Markos G. Tsipouras

Learn More
The detection of recorded epileptic seizure activity in EEG segments is crucial for the localization and classification of epileptic seizures. However, since seizure evolution is typically a dynamic and nonstationary process and the signals are composed of multiple frequencies, visual and conventional frequency-based methods have limited application. In(More)
The recording of seizures is of primary interest in the evaluation of epileptic patients. Seizure is the phenomenon of rhythmicity discharge from either a local area or the whole brain and the individual behavior usually lasts from seconds to minutes. Since seizures, in general, occur infrequently and unpredictably, automatic detection of seizures during(More)
The aim of this study is to detect freezing of gait (FoG) events in patients suffering from Parkinson's disease (PD) using signals received from wearable sensors (six accelerometers and two gyroscopes) placed on the patients' body. For this purpose, an automated methodology has been developed which consists of four stages. In the first stage, missing values(More)
OBJECTIVE This paper proposes a knowledge-based method for arrhythmic beat classification and arrhythmic episode detection and classification using only the RR-interval signal extracted from ECG recordings. METHODOLOGY A three RR-interval sliding window is used in arrhythmic beat classification algorithm. Classification is performed for four categories of(More)
OBJECTIVE In the current work we propose a methodology for the automated creation of fuzzy expert systems, applied in ischaemic and arrhythmic beat classification. METHODS The proposed methodology automatically creates a fuzzy expert system from an initial training dataset. The approach consists of three stages: (a) extraction of a crisp set of rules from(More)
We have developed an automatic arrhythmia detection system, which is based on heart rate features only. Initially, the RR interval duration signal is extracted from ECG recordings and segmented into small intervals. The analysis is based on both time and time-frequency (t-f) features. Time domain measurements are extracted and several combinations between(More)
Epilepsy is a neurological disorder with prevalence of about 1-2% of the world’s population (Mormann, Andrzejak, Elger & Lehnertz, 2007). It is characterized by sudden recurrent and transient disturbances of perception or behaviour resulting from excessive synchronization of cortical neuronal networks; it is a neurological condition in which an individual(More)
OBJECTIVE In this study, a methodology is presented for an automated levodopa-induced dyskinesia (LID) assessment in patients suffering from Parkinson's disease (PD) under real-life conditions. METHODS AND MATERIAL The methodology is based on the analysis of signals recorded from several accelerometers and gyroscopes, which are placed on the subjects'(More)
Tremor is the most common motor disorder of Parkinson's 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(More)