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About

About

João Paulo Cunha is Associate Professor (with “Agregação”) at the Department of Electrical and Computer Engineering of the Faculty of Engineering of the University of Porto (FEUP), Portugal and senior researcher at the INESC-TEC: Institute for Systems and Computer Engineering  (http://www.inesctec.pt) where he created the BRAINBiomedical Research And INnovation - research group and co-founded the Center for Biomedical Engineering Research (C-BER) that aggregates ~30 researchers. Prof. Cunha is also affiliated with the Institute for Electronics and Telematics Engineering (IEETA-http://www.ieeta.pt) of the University of Aveiro, Portugal, the Portuguese Brain Imaging Network (http://www.brainimaging.pt ) that he co-founded and co-directed between 2009 and 2012, the Porto Biomechanics Laboratory (http://labiomep.up.pt) and is visiting professor at the Neurology Dep., Faculty of Medicine of the University of Munich (http://www.med.uni-muenchen.de), Bavaria, Germany, since 2002 and at the Carnegie Mellon – Silicon Valley Campus, NASA Ames Research Park, Mountain View, CA, USA since August 2016 (http://www.cmu.edu/silicon-valley/). He presently serves as Scientific Director of the Carnegie-Mellon | Portugal program (http://www.cmuportugal.org) where he is a faculty since 2007, and as the coordinator of the Center of Competencies for the Future Cities of UP (http://futurecities.up.pt).

He earned a degree in Electronics and Telecommunications engineering (1989), a Ph.D. (1996) and an “Agregação” degree (2009) in Electrical Engineering all at the University of Aveiro, Portugal.

Dr. Cunha is Senior Member of the IEEE (2004) where he joined the Engineering in Medicine and Biology Society (EMBS) in 1986 as a student member. He is habitual reviewer of several IEEE journals, such as the IEEE Trans. on Biomedical Eng., IEEE Trans. on Medical Imaging or the IEEE Trans. on Information Technology in Biomedicine. He co-founded in 2007 the spin-off company Biodevices SA (http://www.biodevices.pt) to bring to the market innovative biomedical technology developed for several years in his lab. His R&D activities are focused in Neuro-Engineering and Advanced Human Sensing technologies. Prof. Cunha is co-author of more than 250 scientific publications and 4 patents, holding an h-index of 17, with more than 1000 citations.

Interest
Topics
Details

Details

016
Publications

2022

Does the type of seizure influence heart rate variability changes?

Authors
Faria, MT; Rodrigues, S; Campelo, M; Dias, D; Rego, R; Rocha, H; Sa, F; Tavares Silva, M; Pinto, R; Pestana, G; Oliveira, A; Pereira, J; Cunha, JPS; Rocha Goncalves, F; Goncalves, H; Martins, E;

Publication
Epilepsy and Behavior

Abstract
Objective: Heart rate variability (HRV), an index of the autonomic cardiac activity, is decreased in patients with epilepsy, and a low HRV is associated with a higher risk of sudden death. Generalized tonic-clonic seizures are one of the most consistent risk factors for SUDEP, but the influence (and relative risk) of each type of seizure on cardiac function is still unknown. Our objective was to assess the impact of the type of seizure (focal to bilateral tonic-clonic seizure – FBTCS – versus non-FBTCS) on periictal HRV, in a group of patients with refractory epilepsy and both types of seizures. Methods: We performed a 48-hour Holter recording on 121 patients consecutively admitted to our Epilepsy Monitoring Unit. We only included patients with both FBTCS and non-FBTCS on the Holter recording and selected the first seizure of each type to analyze. To evaluate HRV parameters (AVNN, SDNN, RMSSD, pNN20, LF, HF, and LF/HF), we chose 5-min epochs pre- and postictally. Results: We included 14 patients, with a median age of 36 (min–max, 16–55) years and 64% were female. Thirty-six percent had cardiovascular risk factors, but no previously known cardiac disease. In the preictal period, there were no statistically significant differences in HRV parameters, between FBTCS and non-FBTCS. In the postictal period, AVNN, RMSSD, pNN20, LF, and HF were significantly lower, and LF/HF and HR were significantly higher in FBTCS. From preictal to postictal periods, FBTCS elicited a statistically significant rise in HR and LF/HF, and a statistically significant fall in AVNN, RMSSD, pNN20, and HF. Non-FBTCS only caused statistically significant changes in HR (decrease) and AVNN (increase). Significance/conclusion: This work emphasizes the greater effect of FBTCS in autonomic cardiac function in patients with refractory epilepsy, compared to other types of seizures, with a significant reduction in vagal tonus, which may be associated with an increased risk of SUDEP. © 2021 Elsevier Inc.

2021

A systematic review on the use of immersive virtual reality to train professionals

Authors
Narciso, D; Melo, M; Rodrigues, S; Cunha, JP; Vasconcelos Raposo, J; Bessa, M;

Publication
MULTIMEDIA TOOLS AND APPLICATIONS

Abstract
The main goal of this systematic review is to synthesize existing evidence on the use of immersive virtual reality (IVR) to train professionals as well as to identify the main gaps and challenges that still remain and need to be addressed by future research. Following a comprehensive search, 66 documents were identified, assessed for relevance, and analysed. The main areas of application of IVR-based training were identified. Moreover, we identified the stimuli provided, the hardware used and information regarding training evaluation. The results showed that the areas in which a greater number of works were published were those related to healthcare and elementary occupations. In hardware, the most commonly used equipment was head mounted displays (HMDs), headphones included in the HMDs and handheld controllers. Moreover, the results indicated that IVR training systems are often evaluated manually, the most common metric being questionnaires applied before and after the experiment, and that IVR training systems have a positive effect in training professionals. We conclude that the literature is insufficient for determining the effect of IVR in the training of professionals. Although some works indicated promising results, there are still relevant themes that must be explored and limitations to overcome before virtual training replaces real-world training.

2021

Changes in heart rate variability after transcranial direct current stimulation in patients with refractory epilepsy

Authors
Lopes, EM; Van Rafelghem, L; Dias, D; Nunes, MC; Hordt, M; Noachtar, S; Kaufmann, E; Cunha, JPS;

Publication
International IEEE/EMBS Conference on Neural Engineering, NER

Abstract
Cathodal transcranial direct current stimulation (c-tDCS) is a non-invasive option for treatment of refractory epilepsy. However, it is still unknown whether this therapy has a positive stabilizing effect on the vegetative function of these patients. Heart Rate Variability (HRV) is considered an efficient tool to monitor the cardiac autonomic system, which has been correlated with the risk of Sudden Unexpected Death in Epilepsy (SUDEP). In this study, changes in HRV are investigated after c-tDCS of six patients (34.50 ± 11.10 years) with refractory epilepsy, which have been selected at the University Hospital, LMU Munich. Patients were categorized as responders (n=2), non-responders (n=3) and uncategorized (n=1). We analyzed 24 hours of electrophysiological data recorded before and after treatment, and computed HRV metrics (AVNN, SDNN, RMSD, pNN20, pNN50, LH/HF, 0V, IV, 2LV, 2UV, SD1 and SD2). All patients revealed a change in almost all HRV metrics post stimulation. Grouped all patients, there was a significant (p < 0.05) change in RMSSD, pNN50, SD1 and LH/HF. For responders there was an increase in all time domain and non-linear metrics, which was not seen for non-responders. These results suggest that tDCS exerts significant changes in cardiovascular autonomic system in patients with refractory epilepsy. HRV metrics may also serve as biomarkers of the response to tDCS stimulation. A larger dataset is being gathered for further analysis. © 2021 IEEE.

2021

DeepEpil: Towards an Epileptologist-Friendly AI Enabled Seizure Classification Cloud System based on Deep Learning Analysis of 3D videos

Authors
Karacsony, T; Loesch Biffar, AM; Vollmar, C; Noachtar, S; Cunha, JPS;

Publication
2021 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)

Abstract

2021

Video-EEG and PerceptTM PC Deep Brain Neurostimulator Fine-Grained Synchronization for Multimodal Neurodata Analysis

Authors
Lopes, EM; Vilas Boas, MD; Rego, R; Santos, A; Cunha, JPS;

Publication
2021 10TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)

Abstract
Adaptive Deep Brain Stimulation has recently emerged to tackle conventional DBS limitations by measuring disease fluctuations and to adapt stimulation parameter accordingly. In early 2020, Medtronic launched in the European Union the first certified DBS neurostimulator capable of simultaneously stimulate and read signals from the deep brain structures, the PerceptTMPC. In epilepsy, the most common target brain structure is the Anterior Nucleus of Thalamus and the Local Field Potentials analysis requires prior synchronization of data recorded from the Percept PC with video-Electroencephalography (vEEG) equipment. Fine-grained synchronization (sub-second resolution) is mandatory for multimodal neurodata analysis and may be achieved by aligning artefacts perceived in both systems. In this work we study two methods aiming for neurodata streams clock synchronization: one based on DBS stimulation artefacts and another on tapping maneuver artefacts. For this purpose, we studied the data collected from the first epileptic patient that underwent 1-week vEEG-PerceptTMPC monitoring at a Hospital monitoring unit. We found that tapping maneuver-based methodology allowed a more accurate synchronization in relation to the stimulation artefact-based method (0.56s vs. 2.07s absolute average uncertainty). This method was also more complete one since tapping timestamps can be determined by video timeframes and do not require a prior identification of artefacts in EEG data by clinicians.

Supervised
thesis

2021

Towards Personalized In-Silico Mathematical Models and Tools for Brain Networks Simulations and Study of Optimal Therapeutic Approaches for Refractory Epilepsy

Author
Elodie Múrias Lopes

Institution
UP-FEUP

2021

Towards New Neural Correlates of Memory from Deep and Cortex Brain Regions in Deep Brain Stimulation Patients

Author
Ana Sofia Santos Cardoso

Institution
UP-FEUP

2021

VitalCoViD Platform: web-based telehealth system for real-time monitoring of CoViD-19 patients at home using wearable health devices.

Author
Diogo Leal da Silva Mota Pinto

Institution
UP-FEUP

2021

Biophotonics signal analysis for biomarker discovery in bio-fluids

Author
Beatriz Isabel Jacinto Barros

Institution
UP-FEUP

2021

FAPMOVE - Motor impairment assessment in Familial Amyloid Polyneuropathy Patients

Author
Maria do Carmo Sousa Cardoso Vilas Boas de Olazabal

Institution
UP-FEUP