Fuzzy logic based driving behavior monitoring using hidden Markov models

2012 ◽  
Author(s):  
Bing-Fei Wu ◽  
Ying-Han Chen ◽  
Chung-Hsuan Yeh
Keyword(s):  
Fuzzy Logic ◽  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Driving Behavior ◽  
Behavior Monitoring

Driving Behavior Modeling Using Autoregressive Input-Output Hidden Markov Models

2019 ◽  
Vol 2019 (0) ◽  
pp. 2A1-E05
Author(s):  
Naoki AKAI ◽  
Takatsugu HIRAYAMA ◽  
Luis Yoichi MORALES ◽  
Yasuhiro AKAGI ◽  
Hailong LIU ◽  
...  
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov ◽  
Driving Behavior ◽  
Behavior Modeling ◽  
Input Output

scholarly journals Diagnosis of Left Ventricular Hypertrophy Based on Electrocardiogram Signal and Fuzzy Logic

2019 ◽  
Vol 35 (2) ◽  
Author(s):  
Tran Nhu Chi ◽  
Nguyen Thi Thanh Van ◽  
Le Van Chieu
Keyword(s):  
Signal Processing ◽  
Fuzzy Logic ◽  
Left Ventricular Hypertrophy ◽  
Hidden Markov Models ◽  
Ventricular Hypertrophy ◽  
Markov Models ◽  
Hidden Markov ◽  
Publishing House ◽  
Left Ventricular ◽  
Ecg Signal

Sokolow – Lyon index in detection of left ventricular hypertrophy is a hard limited index, so the clinical manifestation of the disease can be ignored when the measured index is near the threshold. Several proposed studies incorporate multiple index to improve diagnostic quality. However, the process of examination and diagnosis will be longer due to the need to collect more data. To solve this problem, the paper proposes a method of classifying left ventricular hypertrophy using fuzzy logic combining with digital signal processing techniques. The proposed method mainly uses the Sokolov-Lyon index (SV1+RV5/V6 ≥ 35 mm) for major changes in ECG signal but with four soft thresholds corresponding to the different clinical manifestations of the disease. In addition, a program is written in C++ language with QT Creator compiler also is developed to implement the algorithm. From there, the doctors can refer and propose to the patient's treatment regimen. Keywords ECG, left ventricular hypertrophy, signal processing, fuzzy logic. References [1] Malcolm S. Thaler, The only EKG book, seventh ed. Lippincott Williams & Wilinks, Philadelphia, 2012. [2] Vakili BA, Okin PM, Devereux RB, Prognostic implications of left ventricular hypertrophy, Am Heart J, 141(3) (2001) 334-341.https://doi.org/10.1067/mhj.2001.113218.[3] Tran Do Trinh, Tran Van Dong, How to read EGC signal, Medical Publishing House, 2011 (in Vietnamese).[4] Himanshu Gothwal1, Silky Kedawat1, Rajesh Kumar, Cardiac arrhythmias detection in an ECG beat signal using fast fourier transform and artificial neural network, J. Biomedical Science and Engineering 4 (2011) 289-296.https://doi.org/10.4236/jbise.2011.44039.[5] El-Sayed A. El-Dahshan, Genetic algorithm and wavelet hybrid scheme for ECG signal denoising, Journal of Telecommunications Systems 46(3) (2011) 209-215.https://doi.org/10.1007/s11235-010-9286-2.[6] C. Li, C. Zheng, and C. Tai, Detection of ECG characteristic points using wavelet transforms, IEEE Trans.Biomed. Eng 42(1) (1995) 21-28.https://doi.org/10.1109/10.362922.[7] A.K.M. Fazlul Haque1, Md. Hanif Ali1, M. Adnan Kiber2 and Md. Tanvir Hasan, Detection of small variations of ECG features using Wavelet, ARPN Journal of Engineering and Applied Sciences 4(6) (2009) 27-30.[8] Krimi Samar, Ouni Kas, Noureddine Ellouze, Using Hidden Markov Models for ECG Characterisation, Hidden Markov Models, Theory and Applications, 4 (2011) 151-165.https://doi.org/10.5772/13916.[9] Van Ngoc Tuyet, Bang Ai Vien, Nguyen Van Tri, Medical Journal Ho Chi Minh city, Diagnosis of left ventricular hypertrophy by ECG I 15(1) (2011) 135-140 (in Vietnamese).[10] Buckley, James J., Eslami, Esfandiar, Introduction to Fuzzy Logic and Fuzzy Sets, Physica-Verlag Heidelberg, Berlin, 2002. [11] Phan Xuan Minh, Nguyen Doan Phuoc, Fuzzy Control Theory, Science and Technics Publishing House, Ha Noi, 2006 (in Vietnamese).  

Estimating Personality Impression from Speech Record Using Hidden Markov Models

2015 ◽  
Vol 135 (12) ◽  
pp. 1517-1523 ◽  
Author(s):  
Yicheng Jin ◽  
Takuto Sakuma ◽  
Shohei Kato ◽  
Tsutomu Kunitachi
Keyword(s):  
Hidden Markov Models ◽  
Markov Models ◽  
Hidden Markov

Hidden Markov Processes

2014 ◽  
Author(s):  
M. Vidyasagar
Keyword(s):  
Hidden Markov Models ◽  
Markov Processes ◽  
Viterbi Algorithm ◽  
Markov Models ◽  
Hidden Markov ◽  
Local Alignment ◽  
Biological Applications ◽  
Standard Material ◽  
Hidden Markov Processes ◽  
Genomics And Proteomics

This book explores important aspects of Markov and hidden Markov processes and the applications of these ideas to various problems in computational biology. It starts from first principles, so that no previous knowledge of probability is necessary. However, the work is rigorous and mathematical, making it useful to engineers and mathematicians, even those not interested in biological applications. A range of exercises is provided, including drills to familiarize the reader with concepts and more advanced problems that require deep thinking about the theory. Biological applications are taken from post-genomic biology, especially genomics and proteomics. The topics examined include standard material such as the Perron–Frobenius theorem, transient and recurrent states, hitting probabilities and hitting times, maximum likelihood estimation, the Viterbi algorithm, and the Baum–Welch algorithm. The book contains discussions of extremely useful topics not usually seen at the basic level, such as ergodicity of Markov processes, Markov Chain Monte Carlo (MCMC), information theory, and large deviation theory for both i.i.d and Markov processes. It also presents state-of-the-art realization theory for hidden Markov models. Among biological applications, it offers an in-depth look at the BLAST (Basic Local Alignment Search Technique) algorithm, including a comprehensive explanation of the underlying theory. Other applications such as profile hidden Markov models are also explored.

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