Jenho Tsao

Application of a Modified Entropy Computational Method in Assessing the Complexity of Pulse Wave Velocity Signals in Healthy and Diabetic Subjects

Using 1000 successive points of a pulse wave velocity (PWV) series, we previously distinguished healthy from diabetic subjects with multi-scale entropy (MSE) using a scale factor of 10. One major limitation is the long time for data acquisition (i.e., 20 min). This study aimed at validating the sensitivity of a novel method, short time MSE (sMSE) that utilized a substantially smaller sample size (i.e., 600 consecutive points), in differentiating the complexity of PWV signals both in simulation and in human subjects that were divided into four groups: healthy young (Group 1; n = 24) and middle-aged (Group 2; n = 30) subjects without known cardiovascular disease and middle-aged individuals with well-controlled (Group 3; n = 18) and poorly-controlled (Group 4; n = 22) diabetes mellitus type 2. The results demonstrated that although conventional MSE could differentiate the subjects using 1000 consecutive PWV series points, sensitivity was lost using only 600 points. Simulation study revealed consistent results. By contrast, the novel sMSE method produced significant differences in entropy in both simulation and testing subjects. In conclusion, this study demonstrated that using a novel sMSE approach for PWV analysis, the time for data acquisition can be substantially reduced to that required for 600 cardiac cycles (~10 min) with remarkable preservation of sensitivity in differentiating among healthy, aged, and diabetic populations.

Correlations between the signal complexity of cerebral and cardiac electrical activity: a multiscale entropy analysis.

The heart begins to beat before the brain is formed. Whether conventional hierarchical central commands sent by the brain to the heart alone explain all the interplay between these two organs should be reconsidered. Here, we demonstrate correlations between the signal complexity of brain and cardiac activity. Eighty-seven geriatric outpatients with healthy hearts and varied cognitive abilities each provided a 24-hour electrocardiography (ECG) and a 19-channel eye-closed routine electroencephalography (EEG). Multiscale entropy (MSE) analysis was applied to three epochs (resting-awake state, photic stimulation of fast frequencies (fast-PS), and photic stimulation of slow frequencies (slow-PS)) of EEG in the 1–58 Hz frequency range, and three RR interval (RRI) time series (awake-state, sleep and that concomitant with the EEG) for each subject. The low-to-high frequency power (LF/HF) ratio of RRI was calculated to represent sympatho-vagal balance. With statistics after Bonferroni corrections, we found that: (a) the summed MSE value on coarse scales of the awake RRI (scales 11–20, RRI-MSE-coarse) were inversely correlated with the summed MSE value on coarse scales of the resting-awake EEG (scales 6–20, EEG-MSE-coarse) at Fp2, C4, T6 and T4; (b) the awake RRI-MSE-coarse was inversely correlated with the fast-PS EEG-MSE-coarse at O1, O2 and C4; (c) the sleep RRI-MSE-coarse was inversely correlated with the slow-PS EEG-MSE-coarse at Fp2; (d) the RRI-MSE-coarse and LF/HF ratio of the awake RRI were correlated positively to each other; (e) the EEG-MSE-coarse at F8 was proportional to the cognitive test score; (f) the results conform to the cholinergic hypothesis which states that cognitive impairment causes reduction in vagal cardiac modulation; (g) fast-PS significantly lowered the EEG-MSE-coarse globally. Whether these heart-brain correlations could be fully explained by the central autonomic network is unknown and needs further exploration.

The ultrasonic weak short-pulse responses of microbubbles based on a two-frequency approximation

The ultrasonic short-pulse responses of microbubbles are of interest in cavitation, transient responses, and contrast imaging. We extend the two-frequency analytic solutions of Newhouse and Shankar [J. Acoust. Soc. Am. 75, 1473-1477 (1984)] to approximate the short-pulse responses of microbubbles in a low-amplitude field. Based on their results, there is an expected component near dc in the spectrum of bubble echoes excited by a short pulse. Here this component is named the low-frequency response, and its theoretical properties are verified experimentally. Including the fundamental and second-harmonic components, the weak short-pulse responses of microbubbles include three types of response. Our work has determined the constraint conditions under which this approximated solution can be used to analyze these short-pulse responses. This paper also provides the amplitude and spectral properties of these responses. The low-frequency response has a special bandwidth-dependent property and has potential applications in imaging and bubble sizing.

A new method to derive fetal heart rate from maternal abdominal electrocardiogram: monitoring fetal heart rate during cesarean section.

Background Monitoring of fetal heart rate (FHR) is important during labor since it is a sensitive marker to obtain significant information about fetal condition. To take immediate response during cesarean section (CS), we noninvasively derive FHR from maternal abdominal ECG. Methods We recruited 17 pregnant women delivered by elective cesarean section, with abdominal ECG obtained before and during the entire CS. First, a QRS-template is created by averaging all the maternal ECG heart beats. Then, Hilbert transform was applied to QRS-template to generate the other basis which is orthogonal to the QRS-template. Second, maternal QRS, P and T waves were adaptively subtracted from the composited ECG. Third, Gabor transformation was applied to obtain time-frequency spectrogram of FHR. Heart rate variability (HRV) parameters including standard deviation of normal-to-normal intervals (SDNN), 0V, 1V, 2V derived from symbolic dynamics of HRV and SD1, SD2 derived from Poincareé plot. Three emphasized stages includes: (1) before anesthesia, (2) 5 minutes after anesthesia and (3) 5 minutes before CS delivery. Results FHRs were successfully derived from all maternal abdominal ECGs. FHR increased 5 minutes after anesthesia and 5 minutes before delivery. As for HRV parameters, SDNN increased both 5 minutes after anesthesia and 5 minutes before delivery (21.30±9.05 vs. 13.01±6.89, P < 0.001 and 22.88±12.01 vs. 13.01±6.89, P < 0.05). SD1 did not change during anesthesia, while SD2 increased significantly 5 minutes after anesthesia (27.92±12.28 vs. 16.18±10.01, P < 0.001) and both SD2 and 0V percentage increased significantly 5 minutes before delivery (30.54±15.88 vs. 16.18±10.01, P < 0.05; 0.39±0.14 vs. 0.30±0.13, P < 0.05). Conclusions We developed a novel method to automatically derive FHR from maternal abdominal ECGs and proved that it is feasible during CS.

Synchronized imaging and acoustic analysis of the upper airway in patients with sleep-disordered breathing.

Progressive narrowing of the upper airway increases airflow resistance and can produce snoring sounds and apnea/hypopnea events associated with sleep-disordered breathing due to airway collapse. Recent studies have shown that acoustic properties during snoring can be altered with anatomic changes at the site of obstruction. To evaluate the instantaneous association between acoustic features of snoring and the anatomic sites of obstruction, a novel method was developed and applied in nine patients to extract the snoring sounds during sleep while performing dynamic magnetic resonance imaging (MRI). The degree of airway narrowing during the snoring events was then quantified by the collapse index (ratio of airway diameter preceding and during the events) and correlated with the synchronized acoustic features. A total of 201 snoring events (102 pure retropalatal and 99 combined retropalatal and retroglossal events) were recorded, and the collapse index as well as the soft tissue vibration time were significantly different between pure retropalatal (collapse index, 2 ± 11%; vibration time, 0.2 ± 0.3 s) and combined (retropalatal and retroglossal) snores (collapse index, 13 ± 7% [P ≤ 0.0001]; vibration time, 1.2 ± 0.7 s [P ≤ 0.0001]). The synchronized dynamic MRI and acoustic recordings successfully characterized the sites of obstruction and established the dynamic relationship between the anatomic site of obstruction and snoring acoustics.

Symbolic Entropy of the Amplitude rather than the Instantaneous Frequency of EEG Varies in Dementia

The dynamics of human electroencephalography (EEG) have been proved to be related to cognitive activities. This study separately assessed the two EEG components, amplitude and rhythm, aiming to capture their individual contributions to cognitive functions. We extracted the local peaks of EEGs under rest or photic stimulation and calculated the symbolic dynamics of their voltages (amplitude) and interpeak intervals (instantaneous frequency), individually. The sample consisted of 89 geriatric outpatients in three patient groups: 38 fresh cases of vascular dementia (VD), 22 fresh cases of Alzheimer’s disease (AD) and 29 controls. Both sample entropy and number of forbidden words revealed significantly less regular symbolic dynamics in the whole EEG tracings of the VD than the AD and control groups. We found consistent results between groups with the symbolic dynamics in the local-peak voltage sequence rather than the interpeak interval sequence. Photic stimulation amplified the differences between groups. These results suggest that the EEG dynamics which relates to either cognitive functions or the underlying pathologies of dementia are embedded within the dynamics of the amount of but not the interval between each synchronized firing of adjacent cerebral neurons.

A Novel Application of Multiscale Entropy in Electroencephalography to Predict the Efficacy of Acetylcholinesterase Inhibitor in Alzheimer’s Disease

Alzheimers disease (AD) is the most common form of dementia. According to one hypothesis, AD is caused by the reduced synthesis of the neurotransmitter acetylcholine. Therefore, acetylcholinesterase (AChE) inhibitors are considered to be an effective therapy. For clinicians, however, AChE inhibitors are not a predictable treatment for individual patients. We aimed to disclose the difference by biosignal processing. In this study, we used multiscale entropy (MSE) analysis, which can disclose the embedded information in different time scales, in electroencephalography (EEG), in an attempt to predict the efficacy of AChE inhibitors. Seventeen newly diagnosed AD patients were enrolled, with an initial minimental state examination (MMSE) score of 18.8 ± 4.5. After 12 months of AChE inhibitor therapy, 7 patients were responsive and 10 patients were nonresponsive. The major difference between these two groups is Slope 2 (MSE6 to 20). The area below the receiver operating characteristic (ROC) curve of Slope 2 is 0.871 (95% CI = 0.69–1). The sensitivity is 85.7% and the specificity is 60%, whereas the cut-off value of Slope 2 is −0.024. Therefore, MSE analysis of EEG signals, especially Slope 2, provides a potential tool for predicting the efficacy of AChE inhibitors prior to therapy.

P2B-1 Estimation of Tissue Attenuation Coefficients Using Contrast Agent

Attenuation coefficient (Att. Coef.) is an acoustic parameter for tissue characterization. Two major techniques for Att. Coef. estimation are the backscattering and transmission methods. Backscattering method is suitable for general applications, but the speckle interference makes it inaccurate. Transmission method is more reliable, but it is suitable for peripheral organs only, due to the need of a separate transmitter. With the aid of contrast agent (microbubble), a new way to estimate the Att. Coef. of soft tissues is proposed, which is suitable for general applications. Since microbubble is a highly nonlinear object, using the multiple frequencies generated by microbubbles behind the tissue, the Att. Coef. can be estimated in transmission mode. Since the harmonics are generated by common microbubbles, their relative strengths can be predicted by theory and measured for Att. Coef. estimation. The power spectrum of echo signals are estimated using periodogram. For liver tissue, the frequency dependent Att. Coef. (beta) is estimated as 0.1 dB/cm/MHz. Additional performances of the new technique will be evaluated further in the future

The short-pulse subharmonic response of microbubbles based on a two-frequency approximation

The subharmonic response due to the nonlinear behavior of microbubbles can be used to provide good discrimination between microbubble and surrounding tissue, especially in the deep region. In this work, we extend the 2-frequency approximated analytic solution of Newhouse et al. to derive the subharmonic response of a microbubble under band-limited insonification. Based on Fourier theory, a band-limited signal can be synthesized by multiple sinusoids, with a 2-frequency approximation being the simplest case. Our theoretical analysis illustrates that the amplitude of the subharmonics decreases with the transmitted fractional bandwidth (FB). Moreover, under an applied pressure of 514 kPa, it approaches zero when the FB is increased to 8 %. In other words, this proves theoretically that only narrowband transmission can excite the microbubble to generate the subharmonics. The amplitude of the low-frequency response can be derived to increase with the FB, which is different from that of subharmonics. Experimental data from free gas were used to verify the theoretical predictions.

Mean scatterer spacing estimation using wavelet spectrum [ultrasonic biological tissue characterization applications]

Mean scatterer spacing (MSS) has been proven as an effective method for characterizing biological tissue structures, especially for live tissue. Periodogram, correlogram, cepstrum and spectral autocorrelation (SAC) are the major techniques proposed for MSS estimation. However, due to the interferences of diffusive scattering echo, it is difficult to estimate the MSS of the regular (quasi-periodic) scatterers. Robust techniques, such as the SAC method. which can tolerate the diffusive speckle interference are preferred. A newly developed periodicity detection technique, known as the periodic wavelet transform (PWT) is proposed for MSS estimation here. The wavelet spectrum is defined as the PWT over scale space. The wavelet approach provides a unique framework for the MSS estimation problem.