Excessive daytime sleepiness due to brain tumor

Aoun, Raissa; Kaul, Malvika; Sahni, Ashima

doi:10.5664/jcsm.8788

Cited by 3 publications

(1 citation statement)

References 5 publications

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“…The author of this research describes a model for detecting acute lymphocytic leukemia with separate depthwise convolutions (Clinton et al, 2020 ; Chatila et al, 2021 ). Diagnosing these tumor tissues as soon as possible is necessary to reduce the physical strain on the patient and the treatment difficulties that the disease brings (Sleiman et al, 2019 ; Aoun et al, 2020 ; Ali et al, 2022 ; Rizwan et al, 2022 ). Convolutional neural network (CNN) is tested using the transfer learning method.…”

Section: Literature Reviewmentioning

confidence: 99%

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Almadhor

Sattar

Hejaili

et al. 2022

Front. Comput. Neurosci.

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Leukemia (blood cancer) diseases arise when the number of White blood cells (WBCs) is imbalanced in the human body. When the bone marrow produces many immature WBCs that kill healthy cells, acute lymphocytic leukemia (ALL) impacts people of all ages. Thus, timely predicting this disease can increase the chance of survival, and the patient can get his therapy early. Manual prediction is very expensive and time-consuming. Therefore, automated prediction techniques are essential. In this research, we propose an ensemble automated prediction approach that uses four machine learning algorithms K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), and Naive Bayes (NB). The C-NMC leukemia dataset is used from the Kaggle repository to predict leukemia. Dataset is divided into two classes cancer and healthy cells. We perform data preprocessing steps, such as the first images being cropped using minimum and maximum points. Feature extraction is performed to extract the feature using pre-trained Convolutional Neural Network-based Deep Neural Network (DNN) architectures (VGG19, ResNet50, or ResNet101). Data scaling is performed by using the MinMaxScaler normalization technique. Analysis of Variance (ANOVA), Recursive Feature Elimination (RFE), and Random Forest (RF) as feature Selection techniques. Classification machine learning algorithms and ensemble voting are applied to selected features. Results reveal that SVM with 90.0% accuracy outperforms compared to other algorithms.

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Section: Literature Reviewmentioning

confidence: 99%

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Almadhor

Sattar

Hejaili

et al. 2022

Front. Comput. Neurosci.

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Prevalence of sleep disturbances and their effects on quality of life in adults with untreated pituitary tumor and meningioma

et al. 2021

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Comparison of subjectively and objectively measured sleep–wake patterns among patients with primary brain tumors

Kim,

Kenyon,

Kim

et al. 2024

Neuro-Oncology Practice

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BACKGROUND The sleep diary and wrist-worn actigraphy are widely used to assess sleep disturbances in patients with primary brain tumors (PwPBT) in both clinical and research settings. However, their comparability has not been systematically examined. This study aimed to compare the sleep-wake patterns measured using the subjectively measured Consensus Sleep Diary (CSD) and the objectively measured ActiGraph (AG) actigraphy among PwPBT. METHODS Sleep-wake patterns were assessed through CSD and AG over 14 consecutive nights across two occasions among 30 PwPBT. AG data were processed with AG proprietary and open-source GGIR algorithms, both with and without the assistance of CSD. Thirteen sleep parameters covering sleep-wake times, sleep disruptions, sleep durations, and sleep efficiency were compared using equivalency testing, mean absolute percent error (MAPE), and intra-class correlation (ICC). The estimated sleep parameters were correlated with perceived sleep quality and compared across the different sleep measures. RESULTS Significant between-measure equivalency was claimed for sleep-wake time parameters (Ps≤.05), with acceptable MAPEs (<10%). Sleep disruption parameters such as wake-after-sleep-onset were not statistically equivalent, with a large MAPE (≥10%) between the measures. Sleep efficiency was equivalent, though varied depending on how sleep efficiency was calculated. For most sleep parameters, ICCs were low and unacceptable (<.50) suggesting incomparability between the measures. Lastly, CSD-derived sleep parameters exhibited a stronger correlation with perceived sleep quality compared to actigraphy measures. CONCLUSIONS The findings suggest the incomparability of sleep parameters estimated from different measures. Both subjective and objective measures are recommended to better describe sleep health among PwPBT.

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Excessive daytime sleepiness due to brain tumor

Cited by 3 publications

References 5 publications

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

An efficient computer vision-based approach for acute lymphoblastic leukemia prediction

Prevalence of sleep disturbances and their effects on quality of life in adults with untreated pituitary tumor and meningioma

Comparison of subjectively and objectively measured sleep–wake patterns among patients with primary brain tumors

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