George Rigas

Assessment of Tremor Activity in the Parkinson’s Disease Using a Set of Wearable Sensors

Tremor is the most common motor disorder of Parkinsons disease (PD) and consequently its detection plays a crucial role in the management and treatment of PD patients. The current diagnosis procedure is based on subject-dependent clinical assessment, which has a difficulty in capturing subtle tremor features. In this paper, an automated method for both resting and action/postural tremor assessment is proposed using a set of accelerometers mounted on different patients body segments. The estimation of tremor type (resting/action postural) and severity is based on features extracted from the acquired signals and hidden Markov models. The method is evaluated using data collected from 23 subjects (18 PD patients and 5 control subjects). The obtained results verified that the proposed method successfully: 1) quantifies tremor severity with 87 % accuracy, 2) discriminates resting from postural tremor, and 3) discriminates tremor from other Parkinsonian motor symptoms during daily activities.

Real-Time Driver's Stress Event Detection

In this paper, a real-time methodology for the detection of stress events while driving is presented. The detection is based on the use of physiological signals, i.e., electrocardiogram, electrodermal activity, and respiration, as well as past observations of driving behavior. Features are calculated over windows of specific length and are introduced in a Bayesian network to detect drivers stress events. The accuracy of the stress event detection based only on physiological features, evaluated on a data set obtained in real driving conditions, resulted in an accuracy of 82%. Enhancement of the stress event detection model with the incorporation of driving event information has reduced false positives, yielding an increased accuracy of 96%. Furthermore, our methodology demonstrates good adaptability due to the application of online learning of the model parameters.

Methodology for fully automated segmentation and plaque characterization in intracoronary optical coherence tomography images

Abstract. Optical coherence tomography (OCT) is a light-based intracoronary imaging modality that provides high-resolution cross-sectional images of the luminal and plaque morphology. Currently, the segmentation of OCT images and identification of the composition of plaque are mainly performed manually by expert observers. However, this process is laborious and time consuming and its accuracy relies on the expertise of the observer. To address these limitations, we present a methodology that is able to process the OCT data in a fully automated fashion. The proposed methodology is able to detect the lumen borders in the OCT frames, identify the plaque region, and detect four tissue types: calcium (CA), lipid tissue (LT), fibrous tissue (FT), and mixed tissue (MT). The efficiency of the developed methodology was evaluated using annotations from 27 OCT pullbacks acquired from 22 patients. High Pearson’s correlation coefficients were obtained between the output of the developed methodology and the manual annotations (from 0.96 to 0.99), while no significant bias with good limits of agreement was shown in the Bland-Altman analysis. The overlapping areas ratio between experts’ annotations and methodology in detecting CA, LT, FT, and MT was 0.81, 0.71, 0.87, and 0.81, respectively.

Real-time quantification of resting tremor in the Parkinson's disease

Resting tremor (RT) is one of the most frequent signs of the Parkinsons disease (PD), occurring with various severities in about 75% of the patients. Current diagnosis is based on subjective clinical assessment, which is not always easy to capture subtle, mild and intermittent tremors. The aim of the present study is to assess the suitability and clinical value of a computer based real-time system as an aid to diagnosis of PD, in particular the presence of RT. Five healthy subjects were asked to simulate several severities of RT in hands and feet in three static activities. The behaviour of the subjects is measured using tri-axial accelerometers, which are placed at four different positions on the body. Frequency-domain features, strongly correlated with the RT activity, are extracted from the accelerometer data. The classification of RT severity based on those features, provided accuracy 76%. The real-time system designed for efficient extraction of those features and the provision of a continuous RT severity measure is described.

A reasoning-based framework for car driver’s stress prediction

In this work, we present a novel methodology based on a dynamic Bayesian network for the estimation of car drivers stress produced due to specific driving events. the proposed methodology monitors driverpsilas stress using selected biosignals and provides a probabilistic framework in order to infer the driving events resulting in stress level increase. We conducted a series of experiments under real driving conditions. The extracted results indicate a strong correlation between the level of the stress as reported by the driver and the outcome of our model.

Three-dimensional reconstruction of coronary arteries and plaque morphology using CT angiography – comparison and registration with IVUS

BackgroundThe aim of this study is to present a new methodology for three-dimensional (3D) reconstruction of coronary arteries and plaque morphology using Computed Tomography Angiography (CTA).MethodsThe methodology is summarized in six stages: 1) pre-processing of the initial raw images, 2) rough estimation of the lumen and outer vessel wall borders and approximation of the vessel’s centerline, 3) manual adaptation of plaque parameters, 4) accurate extraction of the luminal centerline, 5) detection of the lumen - outer vessel wall borders and calcium plaque region, and 6) finally 3D surface construction.ResultsThe methodology was compared to the estimations of a recently presented Intravascular Ultrasound (IVUS) plaque characterization method. The correlation coefficients for calcium volume, surface area, length and angle vessel were 0.79, 0.86, 0.95 and 0.88, respectively. Additionally, when comparing the inner and outer vessel wall volumes of the reconstructed arteries produced by IVUS and CTA the observed correlation was 0.87 and 0.83, respectively.ConclusionsThe results indicated that the proposed methodology is fast and accurate and thus it is likely in the future to have applications in research and clinical arena.

PD-Manager: An mHealth platform for Parkinson's disease patient management

PD_Manager is a mobile health platform designed to cover most of the aspects regarding the management of Parkinsons disease (PD) in a holistic approach. Patients are unobtrusively monitored using commercial wrist and insole sensors paired with a smartphone, to automatically estimate the severity of most of the PD motor symptoms. Besides motor symptoms monitoring, the patients mobile application also provides various non-motor self-evaluation tests for assessing cognition, mood and nutrition to motivate them in becoming more active in managing their disease. All data from the mobile application and the sensors is transferred to a cloud infrastructure to allow easy access for clinicians and further processing. Clinicians can access this information using a separate mobile application that is specifically designed for their respective needs to provide faster and more accurate assessment of PD symptoms that facilitate patient evaluation. Machine learning techniques are used to estimate symptoms and disease progression trends to further enhance the provided information. The platform is also complemented with a decision support system (DSS) that notifies clinicians for the detection of new symptoms or the worsening of existing ones. As patients symptoms are progressing, the DSS can also provide specific suggestions regarding appropriate medication changes.

Towards building a Dynamic Bayesian Network for monitoring oral cancer progression using time-course gene expression data

In this work we present a methodology for modeling and monitoring the evolvement of oral cancer in remittent patients during the post-treatment follow-up period. Our primary aim is to calculate the probability that a patient will develop a relapse but also to identify the approximate time-frame that this relapse is prone to appear. To this end, we start off by analyzing a broad set of time-course gene expression data in order to identify a set of genes that are mostly differentially expressed between patients with and without relapse and are therefore discriminatory and indicative of a disease reoccurrence evolvement. Next, we employ the maintained genes coupled with a patient-specific risk indicator in order to build upon them a Dynamic Bayesian Network (DBN) able to stratify patients based on their probability for a disease reoccurrence, but also pinpoint an approximate time-frame that the relapse might appear.

Tremor UPDRS estimation in home environment

In this paper, a method for the assessment of the Unified Parkinson Disease Rating scale (UPDRS) related to tremor is presented. The method described consists of hand resting and posture state detection, tremor detection and tremor quantification based on accelerometer and gyroscope readings from a wrist worn sensor. The initial results on PD patient recordings on home environment indicate the feasibility of the proposed method in monitoring UPDRS tremor in patient home environment.

A Gaussian Mixture Model to detect suction events in rotary blood pumps

In this paper, we introduce a new suction detection approach based on online learning of a Gaussian Mixture Model (GMM) with constrained parameters to model the reduction in pump flow signals baseline during suction events. A novel three-step methodology is employed: i) signal windowing, ii) GMM based classification and iii) GMM parameter adaptation. More specifically, the first 5 second segment is used for the parameter initialization and the consequent 1 second windows are classified and used for model adaptation. The proposed approach has been tested in simulation (pump flow) signals and satisfactory results have been obtained.