TY  - JOUR
AU  - Bojić, Tijana
AU  - Vučković, Aleksandra
AU  - Kalauzi, Aleksandar
PY  - 2010
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8869
AB  - Aim of this preliminary study was to examine and compare topographic distribution of Higuchi's fractal dimension (FD, measure of signal complexity) of EEG signals between states of relaxed wakefulness and drowsiness, as well as their FD differences. The experiments were performed on 10 healthy individuals using a fourteen-channel montage. An explanation is offered on the causes of the detected FD changes. FD values of 60 s records belonging to wake (Hori's stage 1) and drowsy (Hori's stages 2-4) states were calculated for each channel and each subject. In 136 out of 140 epochs an increase in FD was obtained. Relationship between signal FD and its relative alpha amplitude was mathematically modeled and we quantitatively demonstrated that the increase in FD was predominantly due to a reduction in alpha activity. The model was generalized to include other EEG oscillations. By averaging FD values for each channel across 10 subjects, four clusters (O2O1; T6P4T5P3; C3F3F4C4F8F7; T4T3) for the wake and two clusters (O2O1P3T6P4T5; C3C4F4F3F8T4T3F7) for the drowsy state were statistically verified. Topographic distribution of FD values in wakefulness showed a lateral symmetry and a partial fronto-occipital gradient. In drowsiness, a reduction in the number of clusters was detected, due to regrouping of channels T3, T4, O1 and O2. Topographic distribution of absolute FD differences revealed largest values at F7, O1 and F3. Reorganization of channel clusters showed that regionalized brain activity, specific for wakefulness, became more global by entering into drowsiness. Since the global increase in FD during wake-to-drowsy transition correlated with the decrease of alpha power, we inferred that increase of EEG complexity may not necessarily be an index of brain activation. © 2009 Elsevier Ltd. All rights reserved.
T2  - Journal of Theoretical Biology
T1  - Modeling EEG fractal dimension changes in wake and drowsy states in humans—a preliminary study
VL  - 262
IS  - 2
SP  - 214
EP  - 222
DO  - 10.1016/j.jtbi.2009.10.001
ER  - 

@article{
author = "Bojić, Tijana and Vučković, Aleksandra and Kalauzi, Aleksandar",
year = "2010",
abstract = "Aim of this preliminary study was to examine and compare topographic distribution of Higuchi's fractal dimension (FD, measure of signal complexity) of EEG signals between states of relaxed wakefulness and drowsiness, as well as their FD differences. The experiments were performed on 10 healthy individuals using a fourteen-channel montage. An explanation is offered on the causes of the detected FD changes. FD values of 60 s records belonging to wake (Hori's stage 1) and drowsy (Hori's stages 2-4) states were calculated for each channel and each subject. In 136 out of 140 epochs an increase in FD was obtained. Relationship between signal FD and its relative alpha amplitude was mathematically modeled and we quantitatively demonstrated that the increase in FD was predominantly due to a reduction in alpha activity. The model was generalized to include other EEG oscillations. By averaging FD values for each channel across 10 subjects, four clusters (O2O1; T6P4T5P3; C3F3F4C4F8F7; T4T3) for the wake and two clusters (O2O1P3T6P4T5; C3C4F4F3F8T4T3F7) for the drowsy state were statistically verified. Topographic distribution of FD values in wakefulness showed a lateral symmetry and a partial fronto-occipital gradient. In drowsiness, a reduction in the number of clusters was detected, due to regrouping of channels T3, T4, O1 and O2. Topographic distribution of absolute FD differences revealed largest values at F7, O1 and F3. Reorganization of channel clusters showed that regionalized brain activity, specific for wakefulness, became more global by entering into drowsiness. Since the global increase in FD during wake-to-drowsy transition correlated with the decrease of alpha power, we inferred that increase of EEG complexity may not necessarily be an index of brain activation. © 2009 Elsevier Ltd. All rights reserved.",
journal = "Journal of Theoretical Biology",
title = "Modeling EEG fractal dimension changes in wake and drowsy states in humans—a preliminary study",
volume = "262",
number = "2",
pages = "214-222",
doi = "10.1016/j.jtbi.2009.10.001"
}

Bojić, T., Vučković, A.,& Kalauzi, A.. (2010). Modeling EEG fractal dimension changes in wake and drowsy states in humans—a preliminary study. in Journal of Theoretical Biology, 262(2), 214-222.
https://doi.org/10.1016/j.jtbi.2009.10.001

Bojić T, Vučković A, Kalauzi A. Modeling EEG fractal dimension changes in wake and drowsy states in humans—a preliminary study. in Journal of Theoretical Biology. 2010;262(2):214-222.
doi:10.1016/j.jtbi.2009.10.001 .

Bojić, Tijana, Vučković, Aleksandra, Kalauzi, Aleksandar, "Modeling EEG fractal dimension changes in wake and drowsy states in humans—a preliminary study" in Journal of Theoretical Biology, 262, no. 2 (2010):214-222,
https://doi.org/10.1016/j.jtbi.2009.10.001 . .

TY  - JOUR
AU  - Kalauzi, Aleksandar
AU  - Bojić, Tijana
AU  - Rakić, Ljubisav
PY  - 2009
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8870
AB  - Background: Nonlinear methods provide a direct way of estimating complexity of one-dimensional sampled signals through calculation of Higuchi's fractal dimension (1<FD<2). In most cases the signal is treated as being characterized by one value of FD and consequently analyzed as one epoch or, if divided into more epochs, often only mean and standard deviation of epoch FD are calculated. If its complexity variation (or running fractal dimension), FD(t), is to be extracted, a moving window (epoch) approach is needed. However, due to low-pass filtering properties of moving windows, short epochs are preferred. Since Higuchi's method is based on consecutive reduction of signal sampling frequency, it is not suitable for estimating FD of very short epochs (N < 100 samples).Results: In this work we propose a new and simple way to estimate FD for N < 100 by introducing 'normalized length density' of a signal epoch,.where yn(i) represents the ith signal sample after amplitude normalization. The actual calculation of signal FD is based on construction of a monotonic calibration curve, FD = f(NLD), on a set of Weierstrass functions, for which FD values are given theoretically. The two existing methods, Higuchi's and consecutive differences, applied simultaneously on signals with constant FD (white noise and Brownian motion), showed that standard deviation of calculated window FD (FDw) increased sharply as the epoch became shorter. However, in case of the new NLD method a considerably lower scattering was obtained, especially for N < 30, at the expense of some lower accuracy in calculating average FDw. Consequently, more accurate reconstruction of FD waveforms was obtained when synthetic signals were analyzed, containig short alternating epochs of two or three different FD values. Additionally, scatter plots of FDwof an occipital human EEG signal for 10 sample epochs demontrated that Higuchi's estimations for some epochs exceeded the theoretical FD limits, while NLD-derived values did not.Conclusion: The presented approach was more accurate than the existing two methods in FD(t) extraction for very short epochs and could be used in physiological signals when FD is expected to change abruptly, such as short phasic phenomena or transient artefacts, as well as in other fields of science. © 2009 Kalauzi et al; licensee BioMed Central Ltd.
T2  - Nonlinear Biomedical Physics
T1  - Extracting complexity waveforms from one-dimensional signals
VL  - 3
IS  - 1
SP  - 8
DO  - 10.1186/1753-4631-3-8
ER  - 

@article{
author = "Kalauzi, Aleksandar and Bojić, Tijana and Rakić, Ljubisav",
year = "2009",
abstract = "Background: Nonlinear methods provide a direct way of estimating complexity of one-dimensional sampled signals through calculation of Higuchi's fractal dimension (1<FD<2). In most cases the signal is treated as being characterized by one value of FD and consequently analyzed as one epoch or, if divided into more epochs, often only mean and standard deviation of epoch FD are calculated. If its complexity variation (or running fractal dimension), FD(t), is to be extracted, a moving window (epoch) approach is needed. However, due to low-pass filtering properties of moving windows, short epochs are preferred. Since Higuchi's method is based on consecutive reduction of signal sampling frequency, it is not suitable for estimating FD of very short epochs (N < 100 samples).Results: In this work we propose a new and simple way to estimate FD for N < 100 by introducing 'normalized length density' of a signal epoch,.where yn(i) represents the ith signal sample after amplitude normalization. The actual calculation of signal FD is based on construction of a monotonic calibration curve, FD = f(NLD), on a set of Weierstrass functions, for which FD values are given theoretically. The two existing methods, Higuchi's and consecutive differences, applied simultaneously on signals with constant FD (white noise and Brownian motion), showed that standard deviation of calculated window FD (FDw) increased sharply as the epoch became shorter. However, in case of the new NLD method a considerably lower scattering was obtained, especially for N < 30, at the expense of some lower accuracy in calculating average FDw. Consequently, more accurate reconstruction of FD waveforms was obtained when synthetic signals were analyzed, containig short alternating epochs of two or three different FD values. Additionally, scatter plots of FDwof an occipital human EEG signal for 10 sample epochs demontrated that Higuchi's estimations for some epochs exceeded the theoretical FD limits, while NLD-derived values did not.Conclusion: The presented approach was more accurate than the existing two methods in FD(t) extraction for very short epochs and could be used in physiological signals when FD is expected to change abruptly, such as short phasic phenomena or transient artefacts, as well as in other fields of science. © 2009 Kalauzi et al; licensee BioMed Central Ltd.",
journal = "Nonlinear Biomedical Physics",
title = "Extracting complexity waveforms from one-dimensional signals",
volume = "3",
number = "1",
pages = "8",
doi = "10.1186/1753-4631-3-8"
}

Kalauzi, A., Bojić, T.,& Rakić, L.. (2009). Extracting complexity waveforms from one-dimensional signals. in Nonlinear Biomedical Physics, 3(1), 8.
https://doi.org/10.1186/1753-4631-3-8

Kalauzi A, Bojić T, Rakić L. Extracting complexity waveforms from one-dimensional signals. in Nonlinear Biomedical Physics. 2009;3(1):8.
doi:10.1186/1753-4631-3-8 .

Kalauzi, Aleksandar, Bojić, Tijana, Rakić, Ljubisav, "Extracting complexity waveforms from one-dimensional signals" in Nonlinear Biomedical Physics, 3, no. 1 (2009):8,
https://doi.org/10.1186/1753-4631-3-8 . .

TY  - JOUR
AU  - Bojić, Tijana
AU  - Šaponjić, Jasna
AU  - Radulovački, Miodrag
AU  - Carley, David W
AU  - Kalauzi, Aleksandar
PY  - 2008
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8871
AB  - We applied a novel approach to respiratory waveform analysis-Monotone Signal Segments Analysis (MSSA) on 6-h recordings of respiratory signals in rats. To validate MSSA as a respiratory signal analysis tool we tested it by detecting: breaths and breath-to-breath intervals; respiratory timing and volume modes; and changes in respiratory pattern caused by lesions of monoaminergic systems in rats. MSSA differentiated three respiratory timing (tachypneic, eupneic, bradypneic-apneic), and three volume (artifacts, normovolemic, hypervolemic-sighs) modes. Lesion-induced respiratory pattern modulation was visible as shifts in the distributions of monotone signal segment amplitudes, and of breath-to-breath intervals. Specifically, noradrenergic lesion induced an increase in mean volume (p ≤ 0.03), with no change of the mean breath-to-breath interval duration (p ≥ 0.06). MSSA of timing modes detected noradrenergic lesion-induced interdependent changes in the balance of eupneic (decrease; p ≤ 0.02), and tachypneic (an increase; p ≤ 0.02) breath intervals with respect to control. In terms of breath durations within each timing mode, there was a tendency toward prolongation of the eupneic (p ≤ 0.08) and bradypneic-apneic (p ≤ 0.06) intervals. These results demonstrate that MSSA is sensitive to subtle shifts in respiratory rhythmogenesis not detectable by simple respiratory pattern descriptive statistics. MSSA represents a potentially valuable new tool for investigations of respiratory pattern control. © 2008 Elsevier B.V. All rights reserved.
T2  - Respiratory Physiology and Neurobiology
T1  - Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat
VL  - 161
IS  - 3
SP  - 273
EP  - 280
DO  - 10.1016/j.resp.2008.03.001
ER  - 

@article{
author = "Bojić, Tijana and Šaponjić, Jasna and Radulovački, Miodrag and Carley, David W and Kalauzi, Aleksandar",
year = "2008",
abstract = "We applied a novel approach to respiratory waveform analysis-Monotone Signal Segments Analysis (MSSA) on 6-h recordings of respiratory signals in rats. To validate MSSA as a respiratory signal analysis tool we tested it by detecting: breaths and breath-to-breath intervals; respiratory timing and volume modes; and changes in respiratory pattern caused by lesions of monoaminergic systems in rats. MSSA differentiated three respiratory timing (tachypneic, eupneic, bradypneic-apneic), and three volume (artifacts, normovolemic, hypervolemic-sighs) modes. Lesion-induced respiratory pattern modulation was visible as shifts in the distributions of monotone signal segment amplitudes, and of breath-to-breath intervals. Specifically, noradrenergic lesion induced an increase in mean volume (p ≤ 0.03), with no change of the mean breath-to-breath interval duration (p ≥ 0.06). MSSA of timing modes detected noradrenergic lesion-induced interdependent changes in the balance of eupneic (decrease; p ≤ 0.02), and tachypneic (an increase; p ≤ 0.02) breath intervals with respect to control. In terms of breath durations within each timing mode, there was a tendency toward prolongation of the eupneic (p ≤ 0.08) and bradypneic-apneic (p ≤ 0.06) intervals. These results demonstrate that MSSA is sensitive to subtle shifts in respiratory rhythmogenesis not detectable by simple respiratory pattern descriptive statistics. MSSA represents a potentially valuable new tool for investigations of respiratory pattern control. © 2008 Elsevier B.V. All rights reserved.",
journal = "Respiratory Physiology and Neurobiology",
title = "Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat",
volume = "161",
number = "3",
pages = "273-280",
doi = "10.1016/j.resp.2008.03.001"
}

Bojić, T., Šaponjić, J., Radulovački, M., Carley, D. W.,& Kalauzi, A.. (2008). Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat. in Respiratory Physiology and Neurobiology, 161(3), 273-280.
https://doi.org/10.1016/j.resp.2008.03.001

Bojić T, Šaponjić J, Radulovački M, Carley DW, Kalauzi A. Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat. in Respiratory Physiology and Neurobiology. 2008;161(3):273-280.
doi:10.1016/j.resp.2008.03.001 .

Bojić, Tijana, Šaponjić, Jasna, Radulovački, Miodrag, Carley, David W, Kalauzi, Aleksandar, "Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat" in Respiratory Physiology and Neurobiology, 161, no. 3 (2008):273-280,
https://doi.org/10.1016/j.resp.2008.03.001 . .

TY  - JOUR
AU  - Bojić, Tijana
AU  - Šaponjić, Jasna
AU  - Radulovački, Miodrag
AU  - Carley, David W
AU  - Kalauzi, Aleksandar
PY  - 2008
UR  - https://vinar.vin.bg.ac.rs/handle/123456789/8878
AB  - We applied a novel approach to respiratory waveform analysis-Monotone Signal Segments Analysis (MSSA) on 6-h recordings of respiratory signals in rats. To validate MSSA as a respiratory signal analysis tool we tested it by detecting: breaths and breath-to-breath intervals; respiratory timing and volume modes; and changes in respiratory pattern caused by lesions of monoaminergic systems in rats. MSSA differentiated three respiratory timing (tachypneic, eupneic, bradypneic-apneic), and three volume (artifacts, normovolemic, hypervolemic-sighs) modes. Lesion-induced respiratory pattern modulation was visible as shifts in the distributions of monotone signal segment amplitudes, and of breath-to-breath intervals. Specifically, noradrenergic lesion induced an increase in mean volume (p ≤ 0.03), with no change of the mean breath-to-breath interval duration (p ≥ 0.06). MSSA of timing modes detected noradrenergic lesion-induced interdependent changes in the balance of eupneic (decrease; p ≤ 0.02), and tachypneic (an increase; p ≤ 0.02) breath intervals with respect to control. In terms of breath durations within each timing mode, there was a tendency toward prolongation of the eupneic (p ≤ 0.08) and bradypneic-apneic (p ≤ 0.06) intervals. These results demonstrate that MSSA is sensitive to subtle shifts in respiratory rhythmogenesis not detectable by simple respiratory pattern descriptive statistics. MSSA represents a potentially valuable new tool for investigations of respiratory pattern control. © 2008 Elsevier B.V. All rights reserved.
T2  - Respiratory Physiology and Neurobiology
T1  - Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat
VL  - 161
IS  - 3
SP  - 273
EP  - 280
DO  - 10.1016/j.resp.2008.03.001
ER  - 

@article{
author = "Bojić, Tijana and Šaponjić, Jasna and Radulovački, Miodrag and Carley, David W and Kalauzi, Aleksandar",
year = "2008",
abstract = "We applied a novel approach to respiratory waveform analysis-Monotone Signal Segments Analysis (MSSA) on 6-h recordings of respiratory signals in rats. To validate MSSA as a respiratory signal analysis tool we tested it by detecting: breaths and breath-to-breath intervals; respiratory timing and volume modes; and changes in respiratory pattern caused by lesions of monoaminergic systems in rats. MSSA differentiated three respiratory timing (tachypneic, eupneic, bradypneic-apneic), and three volume (artifacts, normovolemic, hypervolemic-sighs) modes. Lesion-induced respiratory pattern modulation was visible as shifts in the distributions of monotone signal segment amplitudes, and of breath-to-breath intervals. Specifically, noradrenergic lesion induced an increase in mean volume (p ≤ 0.03), with no change of the mean breath-to-breath interval duration (p ≥ 0.06). MSSA of timing modes detected noradrenergic lesion-induced interdependent changes in the balance of eupneic (decrease; p ≤ 0.02), and tachypneic (an increase; p ≤ 0.02) breath intervals with respect to control. In terms of breath durations within each timing mode, there was a tendency toward prolongation of the eupneic (p ≤ 0.08) and bradypneic-apneic (p ≤ 0.06) intervals. These results demonstrate that MSSA is sensitive to subtle shifts in respiratory rhythmogenesis not detectable by simple respiratory pattern descriptive statistics. MSSA represents a potentially valuable new tool for investigations of respiratory pattern control. © 2008 Elsevier B.V. All rights reserved.",
journal = "Respiratory Physiology and Neurobiology",
title = "Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat",
volume = "161",
number = "3",
pages = "273-280",
doi = "10.1016/j.resp.2008.03.001"
}

Bojić, T., Šaponjić, J., Radulovački, M., Carley, D. W.,& Kalauzi, A.. (2008). Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat. in Respiratory Physiology and Neurobiology, 161(3), 273-280.
https://doi.org/10.1016/j.resp.2008.03.001

Bojić T, Šaponjić J, Radulovački M, Carley DW, Kalauzi A. Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat. in Respiratory Physiology and Neurobiology. 2008;161(3):273-280.
doi:10.1016/j.resp.2008.03.001 .

Bojić, Tijana, Šaponjić, Jasna, Radulovački, Miodrag, Carley, David W, Kalauzi, Aleksandar, "Monotone Signal Segments Analysis as a novel method of breath detection and breath-to-breath interval analysis in rat" in Respiratory Physiology and Neurobiology, 161, no. 3 (2008):273-280,
https://doi.org/10.1016/j.resp.2008.03.001 . .