A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data

Supakar, Rinku; Satvaya, Parthasarathi; Chakrabarti, Prąsun

doi:10.1016/j.compbiomed.2022.106225

Cited by 35 publications

(21 citation statements)

References 24 publications

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“…For example, recurrent neural networks (RNNs) are known for their excellent performance in forecasting time series. These networks were applied with considerable success to identify Parkinson's disease [28,29] and analyze electrocardiograms [30][31][32] and electroencephalography data [33][34][35]. AI methods were also examined for respiratory condition classifications, like identifying asthma [36], pneumonia [37], tuberculosis [38,39] and lung tumors [40].…”

Section: Artificial Intelligence In Medical Diagnosismentioning

confidence: 99%

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Bacanin,

Jovanovic,

Stoean

et al. 2024

Axioms

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Respiratory conditions have been a focal point in recent medical studies. Early detection and timely treatment are crucial factors in improving patient outcomes for any medical condition. Traditionally, doctors diagnose respiratory conditions through an investigation process that involves listening to the patient’s lungs. This study explores the potential of combining audio analysis with convolutional neural networks to detect respiratory conditions in patients. Given the significant impact of proper hyperparameter selection on network performance, contemporary optimizers are employed to enhance efficiency. Moreover, a modified algorithm is introduced that is tailored to the specific demands of this study. The proposed approach is validated using a real-world medical dataset and has demonstrated promising results. Two experiments are conducted: the first tasked models with respiratory condition detection when observing mel spectrograms of patients’ breathing patterns, while the second experiment considered the same data format for multiclass classification. Contemporary optimizers are employed to optimize the architecture selection and training parameters of models in both cases. Under identical test conditions, the best models are optimized by the introduced modified metaheuristic, with an accuracy of 0.93 demonstrated for condition detection, and a slightly reduced accuracy of 0.75 for specific condition identification.

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Section: Artificial Intelligence In Medical Diagnosismentioning

confidence: 99%

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Bacanin,

Jovanovic,

Stoean

et al. 2024

Axioms

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“…The efficient and exact processing of EEG signals has attracted attention. Typically, the RNN‐based model is used in traditional sequential signal processing [191,192] . However, it lacks long‐term memory and parallel computing capability required to meet the current demand for more complex and explicable feature exploration, prompting researchers to develop Transformers for recognizing time series‐based EEG signals.…”

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

Understanding the brain with attention: A survey of transformers in brain sciences

Chen,

Wang,

Chen

et al. 2023

Brain-X

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Owing to their superior capabilities and advanced achievements, Transformers have gradually attracted attention with regard to understanding complex brain processing mechanisms. This study aims to comprehensively review and discuss the applications of Transformers in brain sciences. First, we present a brief introduction of the critical architecture of Transformers. Then, we overview and analyze their most relevant applications in brain sciences, including brain disease diagnosis, brain age prediction, brain anomaly detection, semantic segmentation, multi‐modal registration, functional Magnetic Resonance Imaging (fMRI) modeling, Electroencephalogram (EEG) processing, and multi‐task collaboration. We organize the model details and open sources for reference and replication. In addition, we discuss the quantitative assessments, model complexity, and optimization of Transformers, which are topics of great concern in the field. Finally, we explore possible future challenges and opportunities, exploiting some concrete and recent cases to provoke discussion and innovation. We hope that this review will stimulate interest in further research on Transformers in the context of brain sciences.

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“…In [4], UNet, and LSTM networks were utilized for two‐stage detection of pulmonary nodules. Reference [5] employed an RNN‐LSTM network for analyzing EEG signals to detect schizophrenia. Moreover, the timeliness of risk early warning is also a key factor, so how to realize real‐time prediction and early warning of industrial risks is a difficult problem.…”

Section: Introductionmentioning

confidence: 99%

Research on industrial risk early warning algorithms based on edge computing and multimodal data

Su,

Wang,

Zhao

et al. 2024

Electronics Letters

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To achieve real‐time and effective prediction of industrial site risks, this paper proposes an industrial risk prediction framework for multimodal data based on edge computing. First, the authors gather and annotate industrial risk multimodal data that consist of text descriptions, images, and videos. Then, the authors transfer the data to the edge server, and apply deep learning models such as Bidirectional Encoder Representations from Transformers (BERT), ResNet etc., to extract features and learn representations for text, image, and video data respectively. The authors input the fused feature data into an enhanced long short term memory (LSTM) model and train it on the dataset. Finally, the authors perform the risk prediction based on the collected multimodal data. The experimental results demonstrate that the method proposed in this paper exhibits superior performance, achieving a 1.4% enhancement in predictive accuracy.

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A deep learning based model using RNN-LSTM for the Detection of Schizophrenia from EEG data

Cited by 35 publications

References 24 publications

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Respiratory Condition Detection Using Audio Analysis and Convolutional Neural Networks Optimized by Modified Metaheuristics

Understanding the brain with attention: A survey of transformers in brain sciences

Research on industrial risk early warning algorithms based on edge computing and multimodal data

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