Automatic detection of the Cyclic Alternating Pattern of sleep and diagnosis of sleep-related pathologies (Preprint)

2021 ◽  
Author(s):  
Jiajia Cui ◽  
Zhipei Huang ◽  
Jiankang Wu
Keyword(s):  
Random Forest ◽  
Eye Movement ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Automatic Recognition ◽  
Rapid Eye Movement Sleep ◽  
Cyclic Alternating Pattern ◽  
Average Recognition Rate ◽  
The Stability ◽  
Cardiopulmonary System

UNSTRUCTURED The Cyclic Alternating Pattern is a periodic electroencephalogram activity occurring during No Rapid Eye Movement sleep. It is a marker of sleep instability and correlated with several sleep-related pathologies. In this article, considering the connection between heart and brain of people, by statistically analysising and comparing the cardiopulmonary characteristics of people with no pathology and patients with sleep-related diseases, an automatic recognition scheme of Cyclic Alternating Pattern is proposed based on the Cardiopulmonary Resonance Indices. Using improved Hidden Markov and Random Forest, the scheme combines both the measurements of coupling state and the stability of the cardiopulmonary system during sleep. The average recognition rate of A-phase reaches 84.67% and F1 score reaches 80.35%. Results show that our scheme could automatically recognize the Cyclic Alternating Pattern accurately, and diagnose insomnia and narcolepsy.

Automatic detection of the Cyclic Alternating Pattern of sleep and diagnosis of sleep-related pathologies (Preprint)

2021 ◽  
Author(s):  
Jiajia Cui ◽  
Zhipei Huang ◽  
Jiankang Wu
Keyword(s):  
Random Forest ◽  
Eye Movement ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Automatic Recognition ◽  
Rapid Eye Movement Sleep ◽  
Cyclic Alternating Pattern ◽  
Average Recognition Rate ◽  
The Stability ◽  
Cardiopulmonary System

BACKGROUND The Cyclic Alternating Pattern is a periodic electroencephalogram activity occurring during No Rapid Eye Movement sleep. It is a marker of sleep instability and correlated with several sleep-related pathologies. OBJECTIVE The objective of our study is to automatic detect the Cyclic Alternating Pattern of sleep and to diagnose sleep-related pathologies based on ECG and respiratory signals. METHODS Considering the connection between heart and brain of people, by statistically analysising and comparing the cardiopulmonary characteristics of people with no pathology and patients with sleep-related diseases, an automatic recognition scheme of Cyclic Alternating Pattern is proposed based on the Cardiopulmonary Resonance Indices. Using improved Hidden Markov and Random Forest, the scheme combines both the measurements of coupling state and the stability of the cardiopulmonary system during sleep. RESULTS In this article, the average recognition rate of A-phase reaches 84.67% and F1 score reaches 80.35% on the CAP Sleep Database in MIT-BIH database. CONCLUSIONS The scheme could automatically recognize the Cyclic Alternating Pattern accurately, and diagnose insomnia and narcolepsy using ECG and respiratory signals.

Plastic Classification with X-ray Absorption Spectroscopy Based on Back Propagation Neural Network

2017 ◽  
Vol 71 (11) ◽  
pp. 2538-2548 ◽  
Author(s):  
Qian Wang ◽  
Xiaomei Wu ◽  
Lingcong Chen ◽  
Zheng Yang ◽  
Zheng Fang
Keyword(s):  
Absorption Spectroscopy ◽  
Recognition Rate ◽  
Back Propagation ◽  
Principal Component ◽  
Back Propagation Neural Network ◽  
X Ray ◽  
Analysis Methods ◽  
Average Recognition Rate ◽  
The Stability ◽  
X Ray Absorption

Currently, spectral analysis methods used in the classification of plastics have limitations that do not apply to opaque plastics or the stability of experimental results is not strong. In this paper, X-ray absorption spectroscopy (XAS) has been applied to classify plastics due to its strong penetrability and stability. Fifteen kinds of plastics are selected as specimens. X-ray, which is excited by a voltage of 60 kV, penetrated these specimens. The spectral data acquired by CdTe X-ray detector are processed by principal component analysis (PCA) and other data analysis methods. Then the back propagation neural networks (BPNN) algorithm is used to classify the processed data. The average recognition rate reached 96.95% and classification results of all types of plastic results were analyzed in detail. It indicates that XAS has the potential to classify plastics and that XAS can be used in some fields such as plastic waste sorting and recycling. At the same time, the technology of XAS, in the future, can also be used to classify more substances.

The Cyclic Alternating Pattern Plays a Gate-Control on Periodic Limb Movements During Non-Rapid Eye Movement Sleep

1996 ◽  
Vol 13 (4) ◽  
pp. 314-323 ◽  
Author(s):  
Liborio Parrino ◽  
Mirella Boselli ◽  
Giovanni Pino Buccino ◽  
Maria Cristina Spaggiari ◽  
Guido Di Giovanni ◽  
...  
Keyword(s):  
Eye Movement ◽  
Periodic Limb Movements ◽  
Rapid Eye Movement ◽  
Rapid Eye Movement Sleep ◽  
Limb Movements ◽  
Cyclic Alternating Pattern ◽  
Gate Control

scholarly journals Recognition of Badminton Shot Action Based on the Improved Hidden Markov Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chao Ma ◽  
Dayang Yu ◽  
Hao Feng
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Rapid Development ◽  
Recognition Rate ◽  
Sliding Window ◽  
Acceleration Sensor ◽  
Sports Activity ◽  
Average Recognition Rate ◽  
Day By Day

In recent years, with the rapid development of sports, the number of people playing various sports is increasing day by day. Among them, badminton has become one of the most popular sports because of the advantages of fewer restrictions on the field and ease of learning. This paper develops a wearable sports activity classification system for accurately recognizing badminton actions. A single acceleration sensor fixed on the end of the badminton racket handle is used to collect the data of the badminton action. The sliding window segmentation technique is used to extract the hitting signal. An improved hidden Markov model (HMM) is developed to identify standard 10 badminton strokes. These include services, forehand chop, backhand chop the goal, the forehand and backhand, forehand drive, backhand push the ball, forehand to pick, pick the ball backhand, and forehand. The experimental results show that the model designed can recognize ten standard strokes in real time. Compared with the traditional HMM, the average recognition rate of the improved HMM is improved by 7.3%. The comprehensive recognition rate of the final strokes can reach up to 95%. Therefore, this model can be used to improve the competitive level of badminton players.

scholarly journals Causality of cortical and cardiovascular activity during cyclic alternating pattern in non-rapid eye movement sleep

2021 ◽  
Vol 379 (2212) ◽  
Author(s):  
Simon Hartmann ◽  
Raffaele Ferri ◽  
Oliviero Bruni ◽  
Mathias Baumert
Keyword(s):  
Granger Causality ◽  
Eye Movement ◽  
Sleep Stage ◽  
High Amplitude ◽  
Rapid Eye Movement ◽  
Causal Relationships ◽  
Rapid Eye Movement Sleep ◽  
Causality Analysis ◽  
Cyclic Alternating Pattern ◽  
Eeg Activation

The dynamic interplay between central and autonomic nervous system activities plays a pivotal role in orchestrating sleep. Macrostructural changes such as sleep-stage transitions or phasic, brief cortical events elicit fluctuations in neural outflow to the cardiovascular system, but the causal relationships between cortical and cardiovascular activities underpinning the microstructure of sleep are largely unknown. Here, we investigate cortical–cardiovascular interactions during the cyclic alternating pattern (CAP) of non-rapid eye movement sleep in a diverse set of overnight polysomnograms. We determine the Granger causality in both 507 CAP and 507 matched non-CAP sequences to assess the causal relationships between electroencephalography (EEG) frequency bands and respiratory and cardiovascular variables (heart period, respiratory period, pulse arrival time and pulse wave amplitude) during CAP. We observe a significantly stronger influence of delta activity on vascular variables during CAP sequences where slow, low-amplitude EEG activation phases (A1) dominate than during non-CAP sequences. We also show that rapid, high-amplitude EEG activation phases (A3) provoke a more pronounced change in autonomic activity than A1 and A2 phases. Our analysis provides the first evidence on the causal interplay between cortical and cardiovascular activities during CAP. Granger causality analysis may also be useful for probing the level of decoupling in sleep disorders. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

scholarly journals Application of Random Forest and Hidden Markov Models for Automatic and Fast Classification of Power Quality Signals

2020 ◽  
Vol 9 (4) ◽  
pp. 2856-2862
Keyword(s):  
Wavelet Transform ◽  
Random Forest ◽  
Wavelet Transforms ◽  
Markov Models ◽  
Hidden Markov ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Second Generation Wavelet Transform ◽  
Quality Signals

In this paper, wavelet transform, namely the maximal overlap discrete Wavelet Transform (MODWT) and the second generation Wavelet Transform (SGWT) have been implemented. These wavelet transforms are applied to get selected features of the signals. Features are used as inputs to two types of classifiers namely, Hidden Markov Model (HMM) classifiers and the Random Forest (RF) classifier in the both absence and presence of Noise to evaluate the efficiency. The classification accuracy (CA) calculated using these classifiers clearly shows that the RF classifiers is a better classifier then the HMM classifier as it possess higher recognition rate at all levels of noise along with the pure PQ signals. Another important property of RF classifier is the proper classification of large number of class of both slow and the fast disturbances.

Gray-Level Co-occurrence Matrix and Random Forest Based Off-line Odia Handwritten Character Recognition

2019 ◽  
Vol 13 (2) ◽  
pp. 136-141 ◽  
Author(s):  
Abhisek Sethy ◽  
Prashanta Kumar Patra ◽  
Deepak Ranjan Nayak
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Character Recognition ◽  
Recognition Rate ◽  
Discrete Wavelet ◽  
Gray Level ◽  
Handwritten Character Recognition ◽  
Handwritten Character ◽  
Wide Range ◽  
Occurrence Matrix

Background: In the past decades, handwritten character recognition has received considerable attention from researchers across the globe because of its wide range of applications in daily life. From the literature, it has been observed that there is limited study on various handwritten Indian scripts and Odia is one of them. We revised some of the patents relating to handwritten character recognition. Methods: This paper deals with the development of an automatic recognition system for offline handwritten Odia character recognition. In this case, prior to feature extraction from images, preprocessing has been done on the character images. For feature extraction, first the gray level co-occurrence matrix (GLCM) is computed from all the sub-bands of two-dimensional discrete wavelet transform (2D DWT) and thereafter, feature descriptors such as energy, entropy, correlation, homogeneity, and contrast are calculated from GLCMs which are termed as the primary feature vector. In order to further reduce the feature space and generate more relevant features, principal component analysis (PCA) has been employed. Because of the several salient features of random forest (RF) and K- nearest neighbor (K-NN), they have become a significant choice in pattern classification tasks and therefore, both RF and K-NN are separately applied in this study for segregation of character images. Results: All the experiments were performed on a system having specification as windows 8, 64-bit operating system, and Intel (R) i7 – 4770 CPU @ 3.40 GHz. Simulations were conducted through Matlab2014a on a standard database named as NIT Rourkela Odia Database. Conclusion: The proposed system has been validated on a standard database. The simulation results based on 10-fold cross-validation scenario demonstrate that the proposed system earns better accuracy than the existing methods while requiring least number of features. The recognition rate using RF and K-NN classifier is found to be 94.6% and 96.4% respectively.

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