Table of contents

This paper presents a new family of indices for the frequency domain analysis of heart rate variability time series that do not need any frequency band definition. After proper detrending of the time series, a cumulated power spectrum is obtained and frequencies that contain a certain percentage of the power below them are identified, so median frequency, bandwidth and a measure of the power spectrum asymmetry are proposed to complement or improve the classical spectral indices as the ratio of the powers of LF and HF bands (LF/HF). In normal conditions the median frequency provides similar information as the classical indices, while the bandwidth and asymmetry can be complementary measures of the physiological state of the tested subject. The proposed indices seem to be a good choice for tracking changes in the power spectrum in exercise stress, and they can guide in the determination of frequency band limits in other animal species.

A family of new heart rate asymmetry measures is introduced, namely deceleration and acceleration runs, as well as entropic measures summarizing their distribution. We introduce the theoretical run distribution for shuffled data and use it as a reference for interpreting the results. The measures defined in the paper are applied to actual 24 h Holter ECG recordings from 87 healthy people, and it is demonstrated that the patterns of accelerations are different from those of decelerations. Acceleration runs are longer and more numerous: all runs of accelerations, with the exception of lengths 3 and 4, are more numerous than those of decelerations. These findings are reflected in the difference between the entropic measures for acceleration and deceleration runs: for 74 subjects the acceleration-related entropic parameter is greater than that of decelerations (p < 0.001). For shuffled data there is no difference in the above parameters, and there are more short runs and fewer long runs than in physiological data. The influence of the measuring equipment resolution is also discussed.

Interpretation of peripheral circulation in ill neonates is crucial but difficult. The aim was to analyse parameters potentially influencing peripheral oxygenation and circulation. In a prospective observational cohort study in 116 cardio-circulatory stable neonates, peripheral muscle near-infrared spectroscopy (NIRS) with venous occlusion was performed. Tissue oxygenation index (TOI), mixed venous oxygenation (SvO₂), fractional oxygen extraction (FOE), fractional tissue oxygen extraction (FTOE), haemoglobin flow (Hbflow), oxygen delivery (DO₂), oxygen consumption (VO₂), and vascular resistance (VR) were assessed. Correlation coefficients between NIRS parameters and demographic parameters (gestational age, birth weight, age, actual weight, diameter of calf, subcutaneous adipose tissue), monitoring parameters (heart rate, arterial oxygen saturation (SaO₂), mean blood pressure (MAP), core/peripheral temperature, central/peripheral capillary refill time) and laboratory parameters (haemoglobin concentration (Hb-blood), pCO₂) were calculated. All demographic parameters except for Hbflow and DO₂ correlated with NIRS parameters. Heart rate correlated with TOI, SvO₂, VO₂ and VR. SaO₂ correlated with FOE/FTOE. MAP correlated with Hbflow, DO₂, VO₂ and VR. Core temperature correlated with FTOE. Peripheral temperature correlated with all NIRS parameters except VO₂. Hb-blood correlated with FOE and VR. pCO₂ levels correlated with TOI and SvO₂. The presence of multiple interdependent factors associated with peripheral oxygenation and circulation highlights the difficulty in interpreting NIRS data. Nevertheless, these findings have to be taken into account when analysing peripheral oxygenation and circulation data.

We propose a dynamical model for mean inlet pressure estimation in an implantable rotary blood pump during the diastolic period. Non-invasive measurements of pump impeller rotational speed (ω), motor power (P), and pulse width modulation signal acquired from the pump controller were used as inputs to the model. The model was validated over a wide range of speed ramp studies, including (i) healthy (C1), variations in (ii) heart contractility (C2); (iii) afterload (C2, C3, C4), and (iv) preload (C5, C6, C7). Linear regression analysis between estimated and extracted mean inlet pressure obtained from in vivo animal data (greyhound dogs, N = 3) resulted in a highly significant correlation coefficients (R² = 0.957, 0.961, 0.958, 0.963, 0.940, 0.946, and 0.959) and mean absolute errors of (e = 1.604, 2.688, 3.667, 3.990, 2.791, 3.215, and 3.225 mmHg) during C1, C2, C3, C4, C5, C6, and C7, respectively. The proposed model was also used to design a controller to regulate mean diastolic pump inlet pressure using non-invasively measured ω and P. In the presence of model uncertainty, the controller was able to track and settle to the desired input within a finite number of sampling periods and minimal error (0.92 mmHg). The model developed herein will play a crucial role in developing a robust control system of the pump that detects and thus avoids undesired pumping states by regulating the inlet pressure within a predefined physiologically realistic limit.

This study presents a retunable surface coil that can be adjusted to at least two Larmor frequencies sequentially without the need to remove the coil from the magnet and while avoiding interference between channels. A prototype ¹H/³¹P surface coil for the analysis of the in vivo mouse leg under electrical stimulation was designed for operation at 11.75 T. The coil has a high-quality factor of over 100 for both operational frequencies. To demonstrate the capabilities of this simple design, in vivo experiments were conducted to acquire high-resolution ¹H images and ³¹P spectra of the C57BL/6 mouse leg, both with high temporal resolution. Proton diffusion tensor imaging was also performed to evaluate rodent skeletal muscle architecture. This design makes the acquisition of physiological data about both muscle structure and energetics (PCr, ATP and Pi) possible in a single experimental session.

This work investigates the relation between the complexity of electroencephalography (EEG) signal, as measured by fractal dimension (FD), and normal sleep structure in terms of its macrostructure and microstructure. Sleep features are defined, encoding sleep stage and cyclic alternating pattern (CAP) related information, both in short and long term. The relevance of each sleep feature to the EEG FD is investigated, and the most informative ones are depicted. In order to quantitatively assess the relation between sleep characteristics and EEG dynamics, a modeling approach is proposed which employs subsets of the sleep macrostructure and microstructure features as input variables and predicts EEG FD based on these features of sleep micro/macrostructure. Different sleep feature sets are investigated along with linear and nonlinear models. Findings suggest that the EEG FD time series is best predicted by a nonlinear support vector machine (SVM) model, employing both sleep stage/transitions and CAP features at different time scales depending on the EEG activation subtype. This combination of features suggests that short-term and long-term history of macro and micro sleep events interact in a complex manner toward generating the dynamics of sleep.

Functional neural gastrointestinal electrical stimulation (NGES) is a methodology of gastric electrical stimulation that can be applied as a possible treatment for disorders such as obesity and gastroparesis. NGES is capable of generating strong lumen-occluding local contractions that can produce retrograde or antegrade movement of gastric content. A feedback-controlled implantable NGES system has been designed, implemented and tested both in laboratory conditions and in an acute animal setting. The feedback system, based on gastric tissue impedance change, is aimed at reducing battery energy requirements and managing the phenomenon of gastric tissue accommodation. Acute animal testing was undertaken in four mongrel dogs (2 M, 2 F, weight 25.53 ± 7.3 kg) that underwent subserosal two-channel electrode implantation. Three force transducers sutured serosally along the gastric axis and a wireless signal acquisition system were utilized to record stimulation-generated contractions and tissue impedance variations respectively. Mechanically induced contractions in the stomach were utilized to indirectly generate a tissue impedance change that was detected by the feedback system. Results showed that increasing or decreasing impedance changes were detected by the implantable stimulator and that therapy can be triggered as a result. The implantable feedback system brings NGES one step closer to long term treatment of burdening gastric motility disorders in humans.

Systemic vascular resistance (SVR) classification is useful for the diagnosis and prognosis of critical pathophysiological conditions, with the ability to identify patients with abnormally high or low SVR of immense clinical value. In this study, a supervised classifier, based on Bayes' rule, is employed to classify a heterogeneous group of intensive care unit patients (N = 48) as being below (SVR < 900 dyn s cm⁻⁵), within (900 ⩽ SVR ⩽ 1200 dyn s cm⁻⁵) or above (SVR > 1200 dyn s cm⁻⁵) the clinically accepted range for normal SVR. Features derived from the finger photoplethysmogram (PPG) waveform and other routine cardiovascular measurements (heart rate and mean arterial pressure) were used as inputs to the classifier. In the construction of the classifier model, two techniques were used to approximate the class conditional probability densities-–a single Gaussian distribution model (also known as discriminant analysis) and a non-parametric model using the Parzen window kernel density estimation method. An exhaustive feature search was performed to select a feature subset that maximized the performance indicator, Cohen's kappa coefficient (κ). The Gaussian model with multiple features achieved the best overall kappa coefficient (κ = 0.57), although the results from the non-parametric model were comparable (κ = 0.51). The optimum subset in the Gaussian model consisted of PPG waveform variability features, including the low-frequency to high-frequency ratio (LF/HF) and the normalized mid-frequency power (MF_NU), in addition to the PPG pulse wave features, such as pulse width, peak-to-notch time, reflection index, and notch time ratio. The classifier performed particularly well in discriminating low SVR, with a sensitivity of 85%, specificity of 86%, positive predictive value of 88% and a negative predictive value of 82%. The results highlight the feasibility of deploying a multivariate statistical approach of SVR classification in the clinical setting, simply using a non-invasive and easy-to-measure PPG waveform signal.

The purpose of this study was to examine the effect different cycling cadences have on heart rate variability (HRV) when exercising at constant power outputs. Sixteen males had ECG and respiratory measurements recorded at rest and during 8, 10 min periods of cycling at four different cadences (40, 60, 80 and 100 revs min⁻¹) and two power outputs (0 W (unloaded) and 100 W (loaded)). The cycling periods were performed following a Latin square design. Spectral analyses of R–R intervals by fast Fourier transforms were used to quantify absolute frequency domain HRV indices (ms²) during the final 5 min of each bout, which were then log transformed using the natural logarithm (Ln). HRV indices of high frequency (HF) power were reduced when cadence was increased (during unloaded cycling (0 W) log transformed HF power decreased from a mean [SD] of 6.3 [1.4] Ln ms² at 40 revs min⁻¹ to 3.9 [1.3] Ln ms² at 100 revs min⁻¹). During loaded cycling (at 100 W), the low to high frequency (LF:HF) ratio formed a 'J' shaped curve as cadence increased from 40 revs min⁻¹ (1.4 [0.4]) to 100 revs min⁻¹ (1.9 [0.7]), but dipped below the 40 revs min⁻¹ values during the 60 revs min⁻¹ 1.1 (0.3) and 80 revs min⁻¹ 1.2 (0.6) cadence conditions. Cardiac frequency (f_C) and ventilatory variables were strongly correlated with frequency domain HRV indices (r = −0.80 to −0.95). It is concluded that HRV indices are influenced by both cycling cadence and power output; this is mediated by the f_C and ventilatory changes that occur as cadence or exercise intensity is increased. Consequently, if HRV is assessed during exercise, both power output/exercise intensity and cadence should be standardized.

Atrial fibrillation (AF) is the most common cardiac arrhythmia and increases the risk of stroke. Predicting the onset of paroxysmal AF (PAF), based on noninvasive techniques, is clinically important and can be invaluable in order to avoid useless therapeutic intervention and to minimize risks for the patients. In this paper, we propose an effective PAF predictor which is based on the analysis of the RR-interval signal. This method consists of three steps: preprocessing, feature extraction and classification. In the first step, the QRS complexes are detected from the electrocardiogram (ECG) signal and then the RR-interval signal is extracted. In the next step, the recurrence plot (RP) of the RR-interval signal is obtained and five statistically significant features are extracted to characterize the basic patterns of the RP. These features consist of the recurrence rate, length of longest diagonal segments (L_max), average length of the diagonal lines (L_mean), entropy, and trapping time. Recurrence quantification analysis can reveal subtle aspects of dynamics not easily appreciated by other methods and exhibits characteristic patterns which are caused by the typical dynamical behavior. In the final step, a support vector machine (SVM)-based classifier is used for PAF prediction. The performance of the proposed method in prediction of PAF episodes was evaluated using the Atrial Fibrillation Prediction Database (AFPDB) which consists of both 30 min ECG recordings that end just prior to the onset of PAF and segments at least 45 min distant from any PAF events. The obtained sensitivity, specificity, positive predictivity and negative predictivity were 97%, 100%, 100%, and 96%, respectively. The proposed methodology presents better results than other existing approaches.

This study assesses the connectivity alterations caused by Alzheimer's disease (AD) and mild cognitive impairment (MCI) in magnetoencephalogram (MEG) background activity. Moreover, a novel methodology to adaptively extract brain rhythms from the MEG is introduced. This methodology relies on the ability of empirical mode decomposition to isolate local signal oscillations and constrained blind source separation to extract the activity that jointly represents a subset of channels. Inter-regional MEG connectivity was analysed for 36 AD, 18 MCI and 26 control subjects in δ, θ, α and β bands over left and right central, anterior, lateral and posterior regions with magnitude squared coherence—c(f). For the sake of comparison, c(f) was calculated from the original MEG channels and from the adaptively extracted rhythms. The results indicated that AD and MCI cause slight alterations in the MEG connectivity. Computed from the extracted rhythms, c(f) distinguished AD and MCI subjects from controls with 69.4% and 77.3% accuracies, respectively, in a full leave-one-out cross-validation evaluation. These values were higher than those obtained without the proposed extraction methodology.

There is a need for robust techniques for early and accurate diagnosis of acute coronary syndromes (ACSs), to avoid inappropriate discharge of patients. This study examined the use of frequency spectrum analysis of heart rate variability (HRV) and photoplethysmogram (PPG) waveform variability for the identification of high-risk ACS patients defined by an elevated cardiac troponin level. The study cohort comprised a convenience sample of adult patients presenting to the emergency department of the Prince of Wales Hospital over a 4 month period complaining of non-traumatic chest pain. Valid electrocardiogram (ECG) and earlobe PPG waveforms together with troponin I test results were obtained from 52 patients at presentation, 4 of which were troponin I positive (Trop 0+). Frequency spectrum analysis was performed on the beat-to-beat HRV and PPG waveform variability (PPGV). The Trop 0+ were found to have significantly higher normalized mid-frequency power (MF_nu) in HRV (P = 0.017), PPG amplitude variability (P = 0.009) and the cross-spectrum of HRV and PPGV (P = 0.001), which were attributed to reflex sympathetic response to myocardial ischemia. MF_nu of PPG amplitude had the best overall performance in detecting Trop 0+, with ROC area under the curve of 0.93. The results demonstrate the potential use of ear PPG waveform to identify high-risk heart disease patients, and further highlight the utility of frequency spectrum analysis of PPGV in critical care.

Baroreflex sensitivity (BRS) is known to be attenuated by inspiration and all the original BRS methodologies took this into account by measuring only in expiration. Spontaneous sequence analysis (SSA) is a non-invasive clinical tool widely used to estimate BRS in Man but does not take breathing into account. We have therefore modified it to test whether it too can detect inspiratory attenuation. Traditional SSA is also entangled with issues of distinguishing causal from random relationships between blood pressure and heart period and of the optimum choice of data filter settings. We have also tested whether the sequences our modified SSA rejects do behave as random relationships and show the limitations of the absence of filter standardization. SSA was performed on eupneic data from 1 h periods in 20 healthy subjects. Applying SSA traditionally produced a mean BRS of 23 ± 3 ms mmHg⁻¹. After modification to measure breathing, SSA detected significant inspiratory attenuation (11 ± 1 ms mmHg⁻¹), and the mean expiratory BRS was significantly higher (26 ± 5 ms mmHg⁻¹). Traditional SSA therefore underestimates BRS by an amount (3 ms mmHg⁻¹) as big as the major physiological and clinical factors known to alter BRS. We show that the sequences rejected by SSA do behave like random associations between pressure and period. We also show the minimal effect of the r² filter and the biases that some pressure and heart period filters can introduce. We discuss whether SSA might be improved by standardization of filter settings and by also measuring breathing.

One of the important factors in blood pressure regulation is the maintenance of the level of blood volume, which depends on several factors including the rate of lymph flow. Lymph flow can be measured directly using cannulation of lymphatic vessels, which is not clinically feasible, or indirectly by the tracer appearance rate, which is the rate at which macromolecules appear into the blood from the peritoneal cavity. However, indirect lymph flow measurements do not always provide consistent results. Through its contribution to osmotic pressure and resistance to flow, the macromolecule hyaluronan takes part in the regulation of tissue hydration and the maintenance of water and protein homeostasis. It arrives in blood plasma through lymph flow. Lymphatic hyaluronic acid (HA, hyaluronan) concentration is several times higher than that in plasma, suggesting that the lymphatic route may account for the majority of HA found in plasma. Furthermore, circulating levels of HA reflect the dynamic state between delivery to—and removal from—the bloodstream. To develop an accurate estimation of the fluid volume distribution and dynamics, the rate of lymph flow needs to be taken into account and hyaluronan could be used as a marker in estimating this flow. To examine the HA distribution and system fluid dynamics, a six-compartment model, which could reflect both the steady-state relationships and qualitative characteristics of the dynamics, was developed. This was then applied to estimate fluid shifts from the interstitial space via the lymphatic system to the plasma during different physiological stresses (orthostatic stress and the stress of ultrafiltration during dialysis). Sensitivity analysis shows that during ultrafiltration, lymph flow is a key parameter influencing the total HA level, thus suggesting that the model may find applications in addressing the problem of estimating lymph flow. Since the fluid balance between interstitium and plasma is maintained by lymph flow and microvasculature filtration, our novel method of flow estimation may provide an important tool for understanding fluid dynamics during perturbations of the cardiovascular system. Since the fluid balance between interstitium and plasma is maintained by lymph flow and microvasculature filtration, our novel method of flow estimation may provide an important tool for understanding fluid dynamics during perturbations of the cardiovascular system.

We introduced a novel non-constrained technique for estimating heart rate variability (HRV) using a ballistocardiogram (BCG). To assess whether the BCG signal can be used to analyse the cardiac autonomic modulation, HRV parameters derived from the BCG signal (ballistocardiographic HRV, B-HRV) were statistically compared with the HRV parameters from the ECG signal during rest and under two different experimental conditions that induce cardiac autonomic rhythm changes: the Valsalva manoeuvre and static exercise. Time domain, frequency domain and nonlinear analyses were individually performed on 15 healthy subjects to assess whether the BCG can be used to analyse the cardiac autonomic modulation under each condition. For all subjects, the proposed method had averages of relative errors of 5.01 ± 4.72, 5.64 ± 4.83 and 5.98 ± 5.80% for resting, Valsalva and post-exercise sessions, respectively, and the correlation coefficients between the reference (ECG) and proposed (BCG) methods are 0.97, 0.98 and 0.98, for resting, Valsalva and post-exercise sessions, respectively. During cardiac autonomic changes, the B-HRV parameters changed in a pattern that is very similar to the variations in the HRV parameters based on Student's t-test results. In addition, some of the B-HRV parameters changed according to cardiac autonomic rhythms controlled by sympathetic and parasympathetic activities during the experiments. These findings indicate that BCG can provide an accurate and reliable means to evaluate autonomic system activation by HRV in its unconstrained way.

An analytical formalism developed previously to examine the robustness of the isovolume calibration technique for non-invasive respiratory monitoring devices based on measurements of torso circumference (e.g. fibre-optic respiratory plethysmography) is extended here to techniques based on area measurement (e.g. respiratory inductive plethysmography), and the results are compared. The earlier perturbation approach is adopted, and an exact method is also presented. It is demonstrated that the area-based techniques have less dependence on the cylindrical compartmental parameters of radius and height, and are independent of compartmental volume if height variations are negligible, in contrast to circumference-based techniques. It is also demonstrated that both the area- and the circumference-based techniques provide similar inferences of volume when calibrated using the isovolume method under reasonable assumptions for the dimensions of the compartments that constitute a model of the torso.

We report the first measured values of conductivities for neonatal mammalian skull samples. We measured the average radial (normal to the skull surface) conductivity of fresh neonatal piglet skull samples at 1 kHz and found it to be around 30 mS m⁻¹ at ambient room temperatures of about 23 °C. Measurements were made on samples of either frontal or parietal cranial bone, using a saline-filled cell technique. The conductivity value we observed was approximately twice the values reported for adult skulls (Oostendorp et al 2000 IEEE Trans. Biomed. Eng.47 1487–92) using a similar technique, but at a frequency of around 5 Hz. Further, we found that the conductivity of skull fragments increased linearly with thickness. We found evidence that this was related to differences in composition between the frontal and parietal bone samples tested, which we believe is because frontal bones contained a larger fraction of higher conductivity cancellous bone material.

A fringing field capacitive sensor has been used to measure the dielectric properties of human skin and underlying tissue in the MHz frequency range. It has recently been shown in clinical experimental studies that these dielectric properties can be related to the effects of in vivo glucose variations of the test subject. Previously, the relationship between electrical impedance and the glucose level has been established via statistical methods, such as the regression method. In this work, we explored a different approach, namely the resolution of the so-called inverse problem. First we applied the method on an artificial two-layer lossy system in order to test the sensitivity of the solution to forced changes in the layer properties and its stability to a constant setting. After validation of this method on artificial systems, a similar inverse problem was set and solved for dielectric measurements on human skin during an induced glucose excursion, where the skin is also modelled as a double-layer system. The changes of the measured permittivity and conductivity of the second layer versus the glucose changes are calculated for 22 study days. The statistical distribution shows that the median slopes of both dielectric properties are negative. These results can be used to test our hypothesis and to continue building potential explanations for the phenomena induced by the glucose changes on the skin layer dielectric parameters.

Ultrasound has been widely used to nondestructively evaluate various materials, including biological tissues. Quantitative ultrasound has been used to assess bone quality and fracture risk. A pulsed phase-locked loop (PPLL) method has been proven for very sensitive tracking of ultrasound time-of-flight (TOF) changes. The objective of this work was to determine if the PPLL TOF tracking is sensitive to bone deformation changes during loading. The ability to noninvasively detect bone deformations has many implications, including assessment of bone strength and more accurate osteoporosis diagnostics and fracture risk prediction using a measure of bone mechanical quality. Fresh sheep femur cortical bone shell samples were instrumented with three 3-element rosette strain gauges and then tested under mechanical compression with eight loading levels using an MTS machine. Samples were divided into two groups based on internal marrow cavity content: with original marrow, or replaced with water. During compressive loading ultrasound waves were measured through acoustic transmission across the mid-diaphysis of bone. Finite element analysis (FEA) was used to describe ultrasound propagation path length changes under loading based on µCT-determined bone geometry. The results indicated that PPLL output correlates well to measured axial strain, with R² values of 0.70 ± 0.27 and 0.62 ± 0.29 for the marrow and water groups, respectively. The PPLL output correlates better with the ultrasound path length changes extracted from FEA. For the two validated FEA tests, correlation was improved to R² = 0.993 and R² = 0.879 through cortical path, from 0.815 and 0.794 via marrow path, respectively. This study shows that PPLL readings are sensitive to displacement changes during external bone loading, which may have potential to noninvasively assess bone strain and tissue mechanical properties.

Portable tensiomyography (TMG) and myotonometry (MMT) devices have been developed to measure mechanical and contractile properties of skeletal muscle. The aim of this study was to explore the sensitivity of the aforementioned techniques in detecting a change in passive mechanical properties of the biceps femoris (BF) muscle as a result of change in knee joint angle (i.e. muscle length). BF responses were assessed in 16 young participants (23.4 ± 4.9 years), at three knee joint angles (0°, 45° and 90°), for maximal isometric torque (MIT) along with myo-electrical activity. Contractile and mechanical properties were measured in a relaxed state. Inter-day reliability of the TMG and MMT was also assessed. MIT changed significantly (p < 0.01) across the three angles, so did stiffness and other parameters measured with MMT (p < 0.01). Conversely, TMG could detect changes only at two knee angles (0° and 45°, p < 0.01), when there is enough tension in the muscle. Reliability was overall insufficient for TMG whilst absolute reliability was excellent (coefficient of variation < 5%) for MMT. The ability of MMT more than TMG to detect an inherent change in stiffness can be conceivably exploited in a number of clinical/therapeutic applications that have to do with unnatural changes in passive muscle stiffness.

Event-related brain potentials (ERPs) are the electrical response of the brain while performing a particular task. Methods traditionally used to study ERPs measure the amplitude and duration of the waveform in order to quantify the changes, being signal morphology dependent. However, the frequency characteristics of those events remain uncovered. The aim of this work was the study of new measures to characterize, by means of time–frequency representation (TFR) techniques, the ERPs recorded while subjects conducted a choice reaction time task (Ericksen flanker task) following the administration of different alprazolam doses. Several measures defined from energy, instantaneous frequency and group delay functions were obtained by means of TFR techniques applied to the Choi–Williams distribution (CWD) of EEG signals. These measures, which are signal morphology independent, were studied in four frequency bands, δ (0–4 Hz), θ (4–8 Hz), α (8–15 Hz), β (15–30 Hz), and for certain time periods. Based on these measures, differences between ERPs were analyzed by comparing the different response types (successes or successfully corrected failures) of the subject performing the task, and comparing the applied drug doses. For each subject, the CWD of EEG signals was applied in two different ways: (a) all ERPs were averaged per channel, and then the CWD was applied; (b) the CWD was applied to each one of the ERPs. When the CWD was applied to each ERP, the energy measures in the δ, θ and β bands, the instantaneous frequency measures in the α and β bands, and the group delay measures in the δ, θ and α bands showed a statistically significant level p < 0.0005 in the analysis of the response type. Also, the energy measures in the θ and β bands and the instantaneous frequency measures in the α band showed statistically significant differences (p < 0.0005) between placebo and low and high drug doses. In contrast, poor results were obtained when all epochs of each subject were averaged per channel. Finally, it was concluded that these results showed that the new proposed measures based on the energy offered a new and more robust way to characterize ERP signals.

Cardiotocography is the monitoring of fetal heart rate (FHR) and uterine contractions (TOCO), used routinely since the 1960s by obstetricians to detect fetal hypoxia. The evaluation of the FHR in clinical settings is based on an evaluation of macroscopic morphological features and so far has managed to avoid adopting any achievements from the HRV research field. In this work, most of the features utilized for FHR characterization, including FIGO, HRV, nonlinear, wavelet, and time and frequency domain features, are investigated and assessed based on their statistical significance in the task of distinguishing the FHR into three FIGO classes. We assess the features on a large data set (552 records) and unlike in other published papers we use three-class expert evaluation of the records instead of the pH values. We conclude the paper by presenting the best uncorrelated features and their individual rank of importance according to the meta-analysis of three different ranking methods. The number of accelerations and decelerations, interval index, as well as Lempel–Ziv complexity and Higuchi's fractal dimension are among the top five features.

Previous measurements of acetone concentrations in the exhaled breath of healthy individuals and the small amount of comparable data for individuals suffering from diabetes are briefly reviewed as a prelude to the presentation of new data on the sporadic and wide variations of breath acetone that occur in ostensibly healthy individuals. Data are also presented which show that following a ketogenic diet taken by eight healthy individuals their breath acetone concentrations increased up to five times over the subsequent 6 h. Similarly, the breath acetone increased six and nine times when a low carbohydrate diet was taken by two volunteers and remained high for the several days for which the diet was continued. These new data, together with the previous data, clearly indicate that diet and natural intra-individual biological and diurnal variability result in wide variations in breath acetone concentration. This places an uncertainty in the use of breath acetone alone to monitor blood glucose and glycaemic control, except and unless the individual acts as their own control and is cognizant of the need for dietary control.