Fast time-varying linear filters for suppression of baseline drift in electrocardiographic signals.

BACKGROUND: The paper presents a method of linear time-varying filtering, with extremely low computational costs, for the suppression of baseline drift in electrocardiographic (ECG) signals. An ECG signal is not periodic as the length of its heart cycles vary. In order to optimally suppress baseline drift by the use of a linear filter, we need a high-pass filter with time-varying cut-off frequency controlled by instant heart rate. METHODS: Realization of the high-pass (HP) filter is based on a narrow-band low-pass (LP) filter of which output is subtracted from the delayed input. The base of an LP filter is an extremely low computational cost Lynn's filter with rectangular impulse response. The optimal cut-off frequency of an HP filter for baseline wander suppression is identical to an instantaneous heart rate. Instantaneous length of heart cycles (e.g. RR intervals) are interpolated between QRS complexes to smoothly control cut-off frequency of the HP filter that has been used. RESULTS AND CONCLUSIONS: We proved that a 0.5 dB decrease in transfer function, at a time-varying cut-off frequency of HP filter controlled by an instant heart rate, is acceptable when related to maximum error due to filtering. Presented in the article are the algorithms that enable the realization of time-variable filters with very low computational costs. We propose fast linear HP filters for the suppression of baseline wander with time-varying cut-off frequencies controlled by instant heart rate. The filters fulfil accepted professional standards and increase the efficiency of the noise suppression.

CSE database: extended annotations and new recommendations for ECG software testing.

Nowadays, cardiovascular diseases represent the most common cause of death in western countries. Among various examination techniques, electrocardiography (ECG) is still a highly valuable tool used for the diagnosis of many cardiovascular disorders. In order to diagnose a person based on ECG, cardiologists can use automatic diagnostic algorithms. Research in this area is still necessary. In order to compare various algorithms correctly, it is necessary to test them on standard annotated databases, such as the Common Standards for Quantitative Electrocardiography (CSE) database. According to Scopus, the CSE database is the second most cited standard database. There were two main objectives in this work. First, new diagnoses were added to the CSE database, which extended its original annotations. Second, new recommendations for diagnostic software quality estimation were established. The ECG recordings were diagnosed by five new cardiologists independently, and in total, 59 different diagnoses were found. Such a large number of diagnoses is unique, even in terms of standard databases. Based on the cardiologists' diagnoses, a four-round consensus (4R consensus) was established. Such a 4R consensus means a correct final diagnosis, which should ideally be the output of any tested classification software. The accuracy of the cardiologists' diagnoses compared with the 4R consensus was the basis for the establishment of accuracy recommendations. The accuracy was determined in terms of sensitivity = 79.20-86.81%, positive predictive value = 79.10-87.11%, and the Jaccard coefficient = 72.21-81.14%, respectively. Within these ranges, the accuracy of the software is comparable with the accuracy of cardiologists. The accuracy quantification of the correct classification is unique. Diagnostic software developers can objectively evaluate the success of their algorithm and promote its further development. The annotations and recommendations proposed in this work will allow for faster development and testing of classification software. As a result, this might facilitate cardiologists' work and lead to faster diagnoses and earlier treatment.

Heart rate variability analysed by Poincaré plot in patients with metabolic syndrome.

INTRODUCTION: The SD1 and SD2 indexes (standard deviations in two orthogonal directions of the Poincaré plot) carry similar information to the spectral density power of the high and low frequency bands but have the advantage of easier calculation and lesser stationarity dependence. METHODS: ECG signals from metabolic syndrome (MetS) and control group patients during tilt table test under controlled breathing (20 breaths/minute) were obtained. SD1, SD2, SDRR (standard deviation of RR intervals) and RMSSD (root mean square of successive differences of RR intervals) were evaluated for 31 control group and 33 MetS subjects. RESULTS: Statistically significant lower values were observed in MetS patients in supine position (SD1: p=0.03, SD2: p=0.002, SDRR: p=0.006, RMSSD: p=0.01) and during tilt (SD2: p=0.004, SDRR: p=0.007). CONCLUSION: SD1 and SD2 combining the advantages of time and frequency domain methods, distinguish successfully between MetS and control subjects.

Adaptive wavelet Wiener filtering of ECG signals.

In this study, we focused on the reduction of broadband myopotentials (EMG) in ECG signals using the wavelet Wiener filtering with noise-free signal estimation. We used the dyadic stationary wavelet transform (SWT) in the Wiener filter as well as in estimating the noise-free signal. Our goal was to find a suitable filter bank and to choose other parameters of the Wiener filter with respect to the signal-to-noise ratio (SNR) obtained. Testing was performed on artificially noised signals from the standard CSE database sampled at 500 Hz. When creating an artificial interference, we started from the generated white Gaussian noise, whose power spectrum was modified according to a model of the power spectrum of an EMG signal. To improve the filtering performance, we used adaptive setting parameters of filtering according to the level of interference in the input signal. We were able to increase the average SNR of the whole test database by about 10.6 dB. The proposed algorithm provides better results than the classic wavelet Wiener filter.