EEG-based deep learning neural net for apnea detection

Locharla, Govinda Rao; Pogiri, Revathi; Prakash, Allam Jaya

doi:10.1016/b978-0-323-91197-9.00010-2

Cited by 4 publications

(3 citation statements)

References 23 publications

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“…As for disorders of consciousness, Spataro et (Coma Recovery Scale-Revised) and reported its reliability and efficacy in improving diagnostic precision for the evaluation of consciousness level [58]. Other conditions that can apply BCI for diagnosis and classification are stroke diseases [75], dementia [59], and sleep apnea [60].…”

Section: Diagnosis and Detectionmentioning

confidence: 99%

Applications of Brain Computer Interface in Present Healthcare Setting

Alwi Alkaff,

Hashimah Ahamed Hassain Malim,

Sumari

et al. 2024

Artificial Intelligence

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Brain-computer interface (BCI) is an innovative method of integrating technology for healthcare. Utilizing BCI technology allows for direct communication and/or control between the brain and an external device, thereby displacing conventional neuromuscular pathways. The primary goal of BCI in healthcare is to repair or reinstate useful function to people who have impairments caused by neuromuscular disorders (e.g., stroke, amyotrophic lateral sclerosis, spinal cord injury, or cerebral palsy). BCI brings with it technical and usability flaws in addition to its benefits. We present an overview of BCI in this chapter, followed by its applications in the medical sector in diagnosis, rehabilitation, and assistive technology. We also discuss BCI’s strengths and limitations, as well as its future direction.

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Section: Diagnosis and Detectionmentioning

confidence: 99%

Applications of Brain Computer Interface in Present Healthcare Setting

Alwi Alkaff,

Hashimah Ahamed Hassain Malim,

Sumari

et al. 2024

Artificial Intelligence

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“…In recent years, the success of deep learning in natural language processing, computer vision and other fields has made the recommendation field begin to pay attention to this powerful tool, and scholars have begun to explore the use of deep learning methods to improve some insurmountable weaknesses of current recommendation systems, such as data sparseness, cold start, poor interpretability and other problems [19,20]. In particular, the emergence of CNN and RNN [21][22][23][24][25][26] has achieved great success in many natural language processing (NLP) tasks. So everyone began to try to use deep learning methods, such as DeepCoNN, D-Attn [12], etc., to mine user preferences and product characteristics in review texts, and then directly apply them to predictive scoring.…”

Section: Relate Workmentioning

confidence: 99%

Deep learning recommendation algorithm based on semantic mining

Huang

Wang²,

Wang

2022

PLoS ONE

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This paper proposes Deep Semantic Mining based Recommendation (DSMR), which can extract user features and item attribute features more accurately by deeply mining the semantic information of review text and item description documents recommend. First, the proposed model uses the BERT pre-training model to process review texts and item description documents, and deeply mine user characteristics and item attributes, which effectively alleviates the problems of data sparseness and item cold start; Then, the forward LSTM is used to pay attention to the changes of user preferences over time, and a more accurate recommendation is obtained; finally, in the model training stage, the experimental data are randomly divided into 1 to 5 points, 1:1:1:1:1. Extraction ensures that the amount of data for each score is equal, so that the results are more accurate and the model is more robust. Experiments are carried out on four commonly used Amazon public data sets, and the results show that with the root mean square error as the evaluation index, the error of DSMR recommendation results is at least 11.95% lower on average than the two classic recommendation models based only on rating data. At the same time, it is better than the three latest recommendation models based on review text, and it is 5.1% lower than the best model on average.

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“…For example, machine-learning methods such as deep learning have been applied to detect arrythmias in electrocardiogram signals [ 7 , 8 ]. Likewise, studies report that deep learning algorithms achieve high accuracy on detecting drowsiness [ 9 ] and apnea [ 10 ] in EEG data.…”

Section: Introductionmentioning

confidence: 99%

MindReader: Unsupervised Classification of Electroencephalographic Data

Rivas-Carrillo

Akkuratov

Valdéz-Ruvalcaba

et al. 2023

Sensors

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Electroencephalogram (EEG) interpretation plays a critical role in the clinical assessment of neurological conditions, most notably epilepsy. However, EEG recordings are typically analyzed manually by highly specialized and heavily trained personnel. Moreover, the low rate of capturing abnormal events during the procedure makes interpretation time-consuming, resource-hungry, and overall an expensive process. Automatic detection offers the potential to improve the quality of patient care by shortening the time to diagnosis, managing big data and optimizing the allocation of human resources towards precision medicine. Here, we present MindReader, a novel unsupervised machine-learning method comprised of the interplay between an autoencoder network, a hidden Markov model (HMM), and a generative component: after dividing the signal into overlapping frames and performing a fast Fourier transform, MindReader trains an autoencoder neural network for dimensionality reduction and compact representation of different frequency patterns for each frame. Next, we processed the temporal patterns using a HMM, while a third and generative component hypothesized and characterized the different phases that were then fed back to the HMM. MindReader then automatically generates labels that the physician can interpret as pathological and non-pathological phases, thus effectively reducing the search space for trained personnel. We evaluated MindReader’s predictive performance on 686 recordings, encompassing more than 980 h from the publicly available Physionet database. Compared to manual annotations, MindReader identified 197 of 198 epileptic events (99.45%), and is, as such, a highly sensitive method, which is a prerequisite for clinical use.

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EEG-based deep learning neural net for apnea detection

Cited by 4 publications

References 23 publications

Applications of Brain Computer Interface in Present Healthcare Setting

Applications of Brain Computer Interface in Present Healthcare Setting

Deep learning recommendation algorithm based on semantic mining

MindReader: Unsupervised Classification of Electroencephalographic Data

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