Improving Monitoring and Controlling Parameters for Alzheimer’s Patients Based on IoMT

Karimi, Mohsen; Harouni, Majid; Jazi, Elaheh Izadi; Nasr, Afrooz; Azizi, Nastaran

doi:10.1007/978-981-19-2057-8_8

Cited by 4 publications

(2 citation statements)

References 31 publications

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“…In the 21st century, empirical studies on cancer have been changed into a common research effort. Based on the statistics, the international agency for research on cancer (2022) has estimated that there are 25 million people *Corresponding Author Email: emadi.mehran49@gmail.com (M. Emadi) suffering from cancer [1][2][3]. Also, the American Cancer Society (2022) has reported that heart diseases have been officially replaced by cancer, such has been the main death factor.…”

Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Emadi

Dehkordi

Mobarakeh

2023

IJE

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Brain tumors are one of the most common causes of death that have been widely investigated by scholars in research areas, including care and prevention. Despite various empirical studies on the brain tumor segmentatin, there is still a need for further investigation. This fact is more needed in the automatic methods of brain tumors detection. In the present study, a new method for improving brain tumor segmentation accuracy based on super-pixel and fast primal dual (PD) algorithms has been proposed. The proposed method detects brain tumor tissue in Flair-MRI imaging in BRATS2012 dataset. This method detects the primary borders of tumors using a super-pixel algorithm, and improves brain tumor borders using fast PD in Markov random field optimization. Then, post-processing processes are used to delete white brain areas. Finally, an active contour algorithm was employed to display tumor area. Different experiments were carried on the proposed method and qualitative and quantitative criteria such as dice similarity measure, accuracy and F-measure were used for evaluation. The obtained results showed the efficiency of the proposed method, such that in the accuracy and sensitivity of 86.59 and 88.57% and F1-Measure 86.37 were obtained, respectively.

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Section: Introductionmentioning

confidence: 99%

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Emadi

Dehkordi

Mobarakeh

2023

IJE

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“…EEG (electroencephalography) is a technique employed to monitor brain activity through the measurement of voltage changes generated by the collective neural activity within the brain ( San-Segundo et al, 2019 ; Dehghani et al, 2020 , 2022 , 2023 ; Sadjadi et al, 2021 ; Mosayebi et al, 2022 ). EEG serves as a reflection of the brain’s activity and functioning, and it finds diverse applications, including but not limited to emotion recognition ( Dehghani et al, 2011a , b , 2013 ; Ebrahimzadeh and Alavi, 2013 ; Nikravan et al, 2016 ; Soroush et al, 2017 , 2018a , b , 2019a , b , 2020 ; Bagherzadeh et al, 2018 ; Alom et al, 2019 ; Ebrahimzadeh et al, 2019a , b , c , 2021 , 2022 , 2023 ; Bagheri and Power, 2020 ; Karimi et al, 2022 ; Rehman et al, 2022 ; Yousefi et al, 2022 , 2023 ).…”

Section: Introductionmentioning

confidence: 99%

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Yousefi,

Dehghani,

Taghaavifar

2023

Front. Hum. Neurosci.

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IntroductionEmotions play a critical role in human communication, exerting a significant influence on brain function and behavior. One effective method of observing and analyzing these emotions is through electroencephalography (EEG) signals. Although numerous studies have been dedicated to emotion recognition (ER) using EEG signals, achieving improved accuracy in recognition remains a challenging task. To address this challenge, this paper presents a deep-learning approach for ER using EEG signals.BackgroundER is a dynamic field of research with diverse practical applications in healthcare, human-computer interaction, and affective computing. In ER studies, EEG signals are frequently employed as they offer a non-invasive and cost-effective means of measuring brain activity. Nevertheless, accurately identifying emotions from EEG signals poses a significant challenge due to the intricate and non-linear nature of these signals.MethodsThe present study proposes a novel approach for ER that encompasses multiple stages, including feature extraction, feature selection (FS) employing clustering, and classification using Dual-LSTM. To conduct the experiments, the DEAP dataset was employed, wherein a clustering technique was applied to Hurst’s view and statistical features during the FS phase. Ultimately, Dual-LSTM was employed for accurate ER.ResultsThe proposed method achieved a remarkable accuracy of 97.5% in accurately classifying emotions across four categories: arousal, valence, liking/disliking, dominance, and familiarity. This high level of accuracy serves as strong evidence for the effectiveness of the deep-learning approach to emotion recognition (ER) utilizing EEG signals.ConclusionThe deep-learning approach proposed in this paper has shown promising results in emotion recognition using EEG signals. This method can be useful in various applications, such as developing more effective therapies for individuals with mood disorders or improving human-computer interaction by allowing machines to respond more intelligently to users’ emotional states. However, further research is needed to validate the proposed method on larger datasets and to investigate its applicability to real-world scenarios.

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Lessons from optimal localization of telecommunications drones in fifth generation networks based on unsupervised machine learning methods

Hasan,

Hamidkhani

2023

4th International Scientific Conference of Alkafeel University (Iscku 2022)

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Improving Monitoring and Controlling Parameters for Alzheimer’s Patients Based on IoMT

Cited by 4 publications

References 31 publications

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Enhancing the accuracy of electroencephalogram-based emotion recognition through Long Short-Term Memory recurrent deep neural networks

Lessons from optimal localization of telecommunications drones in fifth generation networks based on unsupervised machine learning methods

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