Ming-An Lin

Correlates of the shift in heart rate variability with postures and walking by time-frequency analysis

Heart rate (HR) variability derived from electrocardiogram (ECG) can be used to assess the function of the autonomic nervous system. HR exhibits various characteristics during different physical activities attributed to the altered autonomic mediation, where it is also beneficial to reveal the autonomic shift in response to physical-activity change. In this paper, the physical-activity-related HR behaviors were delineated using a portable ECG and body acceleration recorder based on a personal digital assistant and the smoothed pseudo Wigner-Ville distribution. The results based upon eighteen subjects performing four sequential 5-min physical activities (supine, sitting, standing and spontaneous walking) showed that the high-frequency heartbeat fluctuations during supine and sitting were significantly larger than during standing, and that the ratio of low- to high-frequency fluctuation during standing was significantly higher than during supine and sitting. This could be linked with the parasympathetic predominance during supine and sitting, and a shift to sympathetic dominance while standing. During spontaneous walking, the high-frequency fluctuation was significant lower than during supine. The low- to high-frequency ratio decreased significantly from standing to spontaneous walking, which may imply an increased vagal predominance (autonomic effect) or an increased respiratory activity (mechanical effect).

Detection of neuronal spikes using an adaptive threshold based on the max–min spread sorting method

Neuronal spike information can be used to correlate neuronal activity to various stimuli, to find target neural areas for deep brain stimulation, and to decode intended motor command for brain-machine interface. Typically, spike detection is performed based on the adaptive thresholds determined by running root-mean-square (RMS) value of the signal. Yet conventional detection methods are susceptible to threshold fluctuations caused by neuronal spike intensity. In the present study we propose a novel adaptive threshold based on the max-min spread sorting method. On the basis of microelectrode recording signals and simulated signals with Gaussian noises and colored noises, the novel method had the smallest threshold variations, and similar or better spike detection performance than either the RMS-based method or other improved methods. Moreover, the detection method described in this paper uses the reduced features of raw signal to determine the threshold, thereby giving a simple data manipulation that is beneficial for reducing the computational load when dealing with very large amounts of data (as multi-electrode recordings).

Heart rate variability characterization in daily physical activities using wavelet analysis and multilayer fuzzy activity clustering

A portable data recorder was developed to parallel measure the electrocardiogram and body accelerations. A multilayer fuzzy clustering algorithm was proposed to classify the physical activity based on body accelerations. Discrete wavelet transform was incorporated to retrieve time-varying characteristics of heart rate variability under different physical activities. Nine healthy subjects were included to investigate activity-related heart rate variability during 24 h. The results showed that the heartbeat fluctuations in high frequencies were the greatest during lying and the smallest during standing. Moreover, very-low-frequency heartbeat fluctuations during low activity level (lying) were greater than during high activity level (nonlying).

Classification of neuronal spikes over the reconstructed phase space.

Spike information is beneficial to correlate neuronal activity to various stimuli or determine target neural area for deep brain stimulation. Data clustering based on neuronal spike features provides a way to separate spikes generated from different neurons. Nevertheless, some spikes are aligned incorrectly due to spike deformation or noise interference, thereby reducing the accuracy of spike classification. In the present study, we proposed unsupervised spike classification over the reconstructed phase spaces of neuronal spikes in which the derived phase space portraits are less affected by alignment deviations. Principal component analysis was used to extract major principal components of the portrait features and k-means clustering was used to distribute neuronal spikes into various clusters. Finally, similar clusters were iteratively merged based upon inter-cluster portrait differences.

Unsupervised wavelet-based spike sorting with dynamic codebook searching and replenishment

Spike information is beneficial for correlating neuronal activity to various stimuli, finding target neural areas for deep brain stimulation, and decoding intended motor command for brain-machine interface. Unsupervised classification based on spike features provides a way to separate spikes generated from different neurons. Here, we propose an unsupervised spike sorting method based on specific wavelet coefficients (SWC) and using both a new spike alignment technique based on multi-peak energy comparison (MPEC) and a dynamic codebook-based template-matching algorithm with a class-merging feature. The MPEC alignment reduced inconsistent alignment caused by spike deformation. Using SWC not only reduced the number of features but also performed better in terms of matching a neuronal spike to its own class than relying on spike waveform or whole wavelet coefficients. Moreover, the employed codebook searching and replenishment can be operated in an online, real-time mode.

Heartbeat Detection Using Energy Thresholding and Template Match

Heartbeat detection is very important for retrieving the vital signs of heart functions. The morphologies and inter-beat intervals of heartbeats can reveal the condition of heart contraction. In this paper, we developed a heartbeat information integration scheme to deal with the information yielded by the energy thresholding and template match methods, which are usually used to detect the heartbeats and match the QRS, respectively. The proposed method are developed in SIMULINK 2.0 and assessed by the MIT/BIH arrhythmia database. The result demonstrated excellent sensitivity of detecting QRS and ventricular premature contraction in the proposed method

Complex analysis of neuronal spike trains of deep brain nuclei in patients with Parkinson's disease.

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been used to alleviate symptoms of Parkinsons disease. During image-guided stereotactic surgery, signals from microelectrode recordings are used to distinguish the STN from adjacent areas, particularly from the substantia nigra pars reticulata (SNr). Neuronal firing patterns based on interspike intervals (ISI) are commonly used. In the present study, arrival time-based measures, including Lempel-Ziv complexity and deviation-from-Poisson index were employed. Our results revealed significant differences in the arrival time-based measures among non-motor STN, motor STN and SNr and better discrimination than the ISI-based measures. The larger deviations from the Poisson process in the SNr implied less complex dynamics of neuronal discharges. If spike classification was not used, the arrival time-based measures still produced statistical differences among STN subdivisions and SNr, but the ISI-based measures only showed significant differences between motor and non-motor STN. Arrival time-based measures are less affected by spike misclassifications, and may be used as an adjunct for the identification of the STN during microelectrode targeting.

Linear and Nonlinear EEG Indexes in Relation to the Severity of Coma

Glascows coma scale (GCS) is a clinical standard for assessing the severity of coma in intensive care units (ICUs). The EEG monitoring can be on-line work, soon responds to the change in the brain wave, and allows long-term continuous monitoring of brain activity. In this paper, several EEG parameters, including spectral possession distribution and nonlinear description (approximate entropy and Lempel-Ziv complexity) were used to assess the capability of EEG indexes for the severity of coma. Our results demonstrated that all EEG parameters are moderately related to the GCS, with the Spearman statistical correlation of 0.62-0.71 between the spectral possession distribution and the GCS and 0.62-0.66 between nonlinear measures and the GCS. The moderate correlation between EEG parameters and the GCS implies the possibility of the EEG-derived index to reveal the neurological status of patients in coma

Nonlinear characteristics of heart rate variability during unsupervised and steady physical activities.

The heart rate (HR) exhibits various behavior patterns in different postures and during physical activities, whereas a conventional long-term analysis of HR variability has the confounding effect whether the subject was physically active or immobilized. A specially designed ambulatory recorder that simultaneously measures the electrocardiogram and body accelerations was used to study the short-term (< or =11 beats, alpha1) fractal correlation property and the approximate entropy (ApEn) of RR interval data during sleep, sitting and standing (passive standing or mild walking) levels and immediately after rising in the morning in 15 healthy subjects. The alpha1 exponent that increased from sleep to sitting to standing implies an increased correlation of HR dynamics, which is concomitant with an increased ratio of low-frequency power to high-frequency power (LF/HF) that is usually linked with an increased sympathetic activity. A lower ApEn value during standing and after rising implies a reduced complexity of HR dynamics. Compared to the HR measures during the standing level, the LF/HF ratio showed a quick autonomic shift and alpha1 showed a rapid recruitment of fractal HR behavior after rising, whereas the ApEn value had a slower recovery of HR complexity. In conclusion, both linear and nonlinear HR behaviors during different unsupervised physical activities can be better interpreted with the aid of the recorded movement data.

Linear and nonlinear analysis of electroencephalogram of the coma

The coma is common in intensive care units. The bedside physical examination provides a means to measuring the neurological status, but it cannot be a continuous evaluation, whereas electroencephalogram (EEG) can reflect the immediate electrical activities of the brain. In this paper, we investigate the spectral parameters, complexity and irregular measures, and spectral entropy in the coma. Compared to the normal subject, the EEG of the coma has a dominance of slow wave, low complexity, less irregularity, and low spectral entropy. This result demonstrates the possibility to use EEG analysis for the monitoring of neurological function.