{"id":17080,"date":"2014-12-10T08:56:23","date_gmt":"2014-12-10T07:56:23","guid":{"rendered":"http:\/\/ibecbarcelona.eu\/?page_id=17080"},"modified":"2026-01-16T13:09:23","modified_gmt":"2026-01-16T12:09:23","slug":"biomedical-signal-processing-and-interpretation","status":"publish","type":"page","link":"https:\/\/ibecbarcelona.eu\/es\/biomedsignal","title":{"rendered":"Biomedical signal processing and interpretation"},"content":{"rendered":"
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About<\/h2>\n
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The group\u2019s research addresses the design and development of advanced signal processing techniques and the interpretation of biomedical signals to improve non-invasive monitoring, diagnosis, disease prevention and pathology treatment.<\/strong><\/h4>\n\n\n\n
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The group focuses its research in a translational way to promote the transfer of our scientific and technological contributions. <\/span><\/p>\n<\/blockquote>\n\n\n\n

Currently, our prototypes are used in hospitals for research purposes and for future industrial developments. <\/span><\/p>\n\n\n\n

Our main objective is to improve diagnosis capability through the characterization of physiological phenomena and to enhance early detection of major cardiac and respiratory diseases and sleep disorders. We propose and design new signal processing algorithms and develop new biosignal databases, with the collaboration of our hospital partners. <\/span><\/p>\n\n\n\n

To validate the clinical information of new surface signals, we have developed specific invasive\/non-invasive protocols and animal models. <\/span><\/p>\n\n\n\n

Obstructive Sleep Apnea and Chronic Obstructive Pulmonary Disease  <\/strong><\/p>\n\n\n\n

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  • Novel non-invasive measurements of neural inspiratory drive and time from invasive and noninvasive recordings of respiratory activity (<\/span>Scientific Reports<\/span><\/b> 2018, 8:16921; <\/span>IEEE Journal Biomed Health Informatics<\/span><\/b> 2018<\/span>;<\/span> <\/span>IEEE-EMBC<\/span><\/b> 2018: <\/span>3342-3345 <\/span>)<\/span> <\/span><\/li>\n\n\n\n
  • Evaluation of a wearable device to determine physiological parameters from surface diaphragm electromyography (IEEE Journal Biomed Health Informatics<\/b> 2018) acquired by concentric ring electrodes (IEEE-EMBC<\/b> 2018:  3350-3353)<\/li>\n\n\n\n
  • Characterization of the microvascular cerebral blood flow response to obstructive apneic events (Neurophotonics<\/b> 2018, 5: 045003) with the ICFO and the  Hospital de Sant Pau. <\/li>\n\n\n\n
  • Improvement of a front-end step for a wearable device for <\/span> <\/span>biosignals recording <\/span>for<\/span> COPD <\/span>and OSA <\/span>patients <\/span>(IEEE Transactions on Biomedical Circuits and Systems <\/span><\/b>2018, 12: 774 \u2013 783) with <\/span>the <\/span>imec, <\/span>Eindhoven (NL).<\/span> <\/span><\/li>\n<\/ul>\n\n\n\n
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    Cardiac and cardiorespiratory diseases  <\/strong><\/p>\n\n\n\n

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    • Novel eingvalue-based method for time delay estimation of respiratory signals <\/span>in patients with chronic heart failure <\/span>(<\/span><\/b>Digital Signal Processing<\/span><\/b> 2018, 75: 107-119<\/span>)<\/span><\/i>, with Lund University,Sweden, and University of Zaragoza.<\/li>\n\n\n\n
    • Cardirespiratory phase synchronization <\/span>during mental stimuli in healthy subjects (<\/span>IEEE-EMBC<\/span><\/b> 2018: 5298-5301)<\/span> <\/span><\/li>\n\n\n\n
    • Estimation of sinus arrhythmia to classify ischemic cardiomyopathy <\/span>(IEEE-EMBC<\/span><\/b> 2018, 4860-4863) and artifact reconstruction of blood pressure signals (<\/span>IEEE-EMBC<\/span><\/i><\/b> 2018, 4864-4867)<\/span> <\/span><\/li>\n<\/ul>\n\n\n\n
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      Neurorehabilitation <\/strong><\/p>\n\n\n\n

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      • Characterization of upper limb muscle\u2019s EMG activity during reaching and grasping (<\/span><\/i>NeuroRehabilitation-ICNR<\/span><\/i><\/b> 2018)<\/span><\/i> <\/span><\/li>\n<\/ul>\n\n\n\n
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        Novel method for differentiating normal from adventitious respiratory sounds (RS) to improve the diagnosis of pulmonary diseases. Particularly, continuous adventitious sounds (CAS) are of clinical interest because they reflect the severity of certain diseases. The new method is based on the multi-scale analysis of instantaneous frequency (IF) and envelope (IE) calculated after ensemble empirical mode decomposition (EEMD) of respiratory sounds. (Lozano et al., 2016, IEEE Journal of Biomedical and Health Informatics).<\/figcaption><\/figure>\n<\/figure>\n\n\n\n
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        Improvement in Neural Respiratory Drive Estimation from Diaphragm Electromyographic Signals usingSample Entropy of non-invasive EMG signals (Estrada et al., 2016, IEEE Journal of Biomedical and Health Informatics).<\/figcaption><\/figure>\n<\/figure>\n\n\n\n<\/div>

        Staff<\/h2>\n
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