Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements

Al-Angari, Haitham M.; Kanitz, Gunter; Tarantino, Sergio; Cipriani, Christian

doi:10.1016/j.bspc.2016.01.011

Cited by 80 publications

(54 citation statements)

References 26 publications

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“…MI quantifies the amount of information that can be obtained about a random variable through another one [23]. TE quantifies the amount of information transfer from one variable to the other [24].…”

Section: Introductionmentioning

confidence: 99%

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Zheng

Pan

et al. 2017

Entropy

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Abstract:Obstructive sleep apnea (OSA) is a common sleep disorder that often associates with reduced heart rate variability (HRV) indicating autonomic dysfunction. HRV is mainly composed of high frequency components attributed to parasympathetic activity and low frequency components attributed to sympathetic activity. Although, time domain and frequency domain features of HRV have been used to sleep studies, the complex interaction between nonlinear independent frequency components with OSA is less known. This study included 30 electrocardiogram recordings (20 OSA patient recording and 10 healthy subjects) with apnea or normal label in 1-min segment. All segments were divided into three groups: N-N group (normal segments of normal subjects), P-N group (normal segments of OSA subjects) and P-OSA group (apnea segments of OSA subjects). Frequency domain indices and interaction indices were extracted from segmented RR intervals. Frequency domain indices included nuLF, nuHF, and LF/HF ratio; interaction indices included mutual information (MI) and transfer entropy (TE (H→L) and TE (L→H)). Our results demonstrated that LF/HF ratio was significant higher in P-OSA group than N-N group and P-N group. MI was significantly larger in P-OSA group than P-N group. TE (H→L) and TE (L→H) showed a significant decrease in P-OSA group, compared to P-N group and N-N group. TE (H→L) were significantly negative correlation with LF/HF ratio in P-N group (r = −0.789, p = 0.000) and P-OSA group (r = −0.661, p = 0.002). Our results indicated that MI and TE is powerful tools to evaluate sympathovagal modulation in OSA. Moreover, sympathovagal modulation is more imbalance in OSA patients while suffering from apnea event compared to free event.

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Section: Introductionmentioning

confidence: 99%

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Zheng

Pan

et al. 2017

Entropy

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“…The EMG signal deviates greatly from its baseline due to muscle contraction, the samples of EMG signals in different amplitudes can be used as an effective feature. In order to extract the feature, it must be set a threshold, and dived the distance between positive and negative thresholds into different amplitude segments, set the number of different amplitudes of EMG as the feature [5]. The threshold level and the number of segments are determined by experimentally.…”

Section: Advances In Intelligent Systems Research Volume 156mentioning

confidence: 99%

Research on EMG Signal Extraction Based on Biomimetic Hand

Xu¹,

Sun²,

Li³

2017

Proceedings of the 7th International Conference on Management, Education, Information and Control (MEICI 2017)

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Abstract. EMG is a signal which could reflect the information of human body's movement in residual limb muscle group. As a source of information, it could control the bionic prosthesis to complete the multi-degree of freedom of action. EMG prosthesis controlled the bionic prosthesis through the body's own physiological signal. It is remarkable significant for the disabled users that if the bionic prosthesis could understand the movement of the disabled users will. The purpose of the EMG signal acquisition is driving a bionic hand which could help the hand disabilities to achieve grasping, pushing, taking, holding etc. EMG prosthetic is a complex system, which involving multidisciplinary disciplines. And this paper mainly introduces the method of EMG extraction.

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“…The feature extraction stages generally utilize the vector space model (Salton et al, 1975) that makes use of the bag-of-words approach (Joachims, 1997). Finally, the feature selection stage typically uses the filter method such as document frequency (Azam and Yao, 2012;Yang and Pedersen, 1997), mutual information (Tang et al, 2019;Al-Angari et al, 2016;Liu et al, 2009), information gain (Mendez et al, 2019;Lee and Lee, 2006), chi-square (Asdaghi and Soleimani, 2019;Chen and Chen, 2011) and Odds Ratio (Raza and Qamar, 2016;Feng et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Review of feature extraction approaches on biomedical text classification

al.¹

2020

Int. j. adv. appl. sci.

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The overcoming volume of online biomedical literature causes congestion of data and difficulties in organizing these documents and also to retrieve the required documents from the database, especially in the Medline database. One of the solutions to surpass the overwhelming of documents is to apply classification. However, each document must be represented by a set of terminology or feature vectors. The identification of terminology or feature from biomedical literature is one of the most important and challenging tasks in text classification. This is due to a large number of new features and entities that appear in the biomedical domain. In addition, combining sets of features from different terminological resources leads to naming conflicts such as homonymous use of names and terminological ambiguities. Therefore, the purpose of this research is to investigate and evaluate the effective ways for extracting the relevant and meaningful features in order to increase the classification accuracy and improve the performance of web searches. Towards this effort, we conduct several classification experiments to evaluate and compare the effectiveness of feature extraction approaches for extracting the relevant and informative features from the biomedical literature. For our experiments, we use two different sets of features, which are a set of features that are extracted using the Genia tagger tool and set of features that are extracted by medical experts from Pusat Perubatan Universiti Kebangsaan Malaysia (PPUKM). The results show the performance of classification using features that are extracted by medical experts outperform the performance of classification using the Genia Tagger tool when applying feature selection method.

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Distance and mutual information methods for EMG feature and channel subset selection for classification of hand movements

Cited by 80 publications

References 26 publications

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Use of Mutual Information and Transfer Entropy to Assess Interaction between Parasympathetic and Sympathetic Activities of Nervous System from HRV

Research on EMG Signal Extraction Based on Biomimetic Hand

Review of feature extraction approaches on biomedical text classification

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