A mild cognitive impairment diagnostic model based on IAAFT and BiLSTM

Li, Xin; Zhou, Hao; Su, Rui; Kang, Jiannan; Sun, Yu; Yuan, Yi; Han, Ying; Chen, Xiaoling; Xie, Ping; Wang, Yulin; Liu, Qinshuang

doi:10.1016/j.bspc.2022.104349

Cited by 8 publications

(4 citation statements)

References 31 publications

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“…It has not been taken seriously in previous research, which makes it difficult to obtain useful information [8]. The existence of noise, which affected the learning efficiency of classification, was the primary drawback of the earlier study [35]. The prior study did not adequately preprocess the EEG data acquired from MCI and AD patients, which makes them visually indistinguishable [35].…”

Section: Previous Work Limitationsmentioning

confidence: 99%

“…The existence of noise, which affected the learning efficiency of classification, was the primary drawback of the earlier study [35]. The prior study did not adequately preprocess the EEG data acquired from MCI and AD patients, which makes them visually indistinguishable [35]. Most conventional ML techniques use characteristics that are manually generated and derived from unprocessed EEG data.…”

Section: Previous Work Limitationsmentioning

confidence: 99%

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Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Alahmadi,

Rahman,

Alhababi

et al. 2024

Mach. Learn.: Sci. Technol.

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Mild Cognitive Impairment (MCI) is a cognitive disease that primarily affects elderly persons. Patients with MCI have impairments in one or more cognitive areas, such as memory, attention, language, and problem-solving. The risk of Alzheimer's disease (AD) development is 10 times higher among individuals who meet the MCI diagnosis than in those who do not have such a diagnosis. Identifying the primary neurophysiological variations between those who are suffering from cognitive impairment and those who are ageing normally may provide helpful techniques to assess the effectiveness of therapies. Event-related Potentials (ERPs) are utilized to investigate the processing of sensory, cognitive, and motor information in the brain. ERPs enable excellent temporal resolution of underlying brain activity. ERP data is complex due to the temporal variation occurs in time domain. It is actually a type of electroencephalography (EEG) signal that is time-locked to a specific event or behavior. To remove artifacts from the data, this work utilizes Independent component analysis (ICA), finite impulse response (FIR) filter, and Fast Fourier Transformation (FFT) as preprocessing techniques. The Bidirectional Long Short-Term Memory (BiLSTM) network is utilized to retains the spatial relationships between the ERP data while learning changes in temporal information for a long time. This network performed well both in modeling and information extraction from the signals. To validate the model performance, the proposed framework is tested on two benchmark datasets. The proposed framework achieved state-of-the-art accuracy of 96.03% on SEED dataset and 97.31% on CAUEEG dataset for the classification tasks.

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Section: Previous Work Limitationsmentioning

confidence: 99%

Section: Previous Work Limitationsmentioning

confidence: 99%

Prediction of mild cognitive impairment using EEG signal and BiLSTM network

Alahmadi,

Rahman,

Alhababi

et al. 2024

Mach. Learn.: Sci. Technol.

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“…Depending on the type of dam, the components of Formula ( 13) are slightly different, and this paper focuses on the deformation of any point on the gravity dam. According to the structural characteristics of the gravity dam and the actual condition of the project, under the effect of the upstream reservoir, the horizontal displacement on the gravity dam is linearly related to the water depth H, H 2 , H 3 , which can be expressed by Equation (14). The temperature at any point within the concrete of the dam can be approximated by a periodic function, assuming that the temperature displacement is linearly related to the concrete temperature, and the harmonic sine function can be chosen as a factor, i.e., the displacement caused by the temperature effect for a gravity dam can be expressed by Equation (15).…”

Section: Ssa Principlesmentioning

confidence: 99%

“…Wen et al [13] conducted a comparative study between the LSTM-MA and LSTM-PCA models and concluded that the LSTM-MA model is more convenient and suitable for engineering applications. Tishya Manna et al [14] compared multiple models and found that their proposed RSFAS-bi-directional LSTM model had a shorter execution time, which proved beneficial in determining parameters for different seasonal decision processes. Li Xin et al [15] constructed a new model based on iterative amplitude adjusted Fourier transform (IAAFT) and bi-directional long short-term memory (BiLSTM) for classifying mild cognitive impairment (MCI) and a healthy control (HC) group.…”

Section: Introductionmentioning

confidence: 99%

Concrete Dam Deformation Prediction Model Research Based on SSA–LSTM

Madiniyeti

Chao

et al. 2023

Applied Sciences

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In the context of dam deformation monitoring, the prediction task is essentially a time series prediction problem that involves non-stationarity and complex influencing factors. To enhance the accuracy of predictions and address the challenges posed by high randomness and parameter selection in LSTM models, a novel approach called sparrow search algorithm–long short-term memory (SSA–LSTM) has been proposed for predicting the deformation of concrete dams. SSA–LSTM combines the SSA optimization algorithm with LSTM to automatically optimize the model’s parameters, thereby enhancing the prediction performance. Firstly, a concrete dam was used as an example to preprocess the historical monitoring data by cleaning, normalizing, and denoising, and due to the specificity of the data structure, multi-level denoising of abnormal data was performed. Second, some of the data were used to train the model, and the hyperparameters of the long and short-term memory neural network model (LSTM) were optimized by the SSA algorithm to better match the input data with the network structure. Finally, high-precision prediction of concrete dam deformation was carried out. The proposed model in this study significantly improves the prediction accuracy in dam deformation forecasting and demonstrates effectiveness in long-term time series deformation prediction. The model provides a reliable and efficient approach for evaluating the long-term stability of dam structures, offering valuable insights for engineering practices and decision-making.

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