Artifact tolerance test for capacitive wearable chest-belt electrocardiograph - Effect of electrode configuration

Electrocardiogram (ECG) provides useful information for the diagnosis of cardiovascular disease. In capacitive-coupled ECG sensing, electrostatic artifact and movement artifact become serious problems. In particular, low frequency components such as T wave of ECG are susceptible to the artifacts. To obtain clear ECG, high stability is required for the electrocardiograph using capacitive-coupled electrode. In this study, tolerance of capacitive wearable chest-belt electrocardiograph was tested for the electrostatic and movement artifacts. A constant electrostatic artifact was discharged repeatedly on three types of electrode having different shielding configurations, and their transient responses were compared in terms of deviation area, transient slope and recovery time. The results revealed the best tolerance of doubly-shielded five-layered electrode. The best performed five-layered electrode was used for exercise tolerance test. Then, detection rates of R-wave and T-wave of ECG, and standard deviation of base line of the recordings were calculated as tolerance indices and compared to those obtained by commercial disposable electrode. Although R-wave detection rate of the five-layered electrode decreased by 2.0%, the rate of T-wave was comparable to those of the disposable electrode. Furthermore, the standard deviation of the base line was significantly smaller than that of the disposable electrode (p<0.01)

ECG QT-I nterval Measurement Using Wavelet Transformation

Wavelet transformation, with its markedly high time resolution, is an optimal technique for the analysis of non-stationary waveform signals, such as physiological signals. Therefore, wavelet transformation is widely applied to electrocardiographic (ECG) signal processing. However, an appropriate application method for automated QT-interval measurement has yet to be established. In this study, we developed an ECG recognition technique using wavelet transformation and assessed its efficacy and functionality. The results revealed that the difference between the values obtained using our algorithm and the visually measured QT interval was as low as 4.8 ms. Our technique achieves precise automated QT-interval measurement, as well as Te recognition, that is difficult to accomplish even by visual examination under the electromyography noise environment