A highly localized data-association measure, termed intrinsic synchrosqueezing transform (ISC), is proposed for the analysis of coupled nonlinear and non-stationary multivariate signals. This is achieved based on a combination of noise-assisted multivariate empirical mode decomposition and short-time Fourier transform-based univariate and multivariate synchrosqueezing transforms. It is shown that the ISC outperforms six other combinations of algorithms in estimating degrees of synchrony in synthetic linear and nonlinear bivariate signals. Its advantage is further illustrated in the precise identification of the synchronized respiratory and heart rate variability frequencies among a subset of bass singers of a professional choir, where it distinctly exhibits better performance than the continuous wavelet transform-based ISC. We also introduce an extension to the intrinsic phase synchrony (IPS) measure, referred to as nested intrinsic phase synchrony (N-IPS), for the empirical quantification of physically meaningful and straightforward-to-interpret trends in phase synchrony. The N-IPS is employed to reveal physically meaningful variations in the levels of cooperation in choir singing and performing a surgical procedure. Both the proposed techniques successfully reveal degrees of synchronization of the physiological signals in two different aspects: (i) precise localization of synchrony in time and frequency (ISC), and (ii) large-scale analysis for the empirical quantification of physically meaningful trends in synchrony (N-IPS).

Varicose vein surgeries are routine outpatient procedures, which are often performed under local anaesthesia. The use of local anaesthesia both minimises the risk to patients and is cost effective, however, a number of patients still experience pain during surgery. Surgical teams must therefore decide to administer either a general or local anaesthetic based on their subjective qualitative assessment of patient anxiety and sensitivity to pain, without any means to objectively validate their decision. To this end, we develop a 3-D polynomial surface fit, of physiological metrics and numerical pain ratings from patients, in order to model the link between the modulation of cardiovascular responses and pain in varicose vein surgeries. Spectral and structural complexity features found in heart rate variability signals, recorded immediately prior to 17 varicose vein surgeries, are used as pain metrics. The so obtained pain prediction model is validated through a leave-one-out validation, and achieved a Kappa coefficient of 0.72 (substantial agreement) and an area below a receiver operating characteristic curve of 0.97 (almost perfect accuracy). This proof-of-concept study conclusively demonstrates the feasibility of the accurate classification of pain sensitivity, and introduces a mathematical model to aid clinicians in the objective administration of the safest and most cost-effective anaesthetic to individual patients.

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We aimed to train podiatrists to perform a focused duplex ultrasound scan (DUS) of the tibial vessels at the ankle in diabetic patients; podiatry ankle (PodAnk) duplex scan. Thirteen podiatrists underwent an intensive 3-hour long simulation training session. Participants were then assessed performing bilateral PodAnk duplex scans of 3 diabetic patients with peripheral arterial disease. Participants were assessed using the duplex ultrasound objective structured assessment of technical skills (DUOSATS) tool and an "Imaging Score". A total of 156 vessel assessments were performed. All patients had abnormal waveforms with a loss of triphasic flow. Loss of triphasic flow was accurately detected in 145 (92.9%) vessels; the correct waveform was identified in 139 (89.1%) cases. Participants achieved excellent DUOSATS scores (median 24 [interquartile range: 23-25], max attainable score of 26) as well as "Imaging Scores" (8 [8-8], max attainable score of 8) indicating proficiency in technical skills. The mean time taken for each bilateral ankle assessment was 20.4 minutes (standard deviation ±6.7). We have demonstrated that a focused DUS for the purpose of vascular assessment of the diabetic foot is readily learned using intensive simulation training.

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