Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model

Masoudi, Samira; Montazeri, N.; Shamsollahi, Mohammad Bagher; Ge, Di; Beuchée, Alain; Pladys, Patrick; Hernández, Alfredo

doi:10.1109/isspit.2013.6781887

Cited by 11 publications

(10 citation statements)

References 13 publications

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“…A simple thresholding approach takes a couple of seconds detecting a rise in RR signal and mainly uses the amplitude, yet the proposed methods are expected to learn the dynamic and to detect the change in observation earlier with better precision. The results show that our proposed wave-based method is capable of detecting AB faster than the other methods such as (Masoudi et al 2013, Altuve et al 2015.…”

Section: Discussionmentioning

confidence: 87%

“…The application of HMM-based approaches had been previously reported (Altuve et al 2011, Masoudi et al 2013, Altuve et al 2015. The major drawback of such methods is that each beat is represented by finite number of features, which results in methods using only the inter-beat information, like our R-based method.…”

Section: Discussionmentioning

confidence: 99%

“…This approach achieves 74.20% sensitivity and 92.77% specificity. The same strategy is also used in Masoudi et al (2013) employing a coupled HMM (CHMM) to obtain sensitivity and specificity as 84.92% and 94.17%, respectively. In Altuve et al (2015), some improvements in processing of observations, such as quantization are suggested in order to obtain more detection precision (88.66% sensitivity and 92.27% specificity).…”

Section: Introductionmentioning

confidence: 99%

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Switching Kalman filter based methods for apnea bradycardia detection from ECG signals

Ghahjaverestan

Shamsollahi

et al. 2015

Physiol. Meas.

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Apnea bradycardia (AB) is an outcome of apnea occurrence in preterm infants and is an observable phenomenon in cardiovascular signals. Early detection of apnea in infants under monitoring is a critical challenge for the early intervention of nurses. In this paper, we introduce two switching Kalman filter (SKF) based methods for AB detection using electrocardiogram (ECG) signal.The first SKF model uses McSharry's ECG dynamical model integrated in two Kalman filter (KF) models trained for normal and AB intervals. Whereas the second SKF model is established by using only the RR sequence extracted from ECG and two AR models to be fitted in normal and AB intervals. In both SKF approaches, a discrete state variable called a switch is considered that chooses one of the models (corresponding to normal and AB) during the inference phase. According to the probability of each model indicated by this switch, the model with larger probability determines the observation label at each time instant.It is shown that the method based on ECG dynamical model can be effectively used for AB detection. The detection performance is evaluated by comparing statistical metrics and the amount of time taken to detect AB compared with the annotated onset. The results demonstrate the superiority of this method, with sensitivity and specificity 94.74[Formula: see text] and 94.17[Formula: see text], respectively. The presented approaches may therefore serve as an effective algorithm for monitoring neonates suffering from AB.

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Section: Discussionmentioning

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Switching Kalman filter based methods for apnea bradycardia detection from ECG signals

Ghahjaverestan

Shamsollahi

et al. 2015

Physiol. Meas.

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“…where J n (µ n,l , p n,l,k ) in the cost function is the J n expression in (12), when g n and P n , respectively, are replaced with µ n,l (the corresponding quantized channel gain) and p n,l,k (the transmit power corresponding to channel state l and battery state k). The optimization variables are N ×L×K points of the power map consisting of points p n,l,k .…”

Section: Formalizing and Solving Optimal Transmitmentioning

confidence: 99%

Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

Ardeshiri

Yazdani

Vosoughi

2019

2019 IEEE Global Communications Conference (GLOBECOM)

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We consider a wireless sensor network, consisting of N heterogeneous sensors and a fusion center (FC), that is tasked with solving a binary distributed detection problem. Each sensor is capable of harvesting randomly arrived energy and storing it in a finite capacity battery. Sensors are informed of their fading channel states, via a bandwidth-limited feedback channel from the FC. Each sensor has the knowledge of its current battery state and its channel state (quantized channel gain). Our goal is to study how sensors should choose their transmit powers such that J-divergence of the received signal densities under two hypotheses at the FC is maximized, subject to certain (battery and power) constraints. We derive the optimal power map, which depends on the energy arrival rate, the battery capacity, and the battery states probabilities at the steady state. Using the optimal power map, each sensor optimally adapts its transmit power, based on its battery state and its channel state. Our simulation results demonstrate the performance of our proposed power adaptation scheme for different system parameters.

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“…The most common approaches for heart rate characterization in this context are simply based on the detection of bradycardia, by applying a fixed or an adaptive threshold on heart rate time-series [6], [7]. In our previous works, we have proposed different methods to improve the characterization of heart rate dynamics of AB episodes using abrupt-change detection methods [8], and different kinds of unidimensional hidden Markov models (HMM) [9]- [11]. In this paper, we present a significant improvement of our previous methods, by proposing a new methodological framework for the characterization of multivariate time-series dynamics, based on a particular kind of Bayesian network (BN), called the coupled hidden Markov model (CHMM).…”

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confidence: 99%

Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection

Ghahjaverestan

Masoudi

Shamsollahi

et al. 2016

IEEE J. Biomed. Health Inform.

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In this paper, we present a novel framework for the coupled hidden Markov model (CHMM), based on the forward and backward recursions and conditional probabilities, given a multidimensional observation. In the proposed framework, the interdependencies of states networks are modeled with Markovian-like transition laws that influence the evolution of hidden states in all channels. Moreover, an offline inference approach by maximum likelihood estimation is proposed for the learning procedure of model parameters. To evaluate its performance, we first apply the CHMM model to classify and detect disturbances using synthetic data generated by the FitzHugh-Nagumo model. The average sensitivity and specificity of the classification are above 93.98% and 95.38% and those of the detection reach 94.49% and 99.34%, respectively. The method is also evaluated using a clinical database composed of annotated physiological signal recordings of neonates suffering from apnea-bradycardia. Different combinations of beat-to-beat features extracted from electrocardiographic signals constitute the multidimensional observations for which the proposed CHMM model is applied, to detect each apnea bradycardia episode. The proposed approach is finally compared to other previously proposed HMM-based detection methods. Our CHMM provides the best performance on this clinical database, presenting an average sensitivity of 95.74% and specificity of 91.88% while it reduces the detection delay by -0.59 s.

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Early detection of apnea-bradycardia episodes in preterm infants based on coupled hidden Markov model

Cited by 11 publications

References 13 publications

Switching Kalman filter based methods for apnea bradycardia detection from ECG signals

Switching Kalman filter based methods for apnea bradycardia detection from ECG signals

Power Adaptation for Distributed Detection in Energy Harvesting WSNs with Finite-Capacity Battery

Coupled Hidden Markov Model-Based Method for Apnea Bradycardia Detection

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