality. These results are promising but not universally applicable. Conclusions: In summary, this study underscores the utility of HMM in early infarction detection and its statistical approach in medicine. It is emphasized that HMM is not infallible and should be complemented with other clinical options and assessment methods in real-world situations. Originality: This work stands out for its statistical and probabilistic approach in the application of Hidden Markov Models (HMM) in medical analysis, offering an innovative perspective and enhancing the understanding of their utility in the field of medicine. Limitations: It is recognized that there are limitations, such as dependence on data quality and variable applicability in clinical cases. These limitations should be considered in the context of their implementation in medical practice.

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Apnea bradycardia detection based on new coupled hidden semi Markov model

Cited by 6 publications

References 22 publications

Early bradycardia detection and therapeutic interventions in preterm infant monitoring

Early bradycardia detection and therapeutic interventions in preterm infant monitoring

Hybrid Algorithm for English Translation Speech Recognition Based on Deep Learning Model and Clustering

Hidden Markov Models for early detection of cardiovascular diseases

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