Machine learning based biosignals mental stress detection
- Authors: Al-Jumaily, Adel , Matin, Nafisa , Hoshyar, Azadeh
- Date: 2021
- Type: Text , Conference paper
- Relation: 6th International Conference on Soft Computing in Data Science, SCDS 2021 Vol. 1489 CCIS, p. 28-41
- Full Text: false
- Reviewed:
- Description: Mental Stress can be defined as a normal physiological and biological reaction to an incident or a situation that makes a person feel challenged, troubled, or helpless. While dealing with stress, some changes occur in the biological function of a person, which results in a considerable change in some bio-signals such as, Electrocardiogram (ECG), Electromyography (EMG), Electrodermal Activity (EDA), respiratory rate. This paper aims to review the effect of mental stress on mental condition and health, the changes in biosignals as an indicator of the stress response and train a model to detect stressed states using the biosignals. This paper delivers a brief review of mental stress and biosignals correlation. It represents four Support Vector Machine (SVM) models trained with ECG and EMG features from an open access database based on task related stress. After performing comparative analysis on the four types of trained SVM models with chosen features, Gaussian Kernel SVM is selected as the best SVM model to detect mental stress which can predict the mental condition of a subject for a stressed and relaxed condition having an accuracy of 93.7%. These models can be investigated further with more biosignals and applied in practice, which will be beneficial for the physician. © 2021, Springer Nature Singapore Pte Ltd.
Intelligent feature selection algorithm using SA-SVM classification for skin cancer diagnosis
- Authors: Hoshyar, Azadeh , Al-Jumaily, Adel
- Date: 2024
- Type: Text , Book chapter
- Relation: Non-Invasive Health Systems based on Advanced Biomedical Signal and Image Processing Chapter 15 p. 372-395
- Full Text: false
- Reviewed:
- Description: In recent decades, the incidence of malignant melanoma as a deadly skin cancer has increased worldwide. With its high medical costs and death rates, this cancer has prioritized the need for early diagnosis. Computer-based detection systems can improve the diagnosis rate of melanoma by 5%-30% compared to the naked eye and reduce human error. Although much effort has been made to advance the detection of skin cancers, there are still serious concerns about it. This chapter introduces automatic skin cancer diagnosis and an overview of methods in each step toward detection. A novel algorithm in feature selection and classification stages of automatic skin cancer diagnosis is designed and implemented to identify malignant and benign lesions. A smart algorithm is proposed based on inertia-based particle swarm optimization (IPSO) and the self-advising SVM (SA-SVM). This algorithm optimizes the feature selection stage. Additionally, SA-SVM, known as a new classifier in skin cancer detection systems, is employed along with the proposed algorithm. The statistical and performance measurement analyses of algorithms are presented to prove the superiority of the proposed algorithms. © 2024 selection and editorial matter, Adel Al-Jumaily, Paolo Crippa, Ali Mansour, and Claudio Turchetti; individual chapters, the contributors.