The first aim of this study was to compare an ambulatory five-lead ECG system with the commercially available breast belt measuring devices; Polar S810i and Suunto t6, in terms of R-R interval measures and heart rate variability (HRV) indices. The second aim was to compare different HRV spectral analysis methods. Nineteen young males (aged between 22 and 31 years, median 24 years) underwent simultaneous R-R interval recordings with the three instruments during supine and sitting rest, moderate dynamic, and moderate to vigorous static exercise of the upper and lower limb. For each subject, 17 R-R interval series of 3-min length were extracted from the whole recordings and then analyzed in frequency domain using (1) a fast Fourier transform (FFT), (2) an autoregressive model (AR), (3) a Welch periodogram (WP) and (4) a continuous wavelet transform (CWT). Intra-class correlation coefficients (ICC) and Bland-Altman limits of agreement (LoA) method served as criteria for measurement agreement. Regarding the R-R interval recordings, ICC (lower ICC 95% confidence interval >0.99) as well as LoA (maximum LoA: -15.1 to 14.3 ms for ECG vs. Polar) showed an excellent agreement between all devices. Therefore, the three instruments may be used interchangeably in recording and interpolation of R-R intervals. ICCs for HRV frequency parameters were also high, but in most cases LoA analysis revealed unacceptable discrepancies between the instruments. The agreement among the different frequency transform methods can be taken for granted when analyzing the normalized power in low and high frequency ranges; however, not when analyzing the absolute values.

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Despite their use in cardiac risk stratification, the physiological meaning of nonlinear heart rate variability (HRV) measures is not well understood. The aim of this study was to elucidate effects of breathing frequency, tidal volume, and light exercise on nonlinear HRV and to determine associations with traditional HRV indices. R-R intervals, blood pressure, minute ventilation, breathing frequency, and respiratory gas concentrations were measured in 24 healthy male volunteers during 7 conditions: voluntary breathing at rest, and metronome guided breathing (0.1, 0.2 and 0.4 Hz) during rest, and cycling, respectively. The effect of physical load was significant for heart rate (HR; p < 0.001) and traditional HRV indices SDNN, RMSSD, lnLFP, and lnHFP (p < 0.01 for all). It approached significance for sample entropy (SampEn) and correlation dimension (D2) (p < 0.1 for both), while HRV detrended fluctuation analysis (DFA) measures DFAα1 and DFAα2 were not affected by load condition. Breathing did not affect HR but affected all traditional HRV measures. D2 was not affected by breathing; DFAα1 was moderately affected by breathing; and DFAα2, approximate entropy (ApEn), and SampEn were strongly affected by breathing. DFAα1 was strongly increased, whereas DFAα2, ApEn, and SampEn were decreased by slow breathing. No interaction effect of load and breathing pattern was evident. Correlations to traditional HRV indices were modest (r from -0.14 to -0.67, p < 0.05 to <0.01). In conclusion, while light exercise does not significantly affect short-time HRV nonlinear indices, respiratory activity has to be considered as a potential contributor at rest and during light dynamic exercise.

Future-generation healthcare systems will be highly distributed, combining centralised hospital systems with decentralised homework rk-and environment-based monitoring and diagnostics systems. These will reduce costs and injuryrelated risks whilst both improving quality of service, and reducing the response time for diagnostics and treatments made available to patients. To make this vision possible, medical data must be accessed and shared over a variety of mediums including untrusted networks. In this paper, we present the design and initial implementation of the SERUMS tool-chain for accessing, storing, communicating and analysing highly confidential medical data in a safe, secure and privacypreserving way. In addition, we describe a data fabrication framework for generating large volumes of synthetic but realistic data, that is used in the design and evaluation of the tool-chain. We demonstrate the present version of our technique on a use case derived from the Edinburgh Cancer Centre, NHS Lothian, where information about the effects of chemotherapy treatments on cancer patients is collected from different distributed databases, analysed and adapted to improve ongoing treatments.

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