Estimation of sleep recovery in shift working long-haul truck drivers – A heart rate variability based study

Prolonged work hours, shortened and irregular sleep patterns often leads to inadequate recovery in shift workers resulting in increased sleepiness or fatigue during the day. Heart rate and heart rate variability (HRV) have been often used in occupational health studies to examine sleep quality and recovery. The aim of the current study was to determine the factors affecting the recovery process in shift working long-haul truck drivers and to as-sess the impact different shifts have on the drivers’ sleep health. Of the recruited volunteers, data collected from 38 volunteers (Age: 38.46 ± 10.89 years) satisfied the inclusion criteria for this study. Driver demographics and background questionnaires were obtained prior to measurements. R-R intervals and actigraphy data were collected for three intensive measurement days (non-night shift, night shift and lei-sure day) and subjective measures of sleep quality, recorded on the sleep-diary, were used for the analyses. Several time- and frequency-domain HRV indices were calculated in 10-minute segments and averaged on an hourly basis and for the entire duration of sleep. All tests for statistical significance was conducted on a within-subject basis. Comparison of HRV indices over the entire sleep duration recorded on different in-tensive measurement days revealed no significant differences except for LF/HF ratio (Lei-sure day vs. Night shift, p <0.05). Sleep duration and efficiency were significantly lower on duty days. Regression analyses indicated VLF power was strong predictor of recovery and 31% of the outcome was influenced by explanatory factors. SDNN (r = 0.555, ad-justed r2 = 0.248, F(9, 92) = 5.166, p <0.001), RMSSD (r = 0.414, adjusted r2 = 0.131, F(9.92) = 4.229, p <0.05) and HF power (r = 0.460, adjusted r2 = 0.165, F(9.92) = 4.526, p <0.001) were significantly associated with age and sleep duration. Short-term variabil-ity indices, RMSSD and HF power, were moderately influenced by diurnal variations. The results suggest that despite the fact that shift type does not have any direct con-sequences on sleep recovery, the odd work hours and irregular sleep schedules pose an indirect effect. The truncated sleep length, especially seen after night shift work, have been significantly associated with the impaired recovery and is contributed to by other short-term (diurnal variations) and long-term (ageing) factors. These results provide a basis for planning shift schedules such that direct or indirect manifestations of shift type-related influence on recovery are mitigated

Unsupervised Heart-rate Estimation in Wearables With Liquid States and A Probabilistic Readout

Heart-rate estimation is a fundamental feature of modern wearable devices. In this paper we propose a machine intelligent approach for heart-rate estimation from electrocardiogram (ECG) data collected using wearable devices. The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization. Our approach differs from existing works by learning directly from ECG signals (allowing personalization), without requiring costly data annotations. Additionally, our approach can be easily implemented on state-of-the-art spiking-based neuromorphic systems, offering high accuracy, yet significantly low energy footprint, leading to an extended battery life of wearable devices. We validated our approach with CARLsim, a GPU accelerated spiking neural network simulator modeling Izhikevich spiking neurons with Spike Timing Dependent Plasticity (STDP) and homeostatic scaling. A range of subjects are considered from in-house clinical trials and public ECG databases. Results show high accuracy and low energy footprint in heart-rate estimation across subjects with and without cardiac irregularities, signifying the strong potential of this approach to be integrated in future wearable devices.Comment: 51 pages, 12 figures, 6 tables, 95 references. Under submission at Elsevier Neural Network

Effect of heart rate correction on pre- and post-exercise heart rate variability to predict risk of mortality - an experimental study on the FINCAVAS cohort

Peer reviewe

Sleepiness and stress among long-haul truck drivers : An educational intervention to promote safe and economic truck driving

Sleepiness and stress at the wheel are known to be common among professional drivers. Given the safety-sensitive nature of the job, it would be essential for neither of these conditions to reach levels compromising safe driving. The current field study examined the levels of sleepiness and stress at the wheel in a group of Finnish long-haul truck drivers, and the potential factors contributing to the sub-optimal levels of arousal. Over and above, the study examined whether driver alertness could be amended by short one-time alertness management training. The results revealed that driver sleepiness reaches potentially risky levels, especially during the first night shift in the beginning of a shift spell. No clear evidence was found to support the idea that educating professional drivers on alertness management would be sufficient for mitigating their sleepiness on the road

Cardiomyocytes from human pluripotent stem cells: from laboratory curiosity to industrial biomedical platform

Cardiomyocytes from human pluripotent stem cells (hPSCs-CMs) could revolutionise biomedicine. Global burden of heart failure will soon reach USD $90bn, while unexpected cardiotoxicity underlies 28% of drug withdrawals. Advances in hPSC isolation, Cas9/CRISPR genome engineering and hPSC-CM differentiation have improved patient care, progressed drugs to clinic and opened a new era in safety pharmacology. Nevertheless, predictive cardiotoxicity using hPSC-CMs contrasts from failure to almost total success. Since this likely relates to cell immaturity, efforts are underway to use biochemical and biophysical cues to improve many of the ~ 30 structural and functional properties of hPSC-CMs towards those seen in adult CMs. Other developments needed for widespread hPSC-CM utility include subtype specification, cost reduction of large scale differentiation and elimination of the phenotyping bottleneck. This review will consider these factors in the evolution of hPSC-CM technologies, as well as their integration into high content industrial platforms that assess structure, mitochondrial function, electrophysiology, calcium transients and contractility. This article is part of a Special Issue entitled: Cardiomyocyte Biology: Integration of Developmental and Environmental Cues in the Heart edited by Marcus Schaub and Hughes Abriel

Cardiomyocyte MEA Data Analysis (CardioMDA) – A Novel Field Potential Data Analysis Software for Pluripotent Stem Cell Derived Cardiomyocytes

Cardiac safety pharmacology requires in-vitro testing of all drug candidates before clinical trials in order to ensure they are screened for cardio-toxic effects which may result in severe arrhythmias. Micro-electrode arrays (MEA) serve as a complement to current in-vitro methods for drug safety testing. However, MEA recordings produce huge volumes of data and manual analysis forms a bottleneck for high-throughput screening. To overcome this issue, we have developed an offline, semi-automatic data analysis software, ‘Cardiomyocyte MEA Data Analysis (CardioMDA)’, equipped with correlation analysis and ensemble averaging techniques to improve the accuracy, reliability and throughput rate of analysing human pluripotent stem cell derived cardiomyocyte (CM) field potentials. With the program, true field potential and arrhythmogenic complexes can be distinguished from one another. The averaged field potential complexes, analysed using our software to determine the field potential duration, were compared with the analogous values obtained from manual analysis. The reliability of the correlation analysis algorithm, evaluated using various arrhythmogenic and morphology changing signals, revealed a mean sensitivity and specificity of 99.27% and 94.49% respectively, in determining true field potential complexes. The field potential duration of the averaged waveforms corresponded well to the manually analysed data, thus demonstrating the reliability of the software. The software has also the capability to create overlay plots for signals recorded under different drug concentrations in order to visualize and compare the magnitude of response on different ion channels as a result of drug treatment. Our novel field potential analysis platform will facilitate the analysis of CM MEA signals in semi-automated way and provide a reliable means of efficient and swift analysis for cardiomyocyte drug or disease model studies.Public Library of Science open acces

The Role of Features Types and Personalized Assessment in Detecting Affective State Using Dry Electrode EEG

Assessing the human affective state using electroencephalography (EEG) have shown good potential but failed to demonstrate reliable performance in real-life applications. Especially if one applies a setup that might impact affective processing and relies on generalized models of affect. Additionally, using subjective assessment of ones affect as ground truth has often been disputed. To shed the light on the former challenge we explored the use of a convenient EEG system with 20 participants to capture their reaction to affective movie clips in a naturalistic setting. Employing state-of-the-art machine learning approach demonstrated that the highest performance is reached when combining linear features, namely symmetry features and single-channel features, with nonlinear ones derived by a multiscale entropy approach. Nevertheless, the best performance, reflected in the highest F1-score achieved in a binary classification task for valence was 0.71 and for arousal 0.62. The performance was 10&ndash;20% better compared to using ratings provided by 13 independent raters. We argue that affective self-assessment might be underrated and it is crucial to account for personal differences in both perception and physiological response to affective cues

Field potential duration prolongation with increased hERG channel blocker E-4031 drug concentrations.

<p>Abbreviations: FPD- field potential duration, BR-beating rate.</p><p>Mean and S.D. of field potential durations from manually analysed field potential complexes (n = 25) were compared to those of corresponding averaged signals from CardioMDA in a drug dataset depicting the effect of E-4031 on human iPSC-derived CMs. The field potential duration rises as a result of increasing drug concentration. From the results obtained using our software, it can be seen that the changes in field potential duration correspond well to values obtained from manual analysis.</p