Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

Aslan, Muzaffer

doi:10.35377/saucis.03.03.811480

Cited by 4 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“… 48 The SVM method generally finds the separating hyperplane between two or more classes with the maximum margin. 49 Suppose we have a dataset { x i , y i and i = 1, 2, …, M } with a class label of {−1, +1} that consists of M instances. Accordingly, the optimum hyperplane is shown in Equation ( 12 ) 50 :

where w and b symbolize the hyperplane's weight vector and the trend value, respectively.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

Aslan

2022

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

COVID‐19 has emerged as a global pandemic affecting the world, and its adverse effects on society still continue. So far, about 243.57 million people have been diagnosed with COVID‐19, of which about 4.94 million have died. In this study, a new model, called COVIDetNet, is proposed for automated COVID‐19 detection. A lightweight CNN architecture trained instead of the popular and pretrained convolution neural network (CNN) models such as VGG16, VGG19, AlexNet, ResNet50, ResNet100, and MobileNetV2 from scratch with chest x‐ray (CXR) images was designed. A new feature set was created by concatenating the features of all layers of the designed CNN architecture. Then, the most efficient features chosen among the features concatenating with the Relief feature selection algorithm were classified using the support vector machine (SVM) method. The experimental works were carried out on a public COVID‐19 CXR database. Experimental results demonstrated 99.24% accuracy, 99.60% specificity, 99.39% sensitivity, 99.04% precision, and an F1 score of 99.21%. Also, in comparison to AlexNet and VGG16 models, the deep feature extraction durations were reduced by approximately 6‐fold and 38‐fold, respectively. The COVIDetNet model provided a higher accuracy score than state‐of‐the‐art models when compared to multi‐class research studies. Overall, the proposed model will be beneficial for specialist medical staff to detect COVID‐19 cases, as it provides faster and higher accuracy than existing CXR‐based approaches.

show abstract

where w and b symbolize the hyperplane's weight vector and the trend value, respectively.…”

Section: Methodsmentioning

confidence: 99%

“…SVM is a supervised machine learning model based on statistical learning theory 48 . The SVM method generally finds the separating hyperplane between two or more classes with the maximum margin 49 . Suppose we have a dataset { x i , y i and i = 1, 2, …, M } with a class label of {−1, +1} that consists of M instances.…”

Section: Methodsmentioning

confidence: 99%

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

Aslan

2022

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…Elde edilen öznitelikler destek vektör makinesi, karar ağacı, k-En yakın komşu, ve doğrusal diskriminant analizi yöntemleri ile sınıflandırılmıştır. Aslan, temel el hareketlerinin belirlenmesini amaçlamıştır [25]. Bu amaçla dağılım entropisi ve normal kümülatif dağılım fonksiyonu öznitelik çıkarma için kullanılmıştır.…”

Section: Deneysel çAlışma Ve Sonuçlarunclassified

EMG Sinyalleri Kullanılarak GoogLeNet ve Çok Seviyeli DPD ile El Tutma Hareketlerinin Sınıflandırılması

ÖZKÜÇÜK

Alçın

Gençoğlu

2022

Fırat Üniversitesi Mühendislik Bilimleri Dergisi

View full text Add to dashboard Cite

Öz: Elektromiyografi (EMG) elektriksel aktiviteyi ölçmek için kullanılan bir yöntemdir. Bu yöntem günümüzde hastalık tespitinde kullanılmasıyla yaygınlaşmış olsa da robotik, protez kontrolü, video oyunları gibi popüler alanlarda yer edinmiştir. Bu çalışmada altı temel el hareketinin EMG sinyalleri kullanılarak sınıflandırılması amaçlanmıştır. Bu amaç doğrultusunda transfer öğrenme yaklaşımı kullanılmıştır. EMG sinyalleri çok seviyeli dalgacık paket dönüşümü (DPD) ile zaman-frekans (ZF) görüntülerine çevrilmiştir. Bütün kanallara ait ZF görüntülerinin %80'i birleştirilerek GoogLeNet mimarisini eğitmek için kullanılmıştır. Hareket tanımada başarımı artırmak için GoogLeNet'ten elde edilen derin öznitelikler Destek Vektör Makinesi (DVM) ile sınıflandırılmıştır. Önerilen yöntem altı temel el hareketini tanımada %98.833 doğruluk oranına sahiptir. Önerilen yöntem aynı veri setini kullanan çalışmalar ile karşılaştırılmıştır. Yapılan karşılaştırmalar sonucunda önerilen yöntemin mevcut yöntemlerden %0.8 daha yüksek performans sergilediği görülmüştür. Deneysel çalışmalar önerilen yaklaşımın EMG ile hareket tanımada kullanılabilecek etkin ve verimli bir yöntem olduğunu göstermiştir. .

show abstract

“…Karnam et al [ 14 ] proposed using a refined K-nearest neighbors (KNN) classifier in conjunction with ensemble energy features of the sEMG signal for hand activity classification, which achieved the highest accuracy of 88.8%. Aslan et al [ 15 ] performed a Support Vector Machine (SVM) gestures classifier after extracting features with Distribution Entropy (DisEn) and Normal Cumulative Distribution Function (NCDF) methods. Recently, Fatimah et al [ 16 ] decomposed the raw sEMG signals into a Fourier intrinsic band to extract statistical features for classification with SVM and KNN.…”

Section: Introductionmentioning

confidence: 99%

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition

Zhang,

Badesa,

Liu

et al. 2024

Sensors

View full text Add to dashboard Cite

Gesture recognition using electromyography (EMG) signals has prevailed recently in the field of human–computer interactions for controlling intelligent prosthetics. Currently, machine learning and deep learning are the two most commonly employed methods for classifying hand gestures. Despite traditional machine learning methods already achieving impressive performance, it is still a huge amount of work to carry out feature extraction manually. The existing deep learning methods utilize complex neural network architectures to achieve higher accuracy, which will suffer from overfitting, insufficient adaptability, and low recognition accuracy. To improve the existing phenomenon, a novel lightweight model named dual stream LSTM feature fusion classifier is proposed based on the concatenation of five time-domain features of EMG signals and raw data, which are both processed with one-dimensional convolutional neural networks and LSTM layers to carry out the classification. The proposed method can effectively capture global features of EMG signals using a simple architecture, which means less computational cost. An experiment is conducted on a public DB1 dataset with 52 gestures, and each of the 27 subjects repeats every gesture 10 times. The accuracy rate achieved by the model is 89.66%, which is comparable to that achieved by more complex deep learning neural networks, and the inference time for each gesture is 87.6 ms, which can also be implied in a real-time control system. The proposed model is validated using a subject-wise experiment on 10 out of the 40 subjects in the DB2 dataset, achieving a mean accuracy of 91.74%. This is illustrated by its ability to fuse time-domain features and raw data to extract more effective information from the sEMG signal and select an appropriate, efficient, lightweight network to enhance the recognition results.

show abstract

Normal Cumulative Distribution Function and Dispersion Entropy Based EMG Classification

Cited by 4 publications

References 22 publications

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

CoviDetNet: A new COVID‐19 diagnostic system based on deep features of chest x‐ray

EMG Sinyalleri Kullanılarak GoogLeNet ve Çok Seviyeli DPD ile El Tutma Hareketlerinin Sınıflandırılması

Dual Stream Long Short-Term Memory Feature Fusion Classifier for Surface Electromyography Gesture Recognition

Contact Info

Product

Resources

About