Combining wavelet transform and markov model to forecast traffic volume

Epilepsy is the most common neurological disorder with 40-50 million people suffering with it worldwide. Epilepsy is not life threatening but it disables the person to a greater extent due to its uncertainty of occurrences. Epilepsy is detected by repeated occurrences of seizure. Seizure can be generated in brain due to abnormal activity of group of neurons caused by brain tumor, genetic problem, infection, hemorrhage etc. Seizure can be detected by observing the variation in Electroencephalogram (EEG) signal. Focal seizure is defined as seizure localized in one lobe of brain. In this chapter discrete wavelet transform and Hidden Markov Model based focal seizure detection method is proposed for epileptic focus localization. EEG signal was decomposed up to level 5 using dual tree complex wavelet transform and entropy features such as collision entropy, minimum and modified sample entropy were extracted. Hidden Markov model was used for classification purpose. Maximum 80% accuracy was achieved in detecting focal and non-focal EEG signal.

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Prediction of Traffic Volume in Highway Tunnel Group Region Based on Grey Markov Model

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.712-715.2981 ◽

2013 ◽

Vol 712-715 ◽

pp. 2981-2985 ◽

Cited By ~ 1

Author(s):

Wei Zhan ◽

Qing Lu ◽

Yue Quan Shang

Keyword(s):

Markov Model ◽

Early Warning ◽

Prediction Accuracy ◽

Development Trend ◽

Traffic Volume ◽

Grey Model ◽

Prediction Methods ◽

Highway Tunnel ◽

The Development Trend ◽

Highway Management

Based on the investigation and analysis of the traffic volume in highway tunnel group region, the development trend of traffic volume is analyzed by Grey model. Then the prediction accuracy is improved by Markov optimization. The method in this paper has a better prediction accuracy and practicality in a period than other common prediction methods. It can be used for the prediction analysis of traffic volume and for early warning by highway management.

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Modeling Heart Rate Variability with a HMM-based Neural Network

Journal of Robotics and Mechatronics ◽

10.20965/jrm.2002.p0625 ◽

2002 ◽

Vol 14 (6) ◽

pp. 625-632

Author(s):

Osamu Fukuda ◽

◽

Yoshihiko Nagata ◽

Keiko Homma ◽

Toshio Tsuji ◽

...

Keyword(s):

Neural Network ◽

Heart Rate ◽

Heart Rate Variability ◽

Wavelet Transform ◽

Markov Model ◽

Hidden Markov Model ◽

Mental Workload ◽

Hidden Markov ◽

Subjective Rating ◽

Physical Conditions

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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