No abstract

Event-related changes of brain electrical rhythms are typically analysed as amplitude modulations of local field potential (LFP) oscillations, like radio amplitude modulation broadcasting. In telecommunications, frequency modulation (FM) is less susceptible to interference than amplitude modulation (AM) and is therefore preferred for high-fidelity transmissions. Here we hypothesized that LFP rhythms detected from deep brain stimulation (DBS) electrodes implanted in the subthalamic nucleus (STN) in patients with Parkinson's disease could represent movement-related activity not only in AM but also in FM. By combining adaptive autoregressive identification with spectral power decomposition, we were able to show that FM of low-beta (13-20 Hz) and high-beta (20-35 Hz) rhythms significantly contributes to the involvement of the human STN in movement preparation, execution and recovery, and that the FM patterns are regulated by the dopamine levels in the system. Movement-related FM of beta oscillatory activity in the human subthalamic nucleus therefore provides a novel informational domain for rhythm-based pathophysiological models of cortico-basal ganglia processing.

A time-variant algorithm of autoregressive (AR) identification is introduced and applied to the heart rate variability (HRV) signal. The power spectrum is calculated from the AR coefficients derived from each single RR interval considered. Time-variant AR coefficients are determined through adaptive parametric identification with a forgetting factor which obtains weighed values on a running temporal window of 50 preceding measurements. Power spectrum density (PSD) is hence obtained at each cardiac cycle, making it possible to follow the dynamics of the spectral parameters on a beat-by-beat basis. These parameters are mainly the LF (low frequency) and the HF (high frequency) powers, and their ratio LF/HF. These together account for the balanced sympatho-vagal control mechanism affecting the heart rate. This method is applied to subjects suffering from transient ischemic attacks. The time variant spectral parameters suggest an early activation of LF component in the HRV power spectrum. It precedes by approximately 1.5-2 min the tachycardia and the ST displacement, generally indicative of the onset of an ischemic episode. The results suggest an arousal of sympathetic system before the acute attack.

The basic information architecture in the basal ganglia circuit is under debate. Whereas anatomical studies quantify extensive convergence/divergence patterns in the circuit, suggesting an information sharing scheme, neurophysiological studies report an absence of linear correlation between single neurones in normal animals, suggesting a segregated parallel processing scheme. In 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP)-treated monkeys and in parkinsonian patients single neurones become linearly correlated, thus leading to a loss of segregation between neurones. Here we propose a possible integrative solution to this debate, by extending the concept of functional segregation from the cellular level to the network level. To this end, we recorded local field potentials (LFPs) from electrodes implanted for deep brain stimulation (DBS) in the subthalamic nucleus (STN) of parkinsonian patients. By applying bispectral analysis, we found that in the absence of dopamine stimulation STN LFP rhythms became non-linearly correlated, thus leading to a loss of segregation between rhythms. Non-linear correlation was particularly consistent between the low-beta rhythm (13-20 Hz) and the high-beta rhythm (20-35 Hz). Levodopa administration significantly decreased these non-linear correlations, therefore increasing segregation between rhythms. These results suggest that the extensive convergence/divergence in the basal ganglia circuit is physiologically necessary to sustain LFP rhythms distributed in large ensembles of neurones, but is not sufficient to induce correlated firing between neurone pairs. Conversely, loss of dopamine generates pathological linear correlation between neurone pairs, alters the patterns within LFP rhythms, and induces non-linear correlation between LFP rhythms operating at different frequencies. The pathophysiology of information processing in the human basal ganglia therefore involves not only activities of individual rhythms, but also interactions between rhythms.

Through electrodes implanted for deep brain stimulation in three patients (5 sides) with Parkinson's disease, we recorded the electrical activity from the human basal ganglia before, during and after voluntary contralateral finger movements, before and after L-DOPA. We analysed the movement-related spectral changes in the electroencephalographic signal from the subthalamic nucleus (STN) and from the internal globus pallidus (GPi). Before, during and after voluntary movements, signals arising from the human basal ganglia contained two main frequencies: a high beta (around 26 Hz), and a low beta (around 18 Hz). The high beta (around 26 Hz) power decreased in the STN and GPi, whereas the low beta (around 18 Hz) power decrease was consistently found only in the GPi. Both frequencies changed their power with a specific temporal modulation related to the different movement phases. L-DOPA specifically and selectively influenced the spectral power changes in these two signal bands.

This paper presents a method for obstructive sleep apnea (OSA) screening based on the electrocardiogram (ECG) recording during sleep. OSA is a common sleep disorder produced by repetitive occlusions in the upper airways and this phenomenon can usually be observed also in other peripheral systems such as the cardiovascular system. Then the extraction of ECG characteristics, such as the RR intervals and the area of the QRS complex, is useful to evaluate the sleep apnea in noninvasive way. In the presented analysis, 50 recordings coming from the apnea Physionet database were used; data were split into two sets, the training and the testing set, each of which was composed of 25 recordings. A bivariate time-varying autoregressive model (TVAM) was used to evaluate beat-by-beat power spectral densities for both the RR intervals and the QRS complex areas. Temporal and spectral features were changed on a minute-by-minute basis since apnea annotations where given with this resolution. The training set consisted of 4950 apneic and 7127 nonapneic minutes while the testing set had 4428 apneic and 7927 nonapneic minutes. The K-nearest neighbor (KNN) and neural networks (NN) supervised learning classifiers were employed to classify apnea and non apnea minutes. A sequential forward selection was used to select the best feature subset in a wrapper setting. With ten features the KNN algorithm reached an accuracy of 88%, sensitivity equal to 85%, and specificity up to 90%, while NN reached accuracy equal to 88%, sensitivity equal to 89% and specificity equal to 86%. In addition to the minute-by-minute classification, the results showed that the two classifiers are able to separate entirely (100%) the normal recordings from the apneic recordings. Finally, an additional database with eight recordings annotated as normal or apneic was used to test again the classifiers. Also in this new dataset, the results showed a complete separation between apneic and normal recordings.

Background-The wide range of clinical presentation of orthostatic vasovagal syncope suggests different underlying changes in the cardiac autonomic modulation. Methods and Results-To evaluate the beat-by-beat modifications in the neural control of heart period preceding a syncopal event, we studied RR interval variability in 22 healthy subjects who experienced fainting for the first time during a 90°head-up tilt and in 22 control subjects by means of time-variant power spectral analysis. Sympathetic and vagal modulations to the sinoatrial node were assessed by the normalized power of the low-frequency (LF, Ϸ0.1-Hz) and high-frequency (HF, Ϸ0.25-Hz) oscillatory components of RR variability. When the patients were supine, no differences were observed in the hemodynamic and spectral parameters of the 2 groups. During the tilt procedure, RR, LF NU , and HF NU (NUϭnormalized units) values were relatively stable in control subjects. During early tilt (T 1 ), subjects with syncope had reduced RR intervals compared with control subjects. In 13 subjects with syncope, RR decreased while LF NU and LF/HF increased in the last minute of tilt before syncope (T 2 ). Conversely, in the remaining 9 fainters, LF NU and LF/HF decreased from T 1 to T 2 and HF NU increased slightly. Conclusions-Two different patterns may be recognized in the cardiac autonomic changes preceding an occasional vasovagal event, namely, one characterized by a progressive increase of the marker of cardiac sympathetic modulation up to the onset of syncope, the other by a sympathetic inhibition with an impending vagal predominance. The recognition of different pathophysiological mechanisms in fainters may have important therapeutic implications.

We describe a system for the evaluation of the sleep macrostructure on the basis of Emfit sensor foils placed into bed mattress and of advanced signal processing. The signals on which the analysis is based are heart-beat interval (HBI) and movement activity obtained from the bed sensor, the relevant features and parameters obtained through a time-variant autoregressive model (TVAM) used as feature extractor, and the classification obtained through a hidden Markov model (HMM). Parameters coming from the joint probability of the HBI features were used as input to a HMM, while movement features are used for wake period detection. A total of 18 recordings from healthy subjects, including also reference polysomnography, were used for the validation of the system. When compared to wake-nonrapid-eye-movement (NREM)-REM classification provided by experts, the described system achieved a total accuracy of 79+/-9% and a kappa index of 0.43+/-0.17 with only two HBI features and one movement parameter, and a total accuracy of 79+/-10% and a kappa index of 0.44+/-0.19 with three HBI features and one movement parameter. These results suggest that the combination of HBI and movement features could be a suitable alternative for sleep staging with the advantage of low cost and simplicity.