Advances in Neural Signal Processing
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information, and EEG analysis is performed on patients who are considered to have
epileptic seizures [6–8]. Mathematical and spectral methods are used very effectively for the diagnosis of the disease during the analysis of EGG data [9–11].
EEG method establishes the basis of epilepsy science, and its history dates back
to the 1940s and is used since then. In principle, it is based on the recording of fluctuations of electrical activity of neurons in the brain, and the main contributions of
EEG for epileptic cases can be summarized as follows: supports a clinically identified diagnosis; is used as a confirmatory test; helps to make diagnosis correctly;
directly and indirectly identifies seizure type and epilepsy syndrome, together with
some findings; and informs about the location of focus [12].
Delta (δ) waves are those with frequencies of 1–4 Hz and amplitudes of
20–400 μV. They are seen in cases when the brain has very low activity, such as deep
sleep, general anesthesia, immune system, natural recovery.
Theta (θ) waves are those with frequencies of 5–7 Hz and amplitudes of
5–100 μV. They are seen in cases when the brain has low activity, such as sleep with
dream, middle anesthesia, stress, emotional commitment.
Alpha (α) waves are those with frequencies of 8–13 Hz and amplitudes of
2–10 μV. They are seen in cases when awake individuals are physically and mentally
full resting, there is no any external stimulant, in relaxed positions and when eyes
are closed. They are most prominently observed in records obtained from the
occipital region.
Beta (β) waves are those with frequencies of 14–30 Hz and amplitudes of
1–5 μV. They are seen in cases, including focused attention, mental work, problem
solving, memory, sensory information processing, rapid eye movements phase of
sleep [13–16].
The EEG signals used in this study are registered at the University Hospital
of Bonn in Germany [17]. The dataset consists of five subsets (denominated as
A, B, C, D and E) that are recorded with the same 128-channel amplifier system
and 12-bit analog-to-digital converter. Each of the subsets contains 100 segments
with a sampling frequency of 173.61 Hz and a duration of 23.6 s, i.e. 4096 sample
points; the corresponding frequency bandwidth is 86.8 Hz. Subsets B, D and E were
analysed in this study. While EEG samples in set B were obtained from five healthy
volunteers via external surface electrodes, for closed eye condition, set D consisted
of EEG segments recorded from patients with epilepsy using intracranial electrodes
to monitor epileptic activity, obtained at the time without seizure. Set D data were
obtained from epileptic area, and they were recorded. Set E contains EEG data,
obtained from patients with epilepsy, recorded at the time of seizure. Strip electrodes were used while recording set E data.
2. Statistical and mathematical background
EEG signals are not deterministic. Since EEG signals do not have a specific shape
as electrocardiogram (ECG) signals do, statistical and parametric methods are used
in the analysis of EEG signals [18–20]. Spectral methods are used for the classification and characterization of EEG signals [21–35].
2.1 Statistical analysis methods
Statistical parameters are used to obtain necessary properties, in most of the
analyses performed in time domain. Although the mean and median values of
signal are expected to result in pretty near zero when the signals have a periodical
and sinusoidal structure, these values can get away from zero by taking positive
80
information, and EEG analysis is performed on patients who are considered to have
epileptic seizures [6–8]. Mathematical and spectral methods are used very effectively for the diagnosis of the disease during the analysis of EGG data [9–11].
EEG method establishes the basis of epilepsy science, and its history dates back
to the 1940s and is used since then. In principle, it is based on the recording of fluctuations of electrical activity of neurons in the brain, and the main contributions of
EEG for epileptic cases can be summarized as follows: supports a clinically identified diagnosis; is used as a confirmatory test; helps to make diagnosis correctly;
directly and indirectly identifies seizure type and epilepsy syndrome, together with
some findings; and informs about the location of focus [12].
Delta (δ) waves are those with frequencies of 1–4 Hz and amplitudes of
20–400 μV. They are seen in cases when the brain has very low activity, such as deep
sleep, general anesthesia, immune system, natural recovery.
Theta (θ) waves are those with frequencies of 5–7 Hz and amplitudes of
5–100 μV. They are seen in cases when the brain has low activity, such as sleep with
dream, middle anesthesia, stress, emotional commitment.
Alpha (α) waves are those with frequencies of 8–13 Hz and amplitudes of
2–10 μV. They are seen in cases when awake individuals are physically and mentally
full resting, there is no any external stimulant, in relaxed positions and when eyes
are closed. They are most prominently observed in records obtained from the
occipital region.
Beta (β) waves are those with frequencies of 14–30 Hz and amplitudes of
1–5 μV. They are seen in cases, including focused attention, mental work, problem
solving, memory, sensory information processing, rapid eye movements phase of
sleep [13–16].
The EEG signals used in this study are registered at the University Hospital
of Bonn in Germany [17]. The dataset consists of five subsets (denominated as
A, B, C, D and E) that are recorded with the same 128-channel amplifier system
and 12-bit analog-to-digital converter. Each of the subsets contains 100 segments
with a sampling frequency of 173.61 Hz and a duration of 23.6 s, i.e. 4096 sample
points; the corresponding frequency bandwidth is 86.8 Hz. Subsets B, D and E were
analysed in this study. While EEG samples in set B were obtained from five healthy
volunteers via external surface electrodes, for closed eye condition, set D consisted
of EEG segments recorded from patients with epilepsy using intracranial electrodes
to monitor epileptic activity, obtained at the time without seizure. Set D data were
obtained from epileptic area, and they were recorded. Set E contains EEG data,
obtained from patients with epilepsy, recorded at the time of seizure. Strip electrodes were used while recording set E data.
2. Statistical and mathematical background
EEG signals are not deterministic. Since EEG signals do not have a specific shape
as electrocardiogram (ECG) signals do, statistical and parametric methods are used
in the analysis of EEG signals [18–20]. Spectral methods are used for the classification and characterization of EEG signals [21–35].
2.1 Statistical analysis methods
Statistical parameters are used to obtain necessary properties, in most of the
analyses performed in time domain. Although the mean and median values of
signal are expected to result in pretty near zero when the signals have a periodical
and sinusoidal structure, these values can get away from zero by taking positive