My heart is racing! Psychophysiological dynamics of skilled racecar drivers

Our purpose was to test the multi-action plan (MAP) model assumptions in which athletes’ psychophysiological patterns differ among optimal and suboptimal performance experiences. Nine professional drivers competing in premier race categories (e.g., Formula 3, Porsche GT3 Cup Challenge) completed the study. Data collection involved monitoring the drivers’ perceived hedonic tone, accuracy on core components of action, posture, skin temperature, respiration rate, and heart rate responses during a 40-lap simulated race. Time marks, gathered at three standardized sectors, served as the performance variable. The A1GP racing simulator (Allinsport, Modena) established a realistic race platform. Specifically, the Barcelona track was chosen due to its inherently difficult nature characterized by intermittent deceleration points. Idiosyncratic analyses showed large individual differences in the drivers’ psychophysiological profile, as well as distinct patterns in regards to optimal and suboptimal performance experiences. Limitations and future research avenues are discussed. Action (e.g., attentional control) and emotion (e.g., biofeedback training) centered applied sport psychology implications are advanced

Athletic Performance and Recovery-Stress Factors in Cycling: An Ever Changing Balance

We sought to examine whether the relationship between recovery-stress factors and performance would differ at the beginning (Stage 1) and the end (Final Stage) of a multi-stage cycling competition. Sixty-seven cyclists with a mean age of 21.90 years (SD = 1.60) and extensive international experience participated in the study. The cyclists responded to the Recovery-Stress Questionnaire for Athletes (RESTQ-Sport) and rated their performance (1 = extremely poor to 10 = excellent) in respect to the first and last stage. Two step-down multiple regression models were used to estimate the relationship among recovery (nine factors; e.g., Physical Recovery, Sleep Quality) and stress factors (10 factors; e.g., Lack of Energy, Physical Complaints), as assessed by the RESTQ and in relation to performance. Model-1 pertained to Stage 1, whereas Model-2 used data from the Final Stage. The final Model-1 revealed that Physical Recovery (β = .46, p = .01), Injury (β = -.31, p = .01) and General Well-being (β = -.26, p = .04) predicted performance in Stage 1 (R2 = .21). The final Model-2 revealed a different relationship between recovery-stress factors and performance. Specifically, being a climber (β = .28, p = .01), Conflicts/Pressure (β = .33, p = .01), and Lack of Energy (β = -.37, p = .01) were associated with performance at the Final Stage (R2 = .19). Collectively, these results suggest that the relationship among recovery and stress factors changes greatly over a relatively short period of time, and dynamically influences performance in multi-stage competitions

A method for the automatic reconstruction of fetal cardiac signals from magnetocardiographic recordings.

Fetal magnetocardiography (fMCG) allows monitoring the fetal heart function through algorithms able to retrieve the fetal cardiac signal, but no standardized automatic model has become available so far. In this paper, we describe an automatic method that restores the fetal cardiac trace from fMCG recordings by means of a weighted summation of fetal components separated with independent component analysis (ICA) and identified through dedicated algorithms that analyse the frequency content and temporal structure of each source signal. Multichannel fMCG datasets of 66 healthy and 4 arrhythmic fetuses were used to validate the automatic method with respect to a classical procedure requiring the manual classification of fetal components by an expert investigator. ICA was run with input clusters of different dimensions to simulate various MCG systems. Detection rates, true negative and false positive component categorization, QRS amplitude, standard deviation and signal-to-noise ratio of reconstructed fetal signals, and real and per cent QRS differences between paired fetal traces retrieved automatically and manually were calculated to quantify the performances of the automatic method. Its robustness and reliability, particularly evident with the use of large input clusters, might increase the diagnostic role of fMCG during the prenatal period

Performance comparison of independent component analysis algorithms for fetal cardiac signal reconstruction: a study on synthetic fMCG data.

Independent component analysis (ICA) algorithms have been successfully used for signal extraction tasks in the field of biomedical signal processing. We studied the performances of six algorithms (FastICA, CubICA, JADE, Infomax, TDSEP and MRMI-SIG) for fetal magnetocardiography (fMCG). Synthetic datasets were used to check the quality of the separated components against the original traces. Real fMCG recordings were simulated with linear combinations of typical fMCG source signals: maternal and fetal cardiac activity, ambient noise, maternal respiration, sensor spikes and thermal noise. Clusters of different dimensions (19, 36 and 55 sensors) were prepared to represent different MCG systems. Two types of signal-to-interference ratios (SIR) were measured. The first involves averaging over all estimated components and the second is based solely on the fetal trace. The computation time to reach a minimum of 20 dB SIR was measured for all six algorithms. No significant dependency on gestational age or cluster dimension was observed. Infomax performed poorly when a sub-Gaussian source was included; TDSEP and MRMI-SIG were sensitive to additive noise, whereas FastICA, CubICA and JADE showed the best performances. Of all six methods considered, FastICA had the best overall performance in terms of both separation quality and computation times