Analysis on Wavelet Feature and Softmax Discriminant Classifier for the detection of epilepsy

Deepa, R.; Rajaguru, Harikumar; Babu, C. Ganesh

doi:10.1088/1757-899x/1084/1/012036

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…Therefore, the energy characteristics of the pulse signal in different frequency bands were extracted by 8-layer wavelet packet decomposition. For wavelet feature extraction of biological signals, sym8 outperforms Haar, dB2, and dB4 in terms of the performance index, specificity, sensitivity, accuracy, time delay, and quality assessment of wavelets [ 17 ]. A sym8 wavelet has better regularity and symmetry, which can reduce the phase distortion caused by the calculation.…”

Section: Methodsmentioning

confidence: 99%

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study

Yan¹,

Cai²,

Chen³

et al. 2021

JMIR Med Inform

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Background In pulse signal analysis and identification, time domain and time frequency domain analysis methods can obtain interpretable structured data and build classification models using traditional machine learning methods. Unstructured data, such as pulse signals, contain rich information about the state of the cardiovascular system, and local features of unstructured data can be extracted and classified using deep learning. Objective The objective of this paper was to comprehensively use machine learning and deep learning classification methods to fully exploit the information about pulse signals. Methods Structured data were obtained by using time domain and time frequency domain analysis methods. A classification model was built using a support vector machine (SVM), a deep convolutional neural network (DCNN) kernel was used to extract local features of the unstructured data, and the stacking method was used to fuse the above classification results for decision making. Results The highest average accuracy of 0.7914 was obtained using only a single classifier, while the average accuracy obtained using the ensemble learning approach was 0.8330. Conclusions Ensemble learning can effectively use information from structured and unstructured data to improve classification accuracy through decision-level fusion. This study provides a new idea and method for pulse signal classification, which is of practical value for pulse diagnosis objectification.

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Section: Methodsmentioning

confidence: 99%

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study

Yan¹,

Cai²,

Chen³

et al. 2021

JMIR Med Inform

View full text Add to dashboard Cite

show abstract

“…In this paper, the BN-inception network with high accuracy and efficiency extracts the static spatial features of motion action, whose network structure is shown in Table 1. Specific extraction steps are as follows [19][20][21][22]:…”

Section: Static Spatial Characteristic Extractionmentioning

confidence: 99%

“…en to calculate the classification score, the temporal overlap values of different time periods, as in equations ( 21) and (22), and compare them with the set threshold.…”

Section: Time-based Semantic Continuity Optimizationmentioning

confidence: 99%

The Motor Action Analysis Based on Deep Learning

Zhang

2022

Scientific Programming

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For the slow speed and low accuracy of slow motor action recognition methods, this study proposes a motor action analysis method based on the CNN network and the softmax classification model. First, in order to obtain motor action feature information, by using static spatial features of BN-inception based on CNN network extracted actions and high-dimensional features of 3D ConvNet, then based on softmax classifier structure and realizing taxonomic recognition of the motor actions. Finally, through the decision-layer fusion and time semantic continuity optimization strategy, the motion action recognition accuracy is further improved and the more efficient motion action classification recognition is realized. The results show that the proposed method can complete the motor action analysis and achieve the classification recognition accuracy to 83.11%, which has certain practical value.

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Detection Algorithm of Laboratory Abnormal Behavior Based on Two-stream Convolutional Neural Network

Gao

2024

2024 4th Asia-Pacific Conference on Communications Technology and Computer Science (ACCTCS)

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Analysis on Wavelet Feature and Softmax Discriminant Classifier for the detection of epilepsy

Cited by 4 publications

References 3 publications

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study

Ensemble Learning-Based Pulse Signal Recognition: Classification Model Development Study

The Motor Action Analysis Based on Deep Learning

Detection Algorithm of Laboratory Abnormal Behavior Based on Two-stream Convolutional Neural Network

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