A vector-free ECG interpretation with P, QRS & T waves as unbalanced transitions between stable configurations of the heart electric field during P-R, S-T & T-P segments

Electrocardiographic properties of Topsy-Turvy heart

Topsy-turvy heart (TTH) is an exceedingly rare cardiovascular malformation characterized by clockwise rotation of the entire heart around its longitudinal axis. There is superoinferior ventricle positioning without the twisting of atrioventricular connections. Electrocardiography (ECG) is a simple noninvasive tool to aid the diagnosis of heart disease. In this case series, we discuss 12-lead ECG findings of three patients with TTH.

Assessment of artifacts reduction and denoising techniques in Electrocardiographic signals using Ensemble Average-based method

BACKGROUND AND OBJECTIVE

Outpatient vital signs monitoring has a key role in medical diagnosis and treatment. However, ambulatory vital signs monitoring has great challenges to overcome, being the most important, the reduction of noise and Motion Artifacts, which hide essential information, particularly in Electrocardiographic signals. Despite efforts being made to reduce these artifacts, a comparative performance assessment of proposed techniques does not exist to the best of our knowledge and there are no enhancement level measurements obtained by the signals in the artifacts reduction. This article presents a new method based on Ensemble Average for the performance comparison of reported techniques for the processing and reduction of noise and artifacts in Electrocardiographic signals.

METHODS

The comparison was done using a dataset composed by six synthetic noised Electrocardiographic signals and six real one acquired from healthy volunteers that intentionally introduced Motion Artifacts. Several techniques that have reported positive results in the enhancement of Electrocardiographic signals were applied to this dataset to compare their performance in the reduction of Motion Artifacts. The Signal-to-Noise Ratio and the Ensemble Average as a distortion measurement were used to compare the performance of algorithms to produce an enhanced signal.

RESULTS

In agreement to previous reports, all studied methods show a significant improvement of the Signal-to-Noise Ratio. Concerning the distortion of the waveform, although all methods caused high distortion on the enhanced signal waveform, the Wavelet-ICA method showed the best performance. The percentage of signal distortion introduced by denoising techniques was evaluated through the proposed Ensemble Average Electrocardiographic method.

CONCLUSIONS

It was found that the proposed method based on Ensemble Average offers a complementary way to measure the performance of denoising techniques when considering the introduced distortion in the waveform segments once the artifact reduction process was applied and not only the change in the Signal-to-Noise Ratio.

IN SEARCH FOR REAL-TIME NONINVASIVE ASSESSMENT OF MUSCLE FATIGUE DURING EXERTION: CAN DISPERSION OF HIGH-RESOLUTION MUSCLE ELECTRIC ACTIVITY DATA PROVIDE NEW INSIGHTS?

SUMMARY – Paralumbar muscle performance and fatigue were evaluated by measuring electromagnetic activity during entire body vibration (EBV) in 44 healthy subjects. Physical fitness of subjects was estimated on a 5-degree scale. Electric activity was recorded in 200 seconds with 1 kHz sampling on the Biopac Student Lab during EBV. Data were used to produce time series for two vectors of the phase space and spatial axis: X (left-right), Y (up-down) and Z (ventral-dorsal). Time series were evaluated by calculating fractal dimension by the R/S algorithm. Movement of the electric field along the Y-axis showed changes (up-down) extracted in the first and second quarter of the measurement (p=0.02 and p=0.03, respectively). These changes were not specific for gender but showed dependence on subject age and fitness. The fractal dimension values by the R/S algorithm were larger in female subjects. Results suggested the electric field changes during EBV in the up-down direction to contain information on muscular performance and fatigue, not dependent on gender, but on the age and degree of overall physical fitness.

A new method for the extraction of fetal ECG from the dependent abdominal signals using blind source separation and adaptive noise cancellation techniques

BACKGROUND

The electrocardiogram (ECG) is a diagnostic tool that records the electrical activity of the heart, and depicts it as a series of graph-like tracings, or waves. Being able to interpret these details allows diagnosis of a wide range of heart problems. Fetal electrocardiogram (FECG) extraction has an important impact in medical diagnostics during the mother pregnancy period. Since the observed FECG signals are often mixed with the maternal ECG (MECG) and the noise induced by the movement of electrodes or by mother motion, the separation process of the ECG signal sources from the observed data becomes quite complicated. One of its complexity is when the ECG sources are dependent, thus, in this paper we introduce a new approach of blind source separation (BSS) in the noisy context for both independent and dependent ECG signal source. This approach consist in denoising the observed ECG signals using a bilateral total variation (BTV) filter; then minimizing the Kullbak-Leibler divergence between copula densities to separate the FECG signal from the MECG one.

RESULTS

We present simulation results illustrating the performance of our proposed method. We will consider many examples of independent/dependent source component signals. The results will be compared with those of the classical method called independent component analysis (ICA) under the same conditions. The accuracy of source estimation is evaluated through a criterion, called again the signal-to-noise-ratio (SNR). The first experiment shows that our proposed method gives accurate estimation of sources in the standard case of independent components, with performance around 27 dB in term of SNR. In the second experiment, we show the capability of the proposed algorithm to successfully separate two noisy mixtures of dependent source components - with classical criterion devoted to the independent case - fails, and that our method is able to deal with the dependent case with good performance.

CONCLUSIONS

In this work, we focus specifically on the separation of the ECG signal sources taken from skin two electrodes located on a pregnant woman's body. The ECG separation is interpreted as a noisy linear BSS problem with instantaneous mixtures. Firstly, a denoising step is required to reduce the noise due to motion artifacts using a BTV filter as a very effective one-pass filter for denoising. Then, we use the Kullbak-Leibler divergence between copula densities to separate the fetal heart rate from the mother one, for both independent and dependent cases.