Hybrid implementation of the fastICA algorithm for high-density EEG using the capabilities of the Intel architecture and CUDA programming

Gajos-Balińska, Anna; Wójcik, Grzegorz M.; Stpiczyński, Przemysław

doi:10.7494/csci.2023.24.4.5539

Cited by 1 publication

(1 citation statement)

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“…By reducing the time it takes for preventive medicine tools to process biomedical data (including as part of the initial feature extraction by edge computing sensors), it is possible not only to improve diagnosis, help more patients, and speed up research, but also to improve solutions hitherto only considered to be promising, such as brain-computer interfaces (BCIs) based on EEG and fNIRS [38][39][40][41][42][43].…”

Section: Ethical and Legal Considerationsmentioning

confidence: 99%

Development of AI-Based Prediction of Heart Attack Risk as an Element of Preventive Medicine

Rojek,

Kotlarz,

Kozielski

et al. 2024

Electronics

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The future paradigm of early cardiac diagnostics is shifting the focus towards heart attack preventive medicine based on non-invasive medical imaging with the support of artificial intelligence. It is necessary to preventively detect its increased risk early and respond with preventive drugs before moving on to more effective, but also more invasive, forms of therapy. The main motivation of our study was to improve existing and develop new AI-based solutions for cardiac preventive medicine, with particular emphasis on the prevention of heart attacks. This is due to the fact that the epidemic of lifestyle diseases (including cardiologic ones) has been stopped but not reversed; hence, automatically supervised prevention using AI seems to be a key opportunity to introduce progress in the above-mentioned areas. This can have major effects not only scientific and clinical in nature, but also economic and social. The aim of this article is to develop and test an AI-based tool designed to predict the occurrence of a heart attack for the purposes of preventive medicine. It used the combination and comparison of multiple AI methods and techniques to determine a personalized heart attack probability based on a wide range of patient characteristics and, from a computational point of view, determine the minimum set of characteristics necessary to do so. When applied to a specific patient, this represents progress in this field of research, resulting in improvements in preclinical care and diagnostics, as well as predictive accuracy in preventive medicine. After an initial selection based on the authors’ knowledge and experience, four solutions turned out to be the best: linear support vector machine (Linear SVC), logistic regression, k-nearest neighbors algorithm (KNN, k-NN), and random forest. A comparison of the models developed in the study shows that models based on logistic regression proved to be the most accurate, although their predictive value is moderate, but sufficient for the initial screening diagnosis—selecting patients who require further, more accurate testing. In addition, this can be performed based on a reduced set of parameters, particularly heart rate, age, BMI, and cholesterol. This allows the development of a prevention strategy based on modifiable factors (e.g., in the form of diet, activity modification, or a hybrid combining different factors) combined with the monitoring of heart attack risk by the proposed system. The novelty and contribution of the described system lies in the use of AI for a widely available, cheap, and quick predictive analysis of cardiovascular functions in a group of patients classified as at risk, and over time in all patients as a standard periodic examination qualifying them for further, more advanced diagnosis of heart diseases.

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