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Browsing by Author "Pires, Ivan Miguel"

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  • Algorithms for automated diagnosis of cardiovascular diseases based on ECG data: A comprehensive systematic review
    Publication . Pinto, Rui João; Silva, Pedro Miguel; Duarte, Rui P.; Marinho, Francisco Alexandre; Pimenta, Luís; Gouveia, António Jorge; Gonçalves, N.J.A.P.; Coelho, Paulo; Zdravevski, Eftim; Lameski, Petre; LEITHARDT, VALDERI; Garcia, Nuno M.; Pires, Ivan Miguel
    The prevalence of cardiovascular diseases is increasing around the world. However, the technology is evolving and can be monitored with low-cost sensors anywhere at any time. This subject is being researched, and different methods can automatically identify these diseases, helping patients and healthcare professionals with the treatments. This paper presents a systematic review of disease identification, classification, and recognition with ECG sensors. The review was focused on studies published between 2017 and 2022 in different scientific databases, including PubMed Central, Springer, Elsevier, Multidisciplinary Digital Publishing Institute (MDPI), IEEE Xplore, and Frontiers. It results in the quantitative and qualitative analysis of 103 scientific papers. The study demonstrated that different datasets are available online with data related to various diseases. Several ML/DP-based models were identified in the research, where Convolutional Neural Network and Support Vector Machine were the most applied algorithms. This review can allow us to identify the techniques that can be used in a system that promotes the patient’s autonomy.
    2023-02Journal article Open access Show more
  • Identification of Abnormal Behavior in Activities of Daily Life Using Novelty Detection
    Publication . Freitas, Mauricio; de Aquino Piai, Vinicius; Dazzi, Rudimar; Teive, Raimundo; Parreira, Wemerson; Fernandes, Anita; Pires, Ivan Miguel; Leithardt, Valderi Reis Quietinho
    The world population is aging at a rapid pace. According to the WHO (World Health Organization), from 2015 to 2050, the proportion of elderly people AQ1 will practically double, from 12 to 22%, representing 2.1 billion people. From the individual’s point of view, aging brings a series of challenges, mainly related to AQ2 health conditions. Although, seniors can experience opposing health profiles. With advancing age, cognitive functions tend to degrade, and conditions that affect the physical and mental health of the elderly are disabilities or deficiencies that affect Activities of Daily Living (ADL). The difficulty of carrying out these activities within the domestic context prevents the individual from living independently in their home. Abnormal behaviors in these activities may represent a decline in health status and the need for intervention by family members or caregivers. This work proposes the identification of anomalies in the ADL of the elderly in the domestic context through Machine Learning algorithms using the Novelty Detection method. The focus is on using available ADL data to create a baseline of behavior and using new data to classify them as normal or abnormal daily. The results obtained using the E-Health Monitoring database, using different Novelty Detection algorithms, have an accuracy of 91% and an F1-Score of 90%.
    2023Book part Restricted access Show more