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Publications

2020

IHandU: A novel quantitative wrist rigidity evaluation device for deep brain stimulation surgery

Authors
Murias Lopes, E; Vilas Boas, MD; Dias, D; Rosas, MJ; Vaz, R; Silva Cunha, JP;

Publication
Sensors (Switzerland)

Abstract
Deep brain stimulation (DBS) surgery is the gold standard therapeutic intervention in Parkinson’s disease (PD) with motor complications, notwithstanding drug therapy. In the intraoperative evaluation of DBS’s efficacy, neurologists impose a passive wrist flexion movement and qualitatively describe the perceived decrease in rigidity under different stimulation parameters and electrode positions. To tackle this subjectivity, we designed a wearable device to quantitatively evaluate the wrist rigidity changes during the neurosurgery procedure, supporting physicians in decision-making when setting the stimulation parameters and reducing surgery time. This system comprises a gyroscope sensor embedded in a textile band for patient’s hand, communicating to a smartphone via Bluetooth and has been evaluated on three datasets, showing an average accuracy of 80%. In this work, we present a system that has seen four iterations since 2015, improving on accuracy, usability and reliability. We aim to review the work done so far, outlining the iHandU system evolution, as well as the main challenges, lessons learned, and future steps to improve it. We also introduce the last version (iHandU 4.0), currently used in DBS surgeries at São João Hospital in Portugal. © 2020 by the authors.

2020

SnapKi—An Inertial Easy-to-Adapt Wearable Textile Device for Movement Quantification of Neurological Patients

Authors
Oliveira, A; Dias, D; Lopes, EM; Vilas Boas, MD; Silva Cunha, JPS;

Publication
Sensors

Abstract
The development of wearable health systems has been the focus of many researchers who aim to find solutions in healthcare. Additionally, the large potential of textiles to integrate electronics, together with the comfort and usability they provide, has contributed to the development of smart garments in this area. In the field of neurological disorders with motor impairment, clinicians look for wearable devices that may provide quantification of movement symptoms. Neurological disorders affect different motion abilities thus requiring different needs in movement quantification. With this background we designed and developed an inertial textile-embedded wearable device that is adaptable to different movement-disorders quantification requirements. This adaptative device is composed of a low-power 9-axis inertial unit, a customised textile band and a web and Android cross application used for data collection, debug and calibration. The textile band comprises a snap buttons system that allows the attachment of the inertial unit, as well as its connection with the analog sensors through conductive textile. The resulting system is easily adaptable for quantification of multiple motor symptoms in different parts of the body, such as rigidity, tremor and bradykinesia assessments, gait analysis, among others. In our project, the system was applied for a specific use-case of wrist rigidity quantification during Deep Brain Stimulation surgeries, showing its high versatility and receiving very positive feedback from patients and doctors.

2020

Multimodal Approach for Epileptic Seizure Detection in Epilepsy Monitoring Units

Authors
Maia, P; Lopes, E; Hartl, E; Vollmar, C; Noachtar, S; Silva Cunha, JPS;

Publication
XV MEDITERRANEAN CONFERENCE ON MEDICAL AND BIOLOGICAL ENGINEERING AND COMPUTING - MEDICON 2019

Abstract
Epilepsy is one of the most common neurological disorders, affecting up to 1% of the World population. Patients with epilepsy may suffer from severe consequences from seizures (e.g. injuries) when not monitored. Automatic seizure detection systems could mitigate this problem, improving seizure tracking and alerting a caregiver during a seizure. Existing unimodal solutions for seizure detection, based on electroencephalogram (EEG) and electrocardiogram (ECG) still have an unacceptable level of false positives, which can be reduced by combining these two biosignals. In this paper, EEG and ECG data from 7 epileptic patients with diverse recording length and seizure types were used for analyzing the importance of multimodal seizure detection, at a total of around 110 h 2 m. A leave one seizure out cross validation was selected, grouping data containing the period before a seizure and the seizure period. A proof of concept of multimodal seizure detection which uses a deep learning architecture directly on raw data is performed - a Fully Convolutional Neural Network and an architecture based on LSTM were tested. The network based on LSTM achieved better performance - using the best of one or a combination of both signals, all patients had above 91% detected seizures, a specificity per epoch above 0.96 +/- 0.06 and a detection delay below 8.5 +/- 12 s. These results show potential for developing a patient-specific approach for seizure detection that can be transferred to the ambulatory.

2019

Automated measures of gait dynamics and camptocormia angle in Parkinson's disease before and after subthalamic deep brain stimulation

Authors
Soares, C; Vilas Boas, MDC; Lopes, EM; Choupina, H; Soares Dos Reis, R; Fitas, D; Cunha, JPS; Monteiro, P; Linhares, P; Rosas, MJSL;

Publication
EUROPEAN JOURNAL OF NEUROLOGY

Abstract

2019

iHandU: Towards the Validation of a Wrist Rigidity Estimation for Intraoperative DBS Electrode Position Optimization

Authors
Lopes, EM; Sevilla, A; Vilas Boas, MD; Choupina, HMP; Nunes, DP; Rosas, MJ; Oliveira, A; Massano, J; Vaz, R; Cunha, JPS;

Publication
2019 9TH INTERNATIONAL IEEE/EMBS CONFERENCE ON NEURAL ENGINEERING (NER)

Abstract
DBS surgery is considered for Parkinson's Disease patients when motor complications and consequent quality of life is no longer acceptable on optimal medical therapy prescribed by neurologists. Within the operating room, the electrode placement with the best clinical outcome for the patient is quantitatively assessed via the wrist rigidity assessment. A subjective scale is used, influenced by the neurologists' perception and experience. Our research group has previously designed a novel, comfortable and wireless system aiming to tackle this subjectivity. This system comprised a gyroscope sensor in a textile band, placed in the patients' hand, which communicated its measurement to a Smartphone via Bluetooth. During the wrist rigidity evaluation exam, a signal descriptor was computed from angular velocity (omega) and a polynomial mathematical model was used to classify the signals using a quantitative scale of rigidity improvement. In this present work, we aim to develop models that consider the 3-gyroscope-axes to acquire the omega and the cogwheel rigidity. Our results showed that y-gyroscope-axis remains the best way to classify the rigidity reduction, showing an accuracy of 78% and a mean error of 3.5%. According to previous results, the performance was similar and the decrease of samples to extract the omega features did not compromise system performance. The cogwheel rigidity did not improve the previous model and other gyroscope-axis beyond the y-axis decreased system performance.