A new indirect contact (IDC) electrocardiogram (ECG) measurement method (IDC-ECG) for monitoring ECG during sleep that is adequate for long-term use is provided. The provided method did not require any direct conductive contact between the instrument and bare skin. This method utilizes an array of high-input-impedance active electrodes fixed on the mattress and an indirect-skin-contact ground made of a large conductive textile sheet. A thin cotton bedcover covered the mattress, electrodes, and conductive textile, and the participants were positioned on the mattress over the bedcover. An ECG was successfully obtained, although the signal quality was lower and the motion artifact was larger than in conventional direct-contact measurements (DC-ECG). The results showed that further studies are required to apply the provided method to an ECG diagnosis of cardiovascular diseases. However, currently the method can be used for HRV assessment with easy discrimination of R-peaks.

This study proposes the use of flexible capacitive electrodes for reducing motion artifacts in a wearable electrocardiogram (ECG) device. The capacitive electrodes have conductive foam on their surface, a shield, an optimal input bias resistor, and guarding feedback. The electrodes are integrated in a chest belt, and the acquired signals are transmitted wirelessly for ambulatory heart rate monitoring. We experimentally validated the electrode performance with subjects standing and walking on a treadmill at speeds of up to 7 km/h. The results confirmed the highly accurate heart rate detection capacity of the developed system and its feasibility for daily-life ECG monitoring.

In this study, optimal methods for re-sampling and spectral estimation in frequency-domain heart rate variability (HRV) analysis were investigated through a simulation using artificial RR-interval data. Nearest-neighbour, linear, cubic spline and piecewise cubic Hermite interpolation methods were considered for re-sampling and representative non-parametric, parametric, and uneven approaches were used for spectral estimation. Based on this result, the effects of missing RR-interval data on frequency-domain HRV analysis were observed through the simulation of missing data using real RR-interval tachograms. For this simulation, data including the simulated artefact section (0-100 s) were used; these data were selected randomly from the real RR data obtained from the MIT-BIH normal sinus rhythm RR-interval database. In all, 7182 tachograms of 5 min durations were used for this analysis. The analysis for certain missing data durations is performed by 100 Monte Carlo runs. TF, VLF, LF and HF were estimated as the frequency-domain parameters in each run, and the normalized errors between the data with and without the missing data duration for these parameters were calculated. Rules obtained from the results of these simulations were evaluated with real missing RR-interval data derived from a capacitive-coupled ECG during sleep.

In this study, the effects of missing RR-interval data on time-domain analysis were investigated using simulated missing data in real RR-interval tachograms and actual missing RR data in an ECG obtained by an unconstrained measurement. For the simulation, randomly selected data (0-100 s) were removed from real RR data obtained from the MIT-BIH normal sinus rhythm database. In all, 2615 tachograms of 5 min durations were used for this analysis. For certain durations of missing data, the analysis was performed by 1000 Monte Carlo runs. MeanNN, SDNN, SDSD, RMSSD and pNN50 were calculated as the time-domain parameters in each run, and the relative errors between the original and the incomplete tachograms for these parameters were computed. The results of the simulation revealed that MeanNN is the parameter most robust to missing data; this feature can be explained by the theory of finite population correction (FPC). pNN50 is the parameter most sensitive to missing data. MeanNN was also found to be the most robust to real missing RR data derived from a capacitive-coupled ECG recorded during sleep; furthermore, the parameter patterns for the missing data were considerably similar to those for the original RR data, although the relative errors may exceed those of the simulation results.

This paper suggests a beat detection method for ballistocardiogram (BCG) from an unconstrained cardiac signal monitoring devices. A fiducial peak point of BCG is an I-J-K complex which corresponds with ventricle contraction and Electrocardiogram (ECG) QRS complex. The goal of the method is extraction of J peak without ECG synchronization. The detection method is based on a "template matching" rule evaluated using a correlation function in a local moving-window procedure. The total beat detection algorithm operates in two stages, template definition stage and beat detection stage with defined template in previous stage. In the first stage, the BCG template is constructed by the expert with an empirical analysis of BCG signal and measurement device. In the second stage, the correlation function calculates an accuracy of template with BCG signal using a local moving-window. The data analysis has been performed on the subjects tested at Seoul National University Hospital Sleep Medicine Center and presents 95.16% of sensitivity and 94.76% of positive predictivity value for the J peak detection.