Nasal thermal activity during voluntary facial expression in a patient with chronic pain and alexithymia

Medina, David Alberto Rodríguez; Trejo, Benjamín Domínguez; Albarrán, Irving Armando Cruz; Hernández, Luis Morales; Alva, Gerardo Leija; Silva, Patricia Zamudio

doi:10.18073/pajmt.2017.4.25-31

Cited by 2 publications

(1 citation statement)

References 10 publications

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“…Patients with alexithymia were shown a video with emotional undertones. Their research demonstrates that the nasal temperature drops when a patient expresses anger or discomfort [ 50 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease

et al. 2023

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Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer’s disease. Alzheimer’s disease research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person’s mental health. Support vector machine is the first technique. Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several support vector machines kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network architecture to identify Alzheimer’s-related mental disorders. According to the findings, the support vector machines approach accurately recognized over 93% of the photos tested. The deep convolutional neural network approach was one hundred percent accurate during model training, whereas the support vector machines approach achieved just 93 percent accuracy. In contrast to support vector machines accuracy of 89.3%, the deep convolutional neural network model test findings were accurate 98.8% of the time. Based on the findings reported here, the proposed deep convolutional neural network architecture may be used for diagnostic purposes involving the patient’s mental state.

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“…Patients with alexithymia were shown a video with emotional undertones. Their research demonstrates that the nasal temperature drops when a patient expresses anger or discomfort [ 50 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease

et al. 2023

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show abstract

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

Davodabadi

Daneshian

Saati

et al. 2023

Preprint

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Degeneration of the neurological system linked to cognitive deficits, daily living exercise clutters, and behavioral disturbing impacts may define Alzheimer's disease. Ad research conducted later in life focuses on describing ways for early detection of dementia, a kind of mental disorder. To tailor our care to each patient, we utilized visual cues to determine how they were feeling. We did this by outlining two approaches to diagnosing a person's mental health. Support vector machine is the first technique (SVM). Image characteristics are extracted using a fractal model for classification in this method. With this technique, the histogram of a picture is modeled after a Gaussian distribution. Classification was performed with several SVM kernels, and the outcomes were compared. Step two proposes using a deep convolutional neural network (DCNN) architecture to identify Alzheimer's-related mental disorders. According to the findings, the SVM approach accurately recognized over 93% of the photos tested. The DCNN approach was one hundred percent accurate during model training, whereas the SVM approach achieved just 93 percent accuracy. In contrast to SVM's accuracy of 89.3%, the DCNN model test's findings were accurate 98.8% of the time. Based on the findings reported here, the proposed DCNN architecture may be used for diagnostic purposes involving the patient's mental state.

show abstract

Nasal thermal activity during voluntary facial expression in a patient with chronic pain and alexithymia

Cited by 2 publications

References 10 publications

Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease

Mathematical model and artificial intelligence for diagnosis of Alzheimer’s disease

Mathematical Model and Artificial Intelligence for Diagnosis of Alzheimer's Disease

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