Early prediction of paroxysmal atrial fibrillation based on short-term heart rate variability

Narin, Ali | İşler, Yalçın | Özer, Mahmut | Perc, Matjaž

Article | 2018 | Physica A: Statistical Mechanics and its Applications509 , pp.56 - 65

Atrial fibrillation (AF) is the most common arrhythmia type and its early stage is paroxysmal atrial fibrillation (PAF). PAF affects negatively the quality of life by causing dyspnea, chest pain, feeling of excessive fatigue, and dizziness. In this study, our aim is to predict the onset of paroxysmal atrial fibrillation (PAF) events so that patients can take precautions to prevent PAF events. We use an open data from Physionet, Atrial Fibrillation Prediction Database. We construct our approach based on the heart rate variability (HRV) analysis. Short-term HRV analysis requires 5-minute data so that each dataset was divided into 5-mi . . .nute data segments. HRV features for each segment are calculated from time-domain measures and frequency-domain measures using power spectral density estimations of fast Fourier transform, Lomb–Scargle, and wavelet transform methods. Different combinations of these HRV features are selected by Genetic Algorithm and then applied to k-nearest neighbors classification algorithm. We compute the classifier performances by the 10-fold cross-validation method. The proposed approach results in 92% sensitivity, 88% specificity and 90% accuracy in the 2.5–7.5 min time interval priors to PAF event. The proposed method results in better classification performance than the similar studies in literature. Comparing the existing studies, we propose that our approach provide better tool to predict PAF events. © 2018 Elsevier B.V Daha fazlası Daha az

Autaptic pacemaker mediated propagation of weak rhythmic activity across small-world neuronal networks

Yılmaz, Ergin | Baysal, Veli | Özer, Mahmut | Perc, Matjaž

Article | 2016 | Physica A: Statistical Mechanics and its Applications444 , pp.538 - 546

We study the effects of an autapse, which is mathematically described as a self-feedback loop, on the propagation of weak, localized pacemaker activity across a Newman-Watts small-world network consisting of stochastic Hodgkin-Huxley neurons. We consider that only the pacemaker neuron, which is stimulated by a subthreshold periodic signal, has an electrical autapse that is characterized by a coupling strength and a delay time. We focus on the impact of the coupling strength, the network structure, the properties of the weak periodic stimulus, and the properties of the autapse on the transmission of localized pacemaker activity. Obta . . .ined results indicate the existence of optimal channel noise intensity for the propagation of the localized rhythm. Under optimal conditions, the autapse can significantly improve the propagation of pacemaker activity, but only for a specific range of the autaptic coupling strength. Moreover, the autaptic delay time has to be equal to the intrinsic oscillation period of the Hodgkin-Huxley neuron or its integer multiples. We analyze the inter-spike interval histogram and show that the autapse enhances or suppresses the propagation of the localized rhythm by increasing or decreasing the phase locking between the spiking of the pacemaker neuron and the weak periodic signal. In particular, when the autaptic delay time is equal to the intrinsic period of oscillations an optimal phase locking takes place, resulting in a dominant time scale of the spiking activity. We also investigate the effects of the network structure and the coupling strength on the propagation of pacemaker activity. We find that there exist an optimal coupling strength and an optimal network structure that together warrant an optimal propagation of the localized rhythm. © 2015 Elsevier B.V. All rights reserved Daha fazlası Daha az

Effects of autapse and ion channel block on the collective firing activity of Newman–Watts small-world neuronal networks

Uzun, Rukiye | Yılmaz, Ergin | Özer, Mahmut

Article | 2017 | Physica A: Statistical Mechanics and its Applications486 , pp.386 - 396

An autapse is a special kind of synapse established between the axon and dendrites of the same neuron. In the present study, we have investigated the cooperative effects of autapse and ion channel block on the collective firing regularity of Newman–Watts small-world networks of stochastic Hodgkin–Huxley neurons. We obtain autaptic time delay induced multi-coherence resonance (MCR) phenomenon in the absence of ion channel block. When the ion channel block is considered, we find that this autaptic delay induced MCR phenomenon enhances with the increasing of potassium channel block, whereas it weakens with the increasing of sodium chan . . .nel block at weak and intermediate autaptic conductance regimes. However, at strong autaptic conductance regime neither sodium nor potassium channel block have a significant effect on the collective firing regularity of the network. Besides, we investigate the effects of the coupling strength, the network randomness and the cell size on the regularity. We obtain an optimal coupling strength value and an optimal cell size leading to a more prominent MCR effect. We also show that the MCR phenomenon increases with the increasing of network randomness in potassium channel block, but it needs to a minimum network randomness for its appearing in case of sodium channel block. © 2017 Elsevier B.V Daha fazlası Daha az

Vibrational resonance in a scale-free network with different coupling schemes

Ağaoğlu, Şükrüye Nihal | Çalım, Ali | Hövel, Philipp | Özer, Mahmut | Uzuntarla, Muhammet

Article | 2019 | Neurocomputing325 , pp.59 - 66

We investigate the phenomenon of vibrational resonance (VR) in neural populations, whereby weak low-frequency signals below the excitability threshold can be detected with the help of additional high-frequency driving. The considered dynamical elements consist of excitable FitzHugh–Nagumo neurons connected by electrical gap junctions and chemical synapses. The VR performance of these populations is studied in unweighted and weighted scale-free networks. We find that although the characteristic network features – coupling strength and average degree – do not dramatically affect the signal detection quality in unweighted electrically . . .coupled neural populations, they have a strong influence on the required energy level of the high-frequency driving force. On the other hand, we observe that unweighted chemically coupled populations exhibit the opposite behavior, and the VR performance is significantly affected by these network features whereas the required energy remains on a comparable level. Furthermore, we show that the observed VR performance for unweighted networks can be either enhanced or worsened by degree-dependent coupling weights depending on the amount of heterogeneity. © 2018 Elsevier B.V Daha fazlası Daha az

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