Comparative study of left atrium epicardial fat tissue pattern using persistent homology approach.

OBJECTIVE: Atrial Fibrillation (A-fib) is an abnormal heartbeat condition in which the heart races and beats in an uncontrollable way. It is observed that the presence of increased epicardial fat/fatty tissue in the atrium can lead to A-fib. Persistent homology using topological features can be used to recapitulate enormous amounts of spatially complicated medical data into a visual code to identify a specific pattern of epicardial fat tissue with non-fat tissue. Our aim is to evaluate the topological pattern of left atrium epicardial fat tissue with non-fat tissue. RESULTS: A topological data analysis approach was acquired to study the imaging pattern between the left atrium epicardial fat tissue and non-fat tissue patches. The patches of eight patients from CT images of the left atrium heart were used and categorized into "left atrium epicardial fat tissue" and "non-fat tissue" groups. The features that distinguish the "epicardial fat tissue" and "non-fat tissue" groups are extracted using persistent homology (PH). Our result reveals that our proposed research can discriminate between left atrium epicardial fat tissue and non-fat tissue. Specifically, the range of Betti numbers in the epicardial tissue is smaller (0-30) than the non-fat tissue (0-100), indicating that non-fat tissue has good topology.

Radon descriptor-based machine learning using CT images to predict the fat tissue on left atrium in the heart.

Heart disease has a higher fatality rate than any other disease. Increased Atrial fat on the left atrium has been discovered to cause Atrial Fibrillation (AF) in most patients. AF can put one's life at risk and eventually lead to death. AF might worsen over time; therefore, it is crucial to have an early diagnosis and treatment. To evaluate the left atrium fat tissue pattern using Radon descriptor-based machine learning. This study developed a bridge between the Radon transform framework and machine learning to distinguish two distinct patterns. Motivated by a Radon descriptor-based machine learning approach, the patches of eight patients from CT images of the heart were used and categorized into "epicardial fat tissue" and "nonfat tissue" groups. The 10 feature vectors are extracted from each big patch using Radon descriptors and then fed into a traditional machine learning model. The results show that the proposed methodology discriminates between fat tissues and nonfat tissues clearly. KNN has shown the best performance with 96.77% specificity, 98.28% sensitivity, and 97.50% accuracy. To our knowledge, this study is the first attempt to provide a Radon transform-based machine learning method to distinguish between fat tissue and nonfat tissue on the left atrium. Our proposed research method could be potentially used in advanced interventions.

An automated method for detecting atrial fat using convolutional neural network.

Atrial Fibrillation (A-fib) is a common cardiac rhythm problem in the population these days in which irregular heartbeat leads to blood clots, heart failure, stroke, and other significant clinical complications. Researchers have found that the atrial fat can lead to AF in most patients. To develop an automated method for detecting the epicardial fat present in the atrium using a Convolutional Neural Network. Cardiac Computed Tomography (CT) images of ten patients were pre-processed to remove the unwanted structure around the heart. An automated pixel value masking was done to locate the epicardial fat in the atrium and a 3D view of the heart was constructed for correct visualization of the location of the fat. A fast and fully automated Convolutional Neural Network (CNN) was applied to detect the atrial epicardial fat through feature selection from the CT images. We achieved 89.22% accuracy, 90.18% sensitivity, and 88.52% specificity in the detection of atrial epicardial fat using our CNN architecture. Our results showed that this CNN-based method can be helpful in atrial epicardial fat detection. Since Deep learning techniques add robustness, rapidness, and reliability, this study provides an unutilized way to detect the atrial fat tissue.

Non-ischemic endocardial scar geometric remodeling toward topological machine learning.

Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and better evaluation of cardiovascular diseases. Our goal is to apply topology data analysis toward machine learning algorithms to confirm the geometry of scar tissue, in addition to gaining better visualization and quantification of the scar tissue present. We have introduced architecture for integrating geometry in the form of topology toward machine learning. Morphological image processing was carried out to define the regions of the endocardial wall. We implemented convolutional neural networks on delayed enhancement cardiac computed tomography images for the recognition of scar tissue. Segmented two-dimensional images were stacked up to build the geometry of the scar area for visualization purposes. Mathematical calculations were executed for the validation of the scar tissue in addition to performing morphological image processing and marking the scar tissue present on the endocardial wall of the left ventricular. We applied convolutional neural network over convolution and pooling the layers with small sizes; we achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity, and found the dissimilarity distance between the normal endocardial tissue distances to be 9.37. This new concept in this study contributes toward a better understanding of scar structure and transmural variation of the endocardial wall of the left ventricular.

An Automated Method for Detecting the Scar Tissue in the Left Ventricular Endocardial Wall Using Deep Learning Approach.

BACKGROUND: Image evaluation of scar tissue plays a significant role in the diagnosis of cardiovascular diseases. Segmentation of the scar tissue is the first step towards evaluating the morphology of the scar tissue. Then, with the use of CT images, the deep learning approach can be applied to identify possible scar tissue in the left ventricular endocardial wall. OBJECTIVES: To develop an automated method for detecting the endocardial scar tissue in the left ventricular using Deep learning approach. METHODS: Pixel values of the endocardial wall for each image in the sequence were extracted. Morphological operations, including defining regions of the endocardial wall of the LV where scar tissue could predominate, were performed. Convolutional Neural Networks (CNN) is a deep learning application, which allowed choosing appropriate features from delayed enhancement cardiac CT images to distinguish between endocardial scar and healthy tissues of the LV by applying pixel value-based concepts. RESULTS: We achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity in the detection of endocardial scars using the CNN-based method. CONCLUSION: Our findings reveal that the CNN-based method yielded robust accuracies in LV endocardial scar detection, which is currently the most extensively used pixel-based method of deep learning. This study provides a new direction for the assessment of scar tissue in imaging modalities and provides a potential avenue for clinical adaptations of these algorithms. Additionally this methodology, in comparison with those in the literature, provides specific advantages in its translational ability to clinical use.

Functional Remodeling of Both Atria is Associated with Occurrence of Stroke in Patients with Paroxysmal and Persistent Atrial Fibrillation.

BACKGROUND: It is critical to recognize high risk patients who are prone to develop stroke in the management of atrial fibrillation (AF). The purpose of this study was to identify the determinants of AF related stroke by assessing the anatomical and functional remodeling of cardiac chambers. METHODS: We compared the cardiac structure and function of 28 consecutive patients with paroxysmal and persistent AF-related stroke with 69 patients with AF and 21 controls without stroke using contrast-enhanced 64-slice multi-detector computed tomography during sinus rhythm. RESULTS: The volume of left atrium (LA), LA appendage (LAA) and right atrium (RA) were significantly increased across the groups with sinus rhythm (SR), AF and AF-related stroke (p < 0.001 for each, respectively). The emptying fraction and booster-pump function of LA, LAA and RA were decreased across the groups (p < 0.001 for each). In addition, the left ventricular mass index was increased in AF related stroke (p = 0.003). Using multivariate analysis, increased age (p = 0.003), reduced booster-pump function of LA (p = 0.01), LAA (p < 0.001) and RA (p < 0.001) were shown to be independently associated with the occurrence of stroke. CONCLUSIONS: The dilatation and contractile dysfunction of both atria are related to the development of stroke in patients with paroxysmal and persistent AF. Our results suggested that the use of substrate-based assessment may help improve risk stratification of stroke in patients with AF.

The Abundance of Epicardial Adipose Tissue Surrounding Left Atrium Is Associated With the Occurrence of Stroke in Patients With Atrial Fibrillation.

Epicardial adipose tissue (EAT) is positively associated with risk factors for cardiovascular disease, but the role of EAT in the development of atrial fibrillation (AF)-related stroke and its association with the anatomical and functional remodeling of the left atrium (LA) have not been elucidated.This was a comparative cross-sectional study. Twenty-seven patients with paroxysmal or persistent AF and cardioembolic stroke were selected and compared with 68 age- and sex-matched AF patients without stroke. In addition, 20 controls without a history of AF or stroke were included. The periatrial EAT and the structural and functional properties of the LA and left ventricle were evaluated using contrast-enhanced 64-slice multidetector computed tomography during sinus rhythm. Total EAT around the LA was significantly increased across the groups (control vs AF vs AF-related stroke, P < 0.001). The volumes of the LA and the LA appendage (LAA) were also significantly increased across the 3 groups (P < 0.001 for each). The emptying fraction of the LA and LAA and the booster-pump function of the LA and LAA were all reduced across the 3 groups (P < 0.001 for all). In addition, the Hounsfield unit (HU) ratio of the LAA to the ascending aorta (LAA/AA) was also decreased in patients with stroke (P < 0.001). Furthermore, EAT had a negative correlation with the dynamic function of the LA, LAA, and the HU ratio. After a multivariate analysis, increased EAT (P < 0.001) was shown to be independently associated with the occurrence of AF-related stroke.Periatrial EAT was increased and was correlated with atrial dysfunction in patients with AF-related stroke. Hence, EAT assessment may potentially offer an incremental value for grading the risk of cardioembolic stroke in patients with AF.

Is Noninvasive Brachial Systolic Blood Pressure an Accurate Estimate of Central Aortic Systolic Blood Pressure?

OBJECTIVES: Noninvasive brachial systolic blood pressure (nSBP-B) usually approaches invasive central systolic blood pressure (iSBP-C) with a high correlation. Whether nSBP-B is an accurate estimate of iSBP-C remained to be investigated. Thus, this study aimed to compare the errors of nSBP-B and noninvasive central systolic blood pressure (nSBP-C) with different techniques in estimating iSBP-C. METHODS: Simultaneous invasive high-fidelity central aortic pressure waveforms and the noninvasive left brachial pulse volume recording (PVR) waveform were recorded in a Generation group ( N = 40) and a Validation group ( N = 100). The accuracy of the noninvasive estimates of iSBP-C obtained from analysis of the calibrated PVR waveform using the generalized transfer function (GTF), pulse waveform analysis (PWA), and N-point moving average (NPMA) methods was examined in the Validation group by calculating the mean absolute error (MAE). RESULTS: In Generation group, the MAE was 4.6±4.1mm Hg between nSBP-B and invasive brachial SBP, and 6.8±5.5mm Hg between nSBP-B and iSBP-C. In comparison, the MAE of between iSBP-C and nSBP-C with PWA, NPMA, and GTF were 5.5±4.5, 5.8±4.9, and 5.9±5.0mm Hg, respectively. In Validation group, the MAE of nSBP-B (6.9±4.6mm Hg) for estimating iSBP-C was significantly greater than that of PWA (5.0±3.4mm Hg) and NPMA (6.1±4.4mm Hg), and GTF (6.1±4.9mm Hg). The percentage of absolute band error ≤5mm Hg was 62% for nSBP-B, 69% for GTF, 83% for PWA, and 69% for NPMA. CONCLUSIONS: The accuracy of nSBP-B was inferior to the n SBP-C measures in estimating iSBP-C.

Efficacy of informed versus uninformed physiotherapy on postoperative retear rates of medium-sized and large rotator cuff tears.

BACKGROUND: It is important to perform the first 12 weeks of rehabilitation without risk of tearing a cuff tendon from its repair site. Our hypothesis was that performing early postoperative rehabilitation with a limitable pendulum exercise device can produce lower retear rate outcomes when it is combined with safe, informed physiotherapy compared with a standardized protocol of rehabilitation performed at home. METHODS: By using an asymmetric arm support brace and an advanced accelerometer, we attempted to determine the benefits of small pendulum exercises (proposed by Long et al). This study enrolled 24 patients to use a monitoring device in standardized small pendulum exercises. Clinical outcomes and magnetic resonance images were evaluated preoperatively and 12 weeks after surgery. RESULTS: While a patient performed pendulum exercises, a therapist used computer imagery to observe whether vertical acceleration was over a given threshold (identified as physiologic tremors), as a warning of and precaution associated with the increased risk of repair failure. Similar self-reported functional outcomes were reported in 2 areas. The rate of recurrent tears was significantly higher for both the medium-sized and large areas in the uninformed home rehabilitation group compared with the informed group. CONCLUSION: The results of monitoring of pendulum exercises to develop informed physical therapeutic methodology were consistent with those of previously published literature. In this study, use of a monitoring device during early rehabilitation was associated with lower retear rates after rotator cuff repair.

Application of the N-point moving average method for brachial pressure waveform-derived estimation of central aortic systolic pressure.

The N-point moving average (NPMA) is a mathematical low-pass filter that can smooth peaked noninvasively acquired radial pressure waveforms to estimate central aortic systolic pressure using a common denominator of N/4 (where N=the acquisition sampling frequency). The present study investigated whether the NPMA method can be applied to brachial pressure waveforms. In the derivation group, simultaneously recorded invasive high-fidelity brachial and central aortic pressure waveforms from 40 subjects were analyzed to identify the best common denominator. In the validation group, the NPMA method with the obtained common denominator was applied on noninvasive brachial pressure waveforms of 100 subjects. Validity was tested by comparing the noninvasive with the simultaneously recorded invasive central aortic systolic pressure. Noninvasive brachial pressure waveforms were calibrated to the cuff systolic and diastolic blood pressures. In the derivation study, an optimal denominator of N/6 was identified for NPMA to derive central aortic systolic pressure. The mean difference between the invasively/noninvasively estimated (N/6) and invasively measured central aortic systolic pressure was 0.1±3.5 and -0.6±7.6 mm Hg in the derivation and validation study, respectively. It satisfied the Association for the Advancement of Medical Instrumentation standard of 5±8 mm Hg. In conclusion, this method for estimating central aortic systolic pressure using either invasive or noninvasive brachial pressure waves requires a common denominator of N/6. By integrating the NPMA method into the ordinary oscillometric blood pressure determining process, convenient noninvasive central aortic systolic pressure values could be obtained with acceptable accuracy.

Classification of oscillometric envelope shape using frequent sequence mining.

The shape of the oscillometric envelope is known to affect the accuracy of automatic noninvasive blood pressure (NIBP) measurement devices that use the oscillometric principle to determine systolic and diastolic blood pressures. This study proposes a novel shape classification method that uses data mining techniques to determine the characteristic sequences for different envelope shapes. The results indicate that the proposed method effectively determines the characteristic sequences for different subject groups. Subjects in the high- score group and in the low- score group tend to have an envelope with a broader plateau and are bell-shaped, respectively. This information about shape can be used for future determination of the correct algorithm for systolic and diastolic blood pressures determination in NIBP devices.

Development of a cuffless blood pressure measurement system.

This study constructs a novel blood pressure measurement device without the air cuff to overcome the problem of discomfort and portability. The proposed device measures the blood pressure through a mechanism that is made of silicon rubber and pressure transducer. The system uses a microcontroller to control the measurement procedure and to perform the necessary computation. To verify the feasibility of the constructed device, ten young volunteers were recruited. Ten blood pressure readings were obtained using the new system and were compared with ten blood pressure readings from bedside monitor (Spacelabs Medical, model 90367). The results indicated that, when all the readings were included, the mean pressure, systolic pressure and diastolic pressure from the new system were all higher than those from bedside monitor. The correlation coefficients between these two were 0.15, 0.18 and 0.29, for mean, systolic and diastolic pressures, respectively. After excluding irregular apparatus utilization, the correlation coefficient increased to 0.71, 0.60 and 0.41 for diastolic pressure, mean pressure and systolic pressure, respectively. We can conclude from these results that the accuracy can be improved effectively by defining the user regulation more precisely. The above mentioned irregular apparatus utilization factors can be identified and eliminated by the microprocessor to provide a reliable blood pressure measurement in practical applications in the future.

Quantification of the calibration error in the transfer function-derived central aortic blood pressures.

BACKGROUND: The accuracy of the central aortic systolic (SBP-C) and pulse (PP-C) blood pressures estimated noninvasively by a generalized transfer function technique has been questioned. The purpose of the present study was to quantify precisely the impact of the input errors (differences between the oscillometric (SBP-O, DBP-O, PP-O) and invasive (SBP-B, DBP-B, PP-B) brachial systolic, diastolic, and pulse blood pressures) on the output errors (differences between the estimated and invasively measured SBP-C and PP-C). METHODS: Invasive high-fidelity right brachial and central aortic pressure waveforms, and SBP-O, DBP-O, and PP-O (=SBP-O - DBP-O) were obtained simultaneously in 40 patients during cardiac catheterization. A generalized transfer function was applied on the individual brachial pressure waveform to derive predicted SBP-C and PP-C. RESULTS: Observed input errors were -2.3 ± 5.8 mm Hg from SBP-O, 8.1 ± 5.3 mm Hg from DBP-O, and -10.4 ± 7.1 mm Hg from PP-O, respectively. The output errors were -2.2 ± 6.4 mm Hg and -10.3 ± 8.0 mm Hg for SBP-C and PP-C, respectively, when the brachial pressure waveforms were recalibrated using SBP-O and DBP-O. The outputs were determined by the inputs according to the Equation (1): SBP-C error = 0.97 × SBP-O error + 0.03 (r = 0.88, P < 0.01); and the Equation (2): PP-C error = 0.96 × PP-O error - 0.30 (r = 0.86, P < 0.01). CONCLUSIONS: Noninvasive application of the generalized transfer function techniques produces estimates of SBP-C and PP-C with errors equivalent to those of the oscillometric blood pressure monitor in the estimation of SBP-B and PP-B. The output errors can be predicted from input errors of SBP-O and DBP-O.

Quantitative analysis of quantity and distribution of epicardial adipose tissue surrounding the left atrium in patients with atrial fibrillation and effect of recurrence after ablation.

Epicardial adipose tissue (EAT) contains ganglionated plexuses and adipocytes that can affect the pathogenesis of atrial fibrillation (AF). The aim of this study was to quantify the EAT surrounding the left atrium (LA) and correlate it with occurrence of AF and outcome after catheter ablation. EAT was evaluated using 64-slice multidetector computed tomography in 68 patients with AF and 34 controls. EAT volume was acquired by semiautomatically tracing axial images from the pulmonary artery to the coronary sinus. Topographic distribution of EAT was assessed by dividing the periatrial space into 8 equal regions. EAT volume significantly increased in patients with AF than in controls (29.9 ± 12.1 vs 20.2 ± 6.5 cm(3), p <0.001). Most EAT was located in regions (1) within the superior vena cava, right pulmonary artery, and right-sided roof of the LA (29.8%), (2) within the aortic root, pulmonary trunk, and left atrial appendage (26.5%), and (3) between the left inferior pulmonary vein and left atrioventricular groove (18.1%). Baseline variables were analyzed in patients with (n = 24) and without (n = 44) AF recurrence after ablation. The recurrent group showed significantly increased EAT (35.2 ± 12.5 vs 26.8 ± 11.1 cm(3), p = 0.007). Multivariate analysis revealed that EAT was an independent predictor of AF recurrence after ablation (p = 0.038). In conclusion, EAT of LA was increased in patients with AF. Large clusters of EAT were observed adjacent to the anterior roof, left atrial appendage, and lateral mitral isthmus. Abundance of EAT was independently related to AF recurrence after ablation.

Characterization of the dynamic function of the pulmonary veins before and after atrial fibrillation ablation using multi-detector computed tomographic images.

Understanding pulmonary vein (PV) function before and after catheter ablation can validate the benefit of the treatment and provide mechanistic insight into atrial fibrillation (AF). This study was aimed to investigate the functional remodeling process of PVs by multi-detector computed tomography (MDCT). We assessed the dynamic function of four PVs by MDCT at systolic and diastolic phases. Twelve points around each PV ostium were used to assess the magnitude and abnormalities of the regional wall motion. The axis of PV shifting during cardiac cycle was also determined. Seventy-four paroxysmal AF patients and 29 controls were enrolled. In those of AF, the superior PVs had poorer contractile function (ejection fraction: P = 0.01 for left; P = 0.009 for right; magnitude of the motion: P = 0.01 for left; P = 0.02 for right) which mainly resulted from the decreased movement of the posterior wall. In contrast, the function of inferior PVs was similar between two groups. After a mean follow-up of 158 ± 95 days, the PV motion improved in the patients without any AF recurrence. In addition, analysis of the pre-ablation PV function showed that the angles, which shifted during cardiac cycle of left (P = 0.035) and right (P = 0.014) inferior PV, were significantly decreased in recurrent patients. The contractile function of the superior PVs was impaired in paroxysmal AF patients. This was attributed to the hypokinesia of the posterior wall of PVs and improved after circumferential ablation in the patients without recurrence. MDCT images can effectively delineate the functional characteristics of PVs.

A novel system for continuous peripheral arterial pressure-volume loop measurement.

This study develops a system to obtain continuous blood pressure signal and impedance plethysmography (IPG) signal, simultaneously. Based on the principle of impedance measurement, the peripheral vessel volume change can be computed from the IPG signal. Equipped with simultaneous information of pressure and volume, a pressure-volume (PV) loop can be constructed. It is well known that the left ventricular pressure-volume loop contains a number of feature points indicating the performance of cardiac function. Therefore, in this study, the same principle is used to try to discuss the peripheral vessel pressure-volume loop. Ten volunteers were recruited for this study. Subjects went through the cold pressor test by immersing their left foot into ice water. Blood pressure signal and impedance changed were recorded using a custom-made system. The results illustrated that the pressure-volume loop, as it was expected, demonstrated a contraction phenomenon after stimulation in five out of ten subjects. The areas of those pressure-volume loops reduced as much as 70% in some subject. However, loop responses to stressors varied from subject to subject and the slope of the loop did not alter significantly. In conclusion, the proposed system is a potential way to measure and to investigate the compliance and characteristic of peripheral blood vessel.

The impact of catheter ablation on the dynamic function of the left atrium in patients with atrial fibrillation: insights from four-dimensional computed tomographic images.

INTRODUCTION: Elucidating the functional properties and remodeling process of the entire left atrium (LA) is important not only for offering the mechanistic insight into atrial fibrillation (AF) but also for assessing the effectiveness of catheter ablation. METHODS: We included 65 patients with paroxysmal AF and 29 controls. Baseline multidetector computed tomography (MDCT) was acquired in all subjects and a follow-up MDCT was available in 48 patients after pulmonary vein and LA ablation. The 3-dimensional images at atrial end-diastole (ED) and end-systole (ES) were analyzed. RESULTS: The LA volume (ED: 61.11 +/- 15.94 vs 54.12 +/- 8.94 mL/m(2), P = 0.03; ES: 45.29 +/- 17.64 vs 33.38 +/- 7.78 mL/m(2), P < 0.001) was increased, and ejection fraction (EF) (26.93 +/- 13.40 vs 38.09 +/- 11.62%, P < 0.001) decreased in AF patients as compared to controls. After ablation, the ES LA volume (44.73 +/- 14.93 vs 38.04 +/- 11.51 mL/m(2), P = 0.04) decreased and the LA EF (25.04 +/- 13.13 vs 30.82 +/- 7.85%, P = 0.03) increased in patients without any AF recurrence. The wall motion (WM) analysis of the 18 segments of LA revealed increased motional magnitudes of entire LA except for the anterior roof. In contrast, the volume, EF, and WM of LA remained similar in patients with recurrence. CONCLUSION: Dilated LA with global hypokinesia was noted in AF patients. Improved LA transport function was demonstrated in patients without any recurrence after ablation. However, the anatomic and functional reverse remodeling was not significant in patients with AF recurrence.

A portable device for real time drowsiness detection using novel active dry electrode system.

Electroencephalogram (EEG) signals give important information about the vigilance states of a subject. Therefore, this study constructs a real-time EEG-based system for detecting a drowsy driver. The proposed system uses a novel six channels active dry electrode system to acquire EEG non-invasively. In addition, it uses a TMS320VC5510 DSP chip as the algorithm processor, and a MSP430F149 chip as a controller to achieve a real-time portable system. This study implements stationary wavelet transform to extract two features of EEG signal: integral of EEG and zero crossings as the input to a back propagation neural network for vigilance states classification. This system can discriminate alertness and drowsiness in real-time. The accuracy of the system is 79.1% for alertness and 90.91% for drowsiness states. When the system detects drowsiness, it will warn drivers by using a vibrator and a beeper.

Evaluation of changes in cardiac output from the electrical impedance waveform in the forearm.

We tested the validity of regional impedance cardiography (RIC) for measuring changes in both cardiac output and stroke volume by comparing the values with a 2D ultrasound technique in response to the breath-hold manipulation. Among 13 subjects, changes in the maximum amplitude of the regional impedance waveform from the forearm conformed to those in stroke volume (r = 0.86, p < 0.001) and cardiac output (r = 0.76, p < 0.003) measured with the ultrasound technique in baseline and immediately after a 30 s breath-hold maneuver. We also found that the per cent change in cardiac output (r = 0.73, p < 0.005) and the per cent change in stroke volume (r = 0.84, p < 0.0003) by the echocardiography were both positively correlated with the per cent change in the peak impedance amplitude. In addition, both the change and the per cent change in the mean area under the impedance curve were consistent with those in the stroke volume, respectively. Accordingly, the regional electrical impedance waveform from lower limbs may be helpful in providing a non-invasive and continuous assessment of left ventricular output, especially during cardiac procedures.

Evaluation of scalp and auricular acupuncture on EEG, HRV, and PRV.

In this study, the EEG, ECG and blood-pressure-pulse recorder were employed to evaluate heart rate variability, pulse rate variability, and EEG of 10 adults after scalp (experimental test I) at Sishencong scalp acupoint and auricular (experimental test II) acupuncture at Shenmen auricular acupoint for about 10 min. Comparison of the results between the experimental tests and a control with no stimulation test showed that both the heart rate and pulse rate were decreased, and the blood pressure fell. The high and low frequency power of FFT analysis of heart rate was increased and decreased, respectively; indicating that the parasympathetic nerves were activated and the sympathetic nerves were inhibited. The analysis of the power spectrum of EEG showed that the number of low frequency waves was increased after acupuncture stimulation. Therefore, acupuncture on either Sishencong or Shenmen might calm the mind, slow down the heart rate, and activate the parasympathetic nerves.