Voiceless Bangla vowel recognition using sEMG signal

Mostafa, Sheikh Shanawaz; Awal, Md. Abdul; Ahmad, Mohiuddin; Rashid, M. A.

doi:10.1186/s40064-016-3170-9

Cited by 6 publications

(2 citation statements)

References 34 publications

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“…These features can be trained by fuzzy-hybrid neural networks [ 56 ], support vector machine (SVM) [ 57 , 58 ], light gradient boosting machine [ 59 ], and Bayes maximum-likelihood (ML) classifier [ 32 ]. In addition to that, prominent features can also be selected by some feature selection algorithms such as the mRMR method and the Jaya algorithm [ 60 , 61 , 62 ] to increase the classification accuracy.…”

Section: Discussionmentioning

confidence: 99%

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

Awal

Mostafa²,

Ahmad

et al. 2021

Sensors

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The electrocardiogram (ECG) has significant clinical importance for analyzing most cardiovascular diseases. ECGs beat morphologies, beat durations, and amplitudes vary from subject to subject and diseases to diseases. Therefore, ECG morphology-based modeling has long-standing research interests. This work aims to develop a simplified ECG model based on a minimum number of parameters that could correctly represent ECG morphology in different cardiac dysrhythmias. A simple mathematical model based on the sum of two Gaussian functions is proposed. However, fitting more than one Gaussian function in a deterministic way has accuracy and localization problems. To solve these fitting problems, two hybrid optimization methods have been developed to select the optimal ECG model parameters. The first method is the combination of an approximation and global search technique (ApproxiGlo), and the second method is the combination of an approximation and multi-start search technique (ApproxiMul). The proposed model and optimization methods have been applied to real ECGs in different cardiac dysrhythmias, and the effectiveness of the model performance was measured in time, frequency, and the time-frequency domain. The model fit different types of ECG beats representing different cardiac dysrhythmias with high correlation coefficients (>0.98). Compared to the nonlinear fitting method, ApproxiGlo and ApproxiMul are 3.32 and 7.88 times better in terms of root mean square error (RMSE), respectively. Regarding optimization, the ApproxiMul performs better than the ApproxiGlo method in many metrics. Different uses of this model are possible, such as a syntactic ECG generator using a graphical user interface has been developed and tested. In addition, the model can be used as a lossy compression with a variable compression rate. A compression ratio of 20:1 can be achieved with 1 kHz sampling frequency and 75 beats per minute. These optimization methods can be used in different engineering fields where the sum of Gaussians is used.

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Section: Discussionmentioning

confidence: 99%

Design and Optimization of ECG Modeling for Generating Different Cardiac Dysrhythmias

Awal

Mostafa²,

Ahmad

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…In another study, a three-channel EMG system was developed for patients with speech impairment, and three Arabic vowels were recognized by using the sEMG signals recorded from facial muscles [8]. Three channels of sEMG sensors were placed on the facial muscles, and eleven voiceless Bangla vowels were classified by using the artificial neural network [9]. A total of eight sEMG sensors (4 on the face and 4 on the neck) were used to record the sEMG signals when reading phrases constructed from a 2500-word vocabulary for silent speech recognition of patients at least 6 months after total laryngectomy [3].…”

mentioning

confidence: 99%