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  • 1. Abdukalikova, Anara
    et al.
    Kleyko, Denis
    Osipov, Evgeny
    Wiklund, Urban
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Detection of Atrial Fibrillation From Short ECGs: Minimalistic Complexity Analysis for Feature-Based Classifiers2018In: 2018 Computing in Cardiology Conference (CinC), IEEE, 2018Conference paper (Refereed)
    Abstract [en]

    In order to facilitate data-driven solutions for early detection of atrial fibrillation (AF), the 2017 CinC conference challenge was devoted to automatic AF classification based on short ECG recordings. The proposed solutions concentrated on maximizing the classifiers F-1 score, whereas the complexity of the classifiers was not considered. However, we argue that this must be addressed as complexity places restrictions on the applicability of inexpensive devices for AF monitoring outside hospitals. Therefore, this study investigates the feasibility of complexity reduction by analyzing one of the solutions presented for the challenge.

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  • 2. Kleyko, Denis
    et al.
    Osipov, Evgeny
    Wiklund, Urban
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    A Comprehensive Study of Complexity and Performance of Automatic Detection of Atrial Fibrillation: Classification of Long ECG Recordings Based on the PhysioNet Computing in Cardiology Challenge 20172020In: Biomedical Engineering & Physics Express, E-ISSN 2057-1976, Vol. 6, no 2, article id 025010Article in journal (Refereed)
    Abstract [en]

    Objective: The 2017 PhysioNet/CinC Challenge focused on automatic classification of atrial fibrillation (AF) in short ECGs. This study aimed to evaluate the use of the data and results from the challenge for detection of AF in longer ECGs, taken from three other PhysioNet datasets.

    Approach: The used data-driven models were based on features extracted from ECG recordings, calculated according to three solutions from the challenge. A Random Forest classifier was trained with the data from the challenge. The performance was evaluated on all non-overlapping 30 s segments in all recordings from three MIT-BIH datasets. Fifty-six models were trained using different feature sets, both before and after applying three feature reduction techniques.

    Main Results: Based on rhythm annotations, the AF proportion was 0.00 in the MIT-BIH Normal Sinus Rhythm (N = 46083 segments), 0.10 in the MIT-BIH Arrhythmia (N = 2880), and 0.41 in the MIT-BIH Atrial Fibrillation (N = 28104) dataset. For the best performing model, the corresponding detected proportions of AF were 0.00, 0.11 and 0.36 using all features, and 0.01, 0.10 and 0.38 when using the 15 best performing features.

    Significance: The results obtained on the MIT-BIH datasets indicate that the training data and solutions from the 2017 Physionet/Cinc Challenge can be useful tools for developing robust AF detectors also in longer ECG recordings, even when using a low number of carefully selected features. The use of feature selection allows significantly reducing the number of features while preserving the classification performance, which can be important when building low-complexity AF classifiers on ECG devices with constrained computational and energy resources.

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  • 3. Kleyko, Denis
    et al.
    Osipov, Evgeny
    Wiklund, Urban
    Umeå University, Faculty of Medicine, Department of Radiation Sciences, Radiation Physics.
    Vector-Based Analysis of the Similarity Between Breathing and Heart Rate During Paced Deep Breathing2018In: 2018 Computing in Cardiology Conference (CinC), IEEE, 2018, article id 8743692Conference paper (Refereed)
    Abstract [en]

    The heart rate (HR) response to paced deep breathing (DB) is a common test of autonomic function, where the scoring is based on indices reflecting the overall heart rate variability (HRV), where high scores are considered as normal findings but can also reflect arrhythmias. This study presents a method based on hyperdimensional computing for assessment of the similarity between feature vectors derived from the HR and breathing signals. The proposed method was used to identify subjects where HR did not follow the paced breathing pattern in recordings from DB tests in 174 healthy subjects and 135 patients with cardiac autonomic neuropathy. Subjects were classified in 4 similarity classes, where the lowest similiarity class included 35 patients and 3 controls. In general, the autonomic function cannot be evaluated in subjects in the lowest similarity class if they also present with high HRV scores, since this combination is a strong indicator of the presence of arrhythmias. Thus, the proposed vector-based similarity analysis is one tool to identify subjects with high HRV but low cardiorespiratory synchronization during the DB test, which falsely can be interpreted as normal autonomic function.

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