Dr Fratini is a Biomedical Engineer with strong interests in the areas of medical biomedical data processing (imaging, segmentation, modelling), and instrumentation (biosensing and diagnostics).
His work has contributed to the design and development of cutting-edge medical devices and improvement of current medical treatments. His research has contributed to the outputs of leading national and international academic institutions and small-to-medium enterprises (SME).
Areas of Expertise (5)
Physiological Data Processing
Wearable Medical Devices
Aston University: PGcPP, Higher Education 2015
Università degli Studi di Napoli Federico II: PhD 2008
Università degli Studi di Napoli Federico II: Hons, Electronic Engineering 2005
- Leader of the Biomedical Engineering Programmes
- Member of the AUEA Board of Directors
Characterisation of the transient mechanical response and the electromyographical activation of lower leg muscles in whole body vibration trainingScientific Reports
2022 The aim of this study is to characterise the transient mechanical response and the neuromuscular activation of lower limb muscles in subjects undergoing Whole Body Vibration (WBV) at different frequencies while holding two static postures, with focus on muscles involved in shaping postural responses. Twenty-five participants underwent WBV at 15, 20, 25 and 30 Hz while in hack squat or on fore feet. Surface electromyography and soft tissue accelerations were collected from Gastrocnemius Lateralis (GL), Soleus (SOL) and Tibialis Anterior (TA) muscles. Estimated displacement at muscle bellies revealed a pattern never highlighted before that differed across frequencies and postures (p
Cortical pathways during Postural Control: new insights from functional EEG source connectivityIEEE Transactions on Neural Systems and Rehabilitation Engineering
2022 Postural control is a complex feedback system that relies on vast array of sensory inputs in order to maintain a stable upright stance. The brain cortex plays a crucial role in the processing of this information and in the elaboration of a successful adaptive strategy to external stimulation preventing loss of balance and falls. In the present work, the participants postural control system was challenged by disrupting the upright stance via a mechanical skeletal muscle vibration applied to the calves. The EEG source connectivity method was used to investigate the cortical response to the external stimulation and highlight the brain network primarily involved in high-level coordination of the postural control system. The cortical network reconfiguration was assessed during two experimental conditions of eyes open and eyes closed and the network flexibility (i.e. its dynamic reconfiguration over time) was correlated with the sample entropy of the stabilogram sway. The results highlight two different cortical strategies in the alpha band: the predominance of frontal lobe connections during open eyes and the strengthening of temporal-parietal network connections in the absence of visual cues. Furthermore, a high correlation emerges between the flexibility in the regions surrounding the right temporo-parietal junction and the sample entropy of the CoP sway, suggesting their centrality in the postural control system. These results open the possibility to employ network-based flexibility metrics as markers of a healthy postural control system, with implications in the diagnosis and treatment of postural impairing diseases.
Toward a priori noise characterization for real-time edge-aware denoising in fluoroscopic devicesBiomedical Engineering Online
2021 Low-dose X-ray images have become increasingly popular in the last decades, due to the need to guarantee the lowest reasonable patient’s exposure. Dose reduction causes a substantial increase of quantum noise, which needs to be suitably suppressed. In particular, real-time denoising is required to support common interventional fluoroscopy procedures. The knowledge of noise statistics provides precious information that helps to improve denoising performances, thus making noise estimation a crucial task for effective denoising strategies. Noise statistics depend on different factors, but are mainly influenced by the X-ray tube settings, which may vary even within the same procedure. This complicates real-time denoising, because noise estimation should be repeated after any changes in tube settings, which would be hardly feasible in practice. This work investigates the feasibility of an a priori characterization of noise for a single fluoroscopic device, which would obviate the need for inferring noise statics prior to each new images acquisition. The noise estimation algorithm used in this study was tested in silico to assess its accuracy and reliability. Then, real sequences were acquired by imaging two different X-ray phantoms via a commercial fluoroscopic device at various X-ray tube settings. Finally, noise estimation was performed to assess the matching of noise statistics inferred from two different sequences, acquired independently in the same operating conditions.