Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models

Lu, Yanzheng; Wang, Hong; Qi, Yangyang; Xi, Hailong

doi:10.1016/j.bspc.2020.102279

Cited by 34 publications

(12 citation statements)

References 50 publications

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“…Feature extraction is an important step in designing a computer-aided system. In different bio-signal processing research [ 7 , 8 , 9 , 10 , 17 , 42 , 43 ], such as iEEG, EMG, and EEG, a combination of the following feature extraction methods were proposed. To extract the features from each segment of iEEG signals, let us define each channel of the n -th subband as

, which can be represented as

, where L is the length of

.…”

Section: Methodsmentioning

confidence: 99%

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

Akter

Islam

Tanaka

et al. 2020

Entropy

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The design of a computer-aided system for identifying the seizure onset zone (SOZ) from interictal and ictal electroencephalograms (EEGs) is desired by epileptologists. This study aims to introduce the statistical features of high-frequency components (HFCs) in interictal intracranial electroencephalograms (iEEGs) to identify the possible seizure onset zone (SOZ) channels. It is known that the activity of HFCs in interictal iEEGs, including ripple and fast ripple bands, is associated with epileptic seizures. This paper proposes to decompose multi-channel interictal iEEG signals into a number of subbands. For every 20 s segment, twelve features are computed from each subband. A mutual information (MI)-based method with grid search was applied to select the most prominent bands and features. A gradient-boosting decision tree-based algorithm called LightGBM was used to score each segment of the channels and these were averaged together to achieve a final score for each channel. The possible SOZ channels were localized based on the higher value channels. The experimental results with eleven epilepsy patients were tested to observe the efficiency of the proposed design compared to the state-of-the-art methods.

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, which can be represented as

, where L is the length of

.…”

Section: Methodsmentioning

confidence: 99%

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

Akter

Islam

Tanaka

et al. 2020

Entropy

View full text Add to dashboard Cite

show abstract

“…The specific calculation formula is shown in Equations ( 4)-( 7). Another new index, F-Score, was introduced to combine sensitivity and accuracy of the model (Lu et al, 2021). The calculation formulas are shown as Equation (7), in which the letter β was used to adjust the weight and set as 1 in this paper.…”

Section: Damage Area-discrimination Modelingmentioning

confidence: 99%

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

Qiu

Zeng

Shao

et al. 2022

Journal of Food Science

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The surface of carp is easily damaged during the descaling process, which jeopardizes the quality and safety of carp products. Damage recognition realized by manual detection is an important factor restricting the automation in the pretreatment. For the commonly used methods of mechanical and water‐jet descaling, damage area recognition according to the hyperspectral data was proposed. Two discrimination models, including decision tree (DT) and self‐organizing feature mapping (SOM), were established to recognize the damaged and normal descaling area with the average spectral value. The damage‐discrimination model based on DT was determined to be the optimal one, which possessed the best model performance (accuracy = 96.7%, sensitivity = 96.7%, specificity = 96.7%, F1‐score = 96.7%). Considering the efficiency and precision of damage‐area recognition and visualization, the principal component analysis (PCA) combined with pixel values statistical analysis was used to reduce the dimension of hyperspectral images at the image level. Through statistical analysis, the value 0 was used as the threshold to distinguish the normal area and the damaged area in the PC image to achieve preliminary segmentation. Then, the spectral values of the initially discriminated damage area were input into the DT discrimination model to realize the final discriminant of damaged area. On this basis, the position information of the damaged area could be used to realize the visualization. The final visualization maps for mechanical and water‐jet descaling damage were obtained by image morphology processing. The average recognition accuracy can reach 94.9% and 90.3%, respectively. The results revealed that the hyperspectral imaging technique has great potential to recognize the carp damage area nondestructively and accurately under descaling processing. Practical Application This study demonstrated that hyperspectral imaging technique can realize the carp damage area detection nondestructively and accurately under descaling processing. With the advantages of nondestructive and rapid, hyperspectral imaging system and the method can be widely expanded and applied to the quality detection of other freshwater fish pretreatment.

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“…Compared with the single classifier, the MFR has better accuracy and generalization ability. 3) LSTM [41,42,43]. LSTM is a deep learning model for modeling time sequence data, which shows advantages in mining long-term dependence.…”

Section: Comparison Algorithmmentioning

confidence: 99%

Deep learning analysis based on multi-sensor fusion data for hemiplegia rehabilitation training system for stoke patients

Zhang

Zhang²

2021

Robotica

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By recognizing the motion of the healthy side, the lower limb exoskeleton robot can provide therapy to the affected side of stroke patients. To improve the accuracy of motion intention recognition based on sensor data, the research based on deep learning was carried out. Eighty healthy subjects performed gait experiments under five different gait environments (flat ground, 10 ${}^\circ$ upslope and downslope, and upstairs and downstairs) by simulating stroke patients. To facilitate the training and classification of the neural network, this paper presents template processing schemes to adapt to different data formats. The novel algorithm model of a hybrid network model based on convolutional neural network (CNN) and Long–short-term memory (LSTM) model is constructed. To mitigate the data-sparse problem, a spatial–temporal-embedded LSTM model (SQLSTM) combining spatial–temporal influence with the LSTM model is proposed. The proposed CNN-SQLSTM model is evaluated on a real trajectory dataset, and the results demonstrate the effectiveness of the proposed model. The proposed method will be used to guide the control strategy design of robot system for active rehabilitation training.

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Evaluation of classification performance in human lower limb jump phases of signal correlation information and LSTM models

Cited by 34 publications

References 50 publications

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

Statistical Features in High-Frequency Bands of Interictal iEEG Work Efficiently in Identifying the Seizure Onset Zone in Patients with Focal Epilepsy

Detection of physical descaling damage in carp based on hyperspectral images and dimension reduction of principal component analysis combined with pixel values

Deep learning analysis based on multi-sensor fusion data for hemiplegia rehabilitation training system for stoke patients

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