University of Leicester BHF Research Accelerator for Precision Medicine
Tiago Paggi de Almeida
BEng MSc PhD
British Heart Foundation Research Fellow
tpda2@le.ac.uk
+44 (0)7393 437908
Online profiles
Profile
Tiago Almeida graduated in Electrical Engineering in 2007, and received his PhD in Bioengineering in 2017 from the University of Leicester, UK, under the supervision of Dr Fernando Schlindwein and Prof André Ng. Before re-joining Prof André Ng’s group at the University of Leicester as part of the BHF Research Accelerator in January 2020, Tiago conducted postdoctoral investigations at the Federal ABC University and the Technological Institute of Aeronautics (both in Brazil), and spent 6 months as a Visiting Research Fellow at the Karlsruhe Institute of Technology, in Germany. Tiago also worked in the industry, collaborating with companies as Panasonic, Motorola and Johnson & Johnson, where he took part in Strategic Planning development teams and had the opportunity to further develop his technical background.
Research
During his career, Tiago has dedicated himself to bridging the gap between signal processing methods, biomedical engineering and industrial processes. He has introduced and implemented an artificial intelligence protocol at Johnson & Johnson for the price prediction of raw materials to be purchased annually; applied blind source separation techniques to isolate single motor unit activities from surface electromyographic signals; investigated pattern recognition methods in intracardiac signals during heart rhythm disturbances; and applied innovative multivariate models to help in the therapy of cardiac disorders.
Under the BHF Research Accelerator – coordinated by Prof Gavin Murphy at the University of Leicester – Tiago currently investigates novel biological markers to help in the detection and therapy of cardiac arrhythmias. His research interests are biomedical engineering, digital signal processing and modelling of biological signals, cardiac electrophysiology, analysis of cardiac recordings, and multivariate statistical analysis. He has published 9 works in renowned scientific Journals, more than 50 works in international conferences, concluded 7 supervisions and received 7 awards/recognitions related to his research. He has been coordinating a network of collaborations involving groups from England (University of Leicester and Glenfield Hospital), Italy (Università degli Studi di Trento), Germany (Karlsruhe Institute of Technology) and Brazil (Technological Institute of Aeronautics and Federal ABC University). These have placed him in a privileged position to identify the most important problems in cardiovascular research and to lead investigations of innovative engineering solutions desired by end-users.
Publications
Luongo G, Schuler S, Luik A, Almeida TP, Soriano DC, Dössel O, Loewe A. (2020). Non-Invasive Characterization of Atrial Flutter Mechanisms Using Recurrence Quantification Analysis on the ECG: a Computational Study. Accepted for publication at IEEE Transactions on Biomedical Engineering, May 2020.
Almeida TP, Schlindwein FS, Salinet JL, Li X, Chu GS, Tuan JH, Stafford PJ, Ng GA, Soriano DC (2018). Characterization of the underlying dynamics of atrial tissue activations during persistent atrial fibrillation using recurrence quantification analysis. Chaos, Aug; 28:085710. Doi: 10.1063/1.5024248
Almeida TP, Chu GS, Bell MJ, Li X, Salinet JL, Dastagir N, Tuan JH, Stafford PJ, Ng GA, Schlindwein FS (2018). The temporal behavior and consistency of bipolar atrial electrograms in human persistent atrial fibrillation. Med Biol Eng Comput, 56:71. Doi: 10.1007/s11517-017-1667-1
Salinet JL, Schlindwein FS, Stafford P, Almeida TP, Li X, Vanheusden FJ, Guillem MS, Ng GA (2017). Propagation of Meandering Rotors Surrounded by High Dominant Frequency Areas in Persistent Atrial Fibrillation. Heart Rhythm, 14:1269–1278. Doi: 10.1016/j.hrthm.2017.04.031
Li X, Salinet JL, Almeida TP, Vanheusden FJ, Chu GS, Ng GA, Schlindwein FS (2017) An interactive platform to guide catheter ablation in human persistent atrial fibrillation using dominant frequency, organization and phase mapping. Computer Methods and Programs in Biomedicine, 141:83-92. Doi: 10.1016/j.cmpb.2017.01.011
Almeida TP, Chu GS, Salinet JL, Vanheusden FJ, Li X, Tuan JH, Stafford PJ, Ng GA, Schlindwein FS (2016) Minimizing discordances in automated classification of fractionated electrograms in human persistent atrial fibrillation. Med Biol Eng Comput, 54:1695-1706. Doi: 10.1007/s11517-016-1456-2