Harmful cardiac events occurs frequently after exercise. However, the cardiac autonomic regulation after vigorous exercise is not well known. This study was designed to assess heart rate (HR) variability before and after a 75 km cross-country skiing race. HR variability was assessed by using standard statistical measures along with spectral and quantitative Poincarè plot analysis of HR variability in 10 healthy male subjects (age 36 +/- 11 years). The average HR was at the same level 1 day after the race as before the race, but on the second day, HR was significantly lower (P<0.001) compared with the prerace and 1 day after values. The normalized high-frequency (HF) spectral component of HR variability (nuHF) was lower (P<0.01) on the first day after the maximal exercise compared with the pre-exercise values but returned to or even exceeded the prerace level on the second day (P<0.01). The changes in short-term R-R interval variability analysed from the Poincaré plot were similar to those observed in the HF spectral component. The normalized low-frequency (LF) spectral component of HR variability (nuLF) was higher (P<0.01) on the first day after the exercise compared with the prerace levels and it also returned to the pre-exercise level or even dropped below it on the second day after the race. The mean time it took the HF spectral component to return to the pre-exercise level was 4.2 +/- 4.2 h (ranging from 0 to 12 h). This recovery time correlated inversely with the maximal oxygen consumption (VO2max) measured during the bicycle exercise test before the skiing race (r=-0.712, P<0.016). The cardiac vagal outflow is blunted for several hours after prolonged vigorous exercise. The recovery time of reduced vagal outflow depends on individual cardiorespiratory fitness and there is an accentuated rebound of altered autonomic regulation on the second day after prolonged exercise.

Individual responses to aerobic training vary from almost none to a 40% increase in aerobic fitness in sedentary subjects. The reasons for these differences in the training response are not well known. We hypothesized that baseline cardiovascular autonomic function may influence the training response. The study population included sedentary male subjects (n = 39, 35 +/- 9 yr). The training period was 8 wk, including 6 sessions/wk at an intensity of 70-80% of the maximum heart rate for 30-60 min/session. Cardiovascular autonomic function was assessed by measuring the power spectral indexes of heart rate variability from 24-h R-R interval recordings before the training period. Mean peak O2 uptake increased by 11 +/- 5% during the training period (range 2-19%). The training response correlated with age (r = -0.39, P = 0.007) and with the values of the high-frequency (HF) spectral component of R-R intervals (HF power) analyzed over the 24-h recording (r = 0.46, P = 0.002) or separately during the daytime hours (r = 0.35, P = 0.028) and most strongly during the nighttime hours (r = 0.52, P = 0.001). After adjustment for age, HF power was still associated with the training response (e.g., P = 0.001 analyzed during nighttime hours). These data show that cardiovascular autonomic function is an important determinant of the response to aerobic training among sedentary men. High vagal activity at baseline is associated with the improvement in aerobic power caused by aerobic exercise training in healthy sedentary subjects.

Large individual differences in the responsiveness of cardiorespiratory fitness (VO2peak) to endurance training have been observed in healthy subjects. We tested the hypothesis that subjects with a poor responsiveness to endurance training might benefit from resistance training in terms of aerobic fitness. The study population consisted of sedentary healthy male and female subjects (n=91, 42+/-5 year) assigned to either a training (n=73) or a control group (n=18). The randomized cross-over study design included a 2-week laboratory-controlled endurance or resistance training period with a 2-month detraining period between the interventions. Large individual differences were observed in the changes of VO2peak (DeltaVO2peak) after both the endurance (average 8+/-6 %, P<0.001, range -5 to +22%) and resistance training (average 4+/-5%, P<0.001, range -8 to +16%). The average increase in DeltaVO2peak between genders was similar after both the endurance (8+/-6% for both genders, P=ns) and resistance training (3+/-5% for males and 5+/-6% for females, P=ns). There was no linear relationship between the changes in VO2peak after each training intervention (r=-.09, P=ns). On the contrary, when the study group was divided into quartiles according to the endurance training response (1+/-3, 6+/-1, 9+/-1, and 16+/-3% increase in VO2peak), the group with the lowest response to endurance training increased VO2peak after the resistance training intervention (DeltaVO2peak 7+/-5%, P<0.001). The individual responsiveness of VO2peak to exercise training is related to the mode of training. The healthy males and females whose training response is low after endurance training seem to result in a marked improvement in their cardiorespiratory fitness by resistance training.

This study was designed to assess the effects of moderate- and high-volume aerobic training on the time domain and on spectral and fractal heart rate (HR) variability indexes. Sedentary subjects were randomized into groups with moderate-volume training (n = 20), high-volume training (n = 20), and controls (n = 15). The training period was 8 wk, including 6 sessions/wk at an intensity of 70-80% of the maximum HR, lasting for 30 min/session in the moderate-volume group and 60 min/session in the high-volume group. Time domain, frequency domain, and short-term fractal scaling measures of HR variability were analyzed over a 24-h period. Mean HR decreased from 70 +/- 7 to 64 +/- 8 beats/min and from 67 +/- 5 to 60 +/- 6 beats/min (P < 0.001 for both) for the moderate- and high-volume training groups, respectively. The normalized high-frequency spectral component increased in both groups (P < 0.05). The normalized low-frequency component decreased significantly (P < 0.05), resulting in a marked decrease in low frequency-to-high frequency ratio in both groups. In addition, short-term scaling exponent decreased in both groups (P < 0.001). There were no significant differences in the changes of HR variability indexes between groups. Aerobic training in sedentary subjects results in altered autonomic regulation of HR toward vagal dominance. A moderate training volume is a sufficient intervention to induce these beneficial effects.

The design, test methods and results of an ambulatory QRS detector are presented. The device is intended for the accurate measurement of heart rate variability (HRV) and reliable QRS detection in both ambulatory and clinical use. The aim of the design work was to achieve high QRS detection performance in terms of timing accuracy and reliability, without compromising the size and power consumption of the device. The complete monitor system consists of a host computer and the detector unit. The detector device is constructed of a commonly available digital signal processing (DSP) microprocessor and other components. The QRS detection algorithm uses optimized prefiltering in conjunction with a matched filter and dual edge threshold detection. The purpose of the prefiltering is to attenuate various noise components in order to achieve improved detection reliability. The matched filter further improves signal-to-noise ratio (SNR) and symmetries the QRS complex for the threshold detection, which is essential in order to achieve the desired performance. The decision for detection is made in real-time and no search-back method is employed. The host computer is used to configure the detector unit, which includes the setting of the matched filter impulse response, and in the retrieval and postprocessing of the measurement results. The QRS detection timing accuracy and detection reliability of the detector system was tested with an artificially generated electrocardiogram (ECG) signal corrupted with various noise types and a timing standard deviation of less than 1 ms was achieved with most noise types and levels similar to those encountered in real measurements. A QRS detection error rate (ER) of 0.1 and 2.2% was achieved with records 103 and 105 from the MIT-BIH Arrhythmia database, respectively.