Browsing by Author "Rad, Ali Bahrami"

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Citation - WoS: 112
Citation - Scopus: 130
Analysis of High-Dimensional Phase Space Via Poincare Section for Patient-Specific Seizure Detection
(IEEE-Inst Electrical Electronics Engineers Inc, 2016) Zabihi, Morteza; Kiranyaz, Serkan; Rad, Ali Bahrami; Katsaggelos, Aggelos K.; Gabbouj, Moncef; İnce, Türker
In this paper, the performance of the phase space representation in interpreting the underlying dynamics of epileptic seizures is investigated and a novel patient-specific seizure detection approach is proposed based on the dynamics of EEG signals. To accomplish this, the trajectories of seizure and nonseizure segments are reconstructed in a high dimensional space using time-delay embedding method. Afterwards, Principal Component Analysis (PCA) was used in order to reduce the dimension of the reconstructed phase spaces. The geometry of the trajectories in the lower dimensions is then characterized using Poincare section and seven features were extracted from the obtained intersection sequence. Once the features are formed, they are fed into a two-layer classification scheme, comprising the Linear Discriminant Analysis (LDA) and Naive Bayesian classifiers. The performance of the proposed method is then evaluated over the CHB-MIT benchmark database and the proposed approach achieved 88.27% sensitivity and 93.21% specificity on average with 25% training data. Finally, we perform comparative performance evaluations against the state-of-the-art methods in this domain which demonstrate the superiority of the proposed method.
Citation - WoS: 35
Citation - Scopus: 44
Real-Time Phonocardiogram Anomaly Detection by Adaptive 1d Convolutional Neural Networks
(Elsevier, 2020) Kiranyaz, Serkan; Zabihi, Morteza; Rad, Ali Bahrami; İnce, Türker; Hamila, Ridha; Gabbouj, Moncef
The heart sound signals (Phonocardiogram - PCG) enable the earliest monitoring to detect a potential cardiovascular pathology and have recently become a crucial tool as a diagnostic test in outpatient monitoring to assess heart hemodynamic status. The need for an automated and accurate anomaly detection method for PCG has thus become imminent. To determine the state-of-the-art PCG classification algorithm, 48 international teams competed in the PhysioNet (CinC) Challenge in 2016 over the largest benchmark dataset with 3126 records with the classification outputs, normal (N), abnormal (A) and unsure - too noisy (U). In this study, our aim is to push this frontier further; however, we focus deliberately on the anomaly detection problem while assuming a reasonably high Signal-to-Noise Ratio (SNR) on the records. By using 1D Convolutional Neural Networks trained with a novel data purification approach, we aim to achieve the highest detection performance and real-time processing ability with significantly lower delay and computational complexity. The experimental results over the high-quality subset of the same benchmark dataset show that the proposed approach achieves both objectives. Furthermore, our findings reveal the fact that further improvements indeed require a personalized (patient-specific) approach to avoid major drawbacks of a global PCG classification approach. (C) 2020 The Authors. Published by Elsevier B.V.