Modeling Heart Rate Variability with a HMM-based Neural Network

This paper proposes a method of modeling heart rate variability combining wavelet transform with a neural network based on a hidden Markov model. The proposed method has the following features: 1. The wavelet transform is used for feature extraction to extract the local change of heart rate variability in the timefrequency domain. 2. A new recurrent neural network incorporating a hidden Markov model is used to model the different patterns of heart rate variability caused by individual variations, physical conditions and so on. In experiments, five subjects were subjected to a mental workload, and the proposed method was used map subjective rating scores of their mental stress and the pattern of heart rate variability. Experiments confirmed that the proposed method achieved highly accurate modeling.

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Forecasting of heart rate variability using wrist-worn heart rate monitor based on hidden Markov model

10.23919/elinfocom.2018.8330626 ◽

2018 ◽

Cited By ~ 3

Author(s):

Sanghun Yun ◽

Chang-Sik Son ◽

Sang-Ho Lee ◽

Won-Seok Kang

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Markov Model ◽

Hidden Markov Model ◽

Hidden Markov ◽

Heart Rate Monitor

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A Hybrid Hidden Markov Model for Pipeline Leakage Detection

Applied Sciences ◽

10.3390/app11073138 ◽

2021 ◽

Vol 11 (7) ◽

pp. 3138

Author(s):

Mingchi Zhang ◽

Xuemin Chen ◽

Wei Li

Keyword(s):

Neural Network ◽

Markov Model ◽

Hidden Markov Model ◽

Gaussian Mixture Model ◽

Mixture Model ◽

Deep Neural Network ◽

Hidden Markov ◽

Gaussian Mixture ◽

State Sequence ◽

Non Linear

In this paper, a deep neural network hidden Markov model (DNN-HMM) is proposed to detect pipeline leakage location. A long pipeline is divided into several sections and the leakage occurs in different section that is defined as different state of hidden Markov model (HMM). The hybrid HMM, i.e., DNN-HMM, consists of a deep neural network (DNN) with multiple layers to exploit the non-linear data. The DNN is initialized by using a deep belief network (DBN). The DBN is a pre-trained model built by stacking top-down restricted Boltzmann machines (RBM) that compute the emission probabilities for the HMM instead of Gaussian mixture model (GMM). Two comparative studies based on different numbers of states using Gaussian mixture model-hidden Markov model (GMM-HMM) and DNN-HMM are performed. The accuracy of the testing performance between detected state sequence and actual state sequence is measured by micro F1 score. The micro F1 score approaches 0.94 for GMM-HMM method and it is close to 0.95 for DNN-HMM method when the pipeline is divided into three sections. In the experiment that divides the pipeline as five sections, the micro F1 score for GMM-HMM is 0.69, while it approaches 0.96 with DNN-HMM method. The results demonstrate that the DNN-HMM can learn a better model of non-linear data and achieve better performance compared to GMM-HMM method.

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