Heart sound signal recovery based on time series signal prediction using a recurrent neural network in the long short-term memory model

Wang, Zhi-Hao; Horng, Gwo-Jiun; Hsu, Tz-Heng; Aripriharta, A.; Jong, Gwo-Jia

doi:10.1007/s11227-019-03096-x

Cited by 17 publications

(12 citation statements)

References 26 publications

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“…The transfer learning models were tested and trained using these spectrograms. We employed the CWTS process, compared to the technique in [ 17 ], for recovering heart sound signals, based on LSTM architecture.…”

Section: Methodsmentioning

confidence: 99%

“…Regarding the second stage, numerous feature extraction algorithms have been proposed in the literature, falling into the following three primary categories: time domain [ 17 ], frequency domain [ 18 ], and time-frequency complexity domain [ 19 ]. Due to the physiological properties of the PCG signals, the time or frequency domain features are straightforward, simple to grasp, and easy to calculate.…”

Section: Literature Reviewmentioning

confidence: 99%

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Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

2022

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The major cause of death worldwide is due to cardiovascular disorders (CVDs). For a proper diagnosis of CVD disease, an inexpensive solution based on phonocardiogram (PCG) signals is proposed. (1) Background: Currently, a few deep learning (DL)-based CVD systems have been developed to recognize different stages of CVD. However, the accuracy of these systems is not up-to-the-mark, and the methods require high computational power and huge training datasets. (2) Methods: To address these issues, we developed a novel attention-based technique (CVT-Trans) on a convolutional vision transformer to recognize and categorize PCG signals into five classes. The continuous wavelet transform-based spectrogram (CWTS) strategy was used to extract representative features from PCG data. Following that, a new CVT-Trans architecture was created to categorize the CWTS signals into five groups. (3) Results: The dataset derived from our investigation indicated that the CVT-Trans system had an overall average accuracy ACC of 100%, SE of 99.00%, SP of 99.5%, and F1-score of 98%, based on 10-fold cross validation. (4) Conclusions: The CVD-Trans technique outperformed many state-of-the-art methods. The robustness of the constructed model was confirmed by 10-fold cross-validation. Cardiologists can use this CVT-Trans system to help patients with the diagnosis of heart valve problems.

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Section: Methodsmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

2022

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“…RNNs have been successfully applied to numerous applications, including time-series prediction [ 34 , 37 ], speech recognition [ 9 , 16 ], image classification [ 26 ], and video analysis [ 40 ], where a model effectively captures the dynamics of sequences via cycles in the network nodes.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of e-learners’ concentration using recurrent neural networks

Jeong

Cho

2022

J Supercomput

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Recently, interest in e-learning has increased rapidly owing to the lockdowns imposed by COVID-19. A major disadvantage of e-learning is the difficulty in maintaining concentration because of the limited interaction between teachers and students. The objective of this paper is to develop a methodology to predict e-learners’ concentration by applying recurrent neural network models to eye gaze and facial landmark data extracted from e-learners’ video data. One hundred eighty-four video data of ninety-two e-learners were obtained, and their frame data were extracted using the OpenFace 2.0 toolkit. Recurrent neural networks, long short-term memory, and gated recurrent units were utilized to predict the concentration of e-learners. A set of comparative experiments was conducted. As a result, gated recurrent units exhibited the best performance. The main contribution of this paper is to present a methodology to predict e-learners’ concentration in a natural e-learning environment.

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“…(2) Range M n : Amplitude refers to the width expanded by the waveform vibration of audio signal [32][33][34]. e more passionate the music, the greater the audio amplitude.…”

Section: Audio Featuresmentioning

confidence: 99%

Research on Multimodal Music Emotion Recognition Method Based on Image Sequence

Zhao¹

2021

Scientific Programming

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The work of music performance system is to control the light change by identifying the emotional elements of music. Therefore, once the identification error occurs, it will not be able to create a good stage effect. Therefore, a multimodal music emotion recognition method based on image sequence is studied. The emotional characteristics of music are analyzed, including acoustic characteristics, melody characteristics, and audio characteristics, and the feature vector is constructed. The recognition and classification model based on neural network is trained, the weight and threshold of each layer are adjusted, and then the feature vector is input into the trained model to realize the intelligent recognition and classification of multimodal music emotion. The threshold of the starting point range of a specific humming note is given by the center clipping method, which is used to eliminate the low amplitude part of the humming note signal, extract the short-time spectral structure features and envelope features of the pitch, and complete the multimodal music emotion recognition. The results show that the calculated kappa coefficient k is greater than 0.75, which shows that the recognition and classification results are in good agreement with the actual results, and the classification and recognition accuracy is high.

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Heart sound signal recovery based on time series signal prediction using a recurrent neural network in the long short-term memory model

Cited by 17 publications

References 26 publications

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

Automatic Detection and Classification of Cardiovascular Disorders Using Phonocardiogram and Convolutional Vision Transformers

Evaluation of e-learners’ concentration using recurrent neural networks

Research on Multimodal Music Emotion Recognition Method Based on Image Sequence

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