A multi-step method with signal quality assessment and fine-tuning procedure to locate maternal and fetal QRS complexes from abdominal ECG recordings

Abstract

Non-invasive monitoring of fetal electrocardiogram (fECG) plays an important role in detecting and diagnosing fetal diseases. This study aimed to develop a multi-step method for locating both maternal and fetal QRS complexes from abdominal ECG (aECG) recordings. The proposed method included four major steps: abdominal ECG pre-processing, maternal QRS complex locating, maternal ECG cancellation and fetal QRS complex locating. Signal quality assessment (SQA) and fine-tuning for maternal ECG (FTM) were implemented in the first and third steps, respectively. The method was then evaluated using 75 non-invasive 4-channel aECG recordings provided by the PhysioNet/Computing in Cardiology Challenge 2013. The F1 measure, which is a new index introduced by Behar et al (2013 Proc. Comput. Cardiol. 40 297-300), was used to assess the locating accuracy. The other two indices, mean squared error of heart rate (MSE_HR) between the fetal HR signals estimated from the reference and our method (MSE_HR in bpm(2)) and root mean squared difference between the corresponding fetal RR intervals (MSE_RR in ms) were also used to assess the locating accuracy. Overall, for the maternal QRS complex, the F1 measure was 98.4% from the method without the implementation of SQA, and it was improved to 99.8% with SQA. For the fetal QRS complex, the F1 measure, MSE_HR and MSE_RR were 84.9%, 185.6 bpm(2) and 19.4 ms for the method without both SQA and FTM procedures. They were improved to 93.9%, 47.5 bpm(2) and 7.6 ms with both SQA and FTM procedures. These improvements were observed from each individual subject. It can be concluded that implementing both SQA and FTM procedures could achieve better performance for locating both maternal and fetal QRS complexes.

Wavelet Entropy Automatically Detects Episodes of Atrial Fibrillation from Single-Lead Electrocardiograms

“…It should also be mentioned that a very similar result was obtained when another entropy metric, such as sample entropy (SampEn), was used instead of WE. SampEn was tested because it has proven recently to have a high ability to identify noise in the ECG [54]. On the other hand, it is interesting to remark that although the method has been developed for single-lead ECG recordings, detection of noisy beats could be helpful in multi-lead ECG environments to improve the proposed method's robustness.…”

Determination of Sample Entropy and Fuzzy Measure Entropy Parameters for Distinguishing Congestive Heart Failure from Normal Sinus Rhythm Subjects

“…The setting ranges of N usually vary largely, from dozens up to thousands of points [4]. The aforementioned recommendation is mainly based on the application of ApEn to relatively slow dynamic signals such as heart rate [1,3] and hormone secretion data [5], as well as used for ECG signal quality assessment [6,7]. Because SampEn is regarded as a modified version of ApEn designed to solve its shortcomings, such as bias and relative inconsistency [2], these recommended values are also applicable to SampEn [2,[8][9][10].…”

Extract Fetal ECG from Single-Lead Abdominal ECG by De-Shape Short Time Fourier Transform and Nonlocal Median

“…Thus, it would be of great interest to consider the possibility of combining the proposed algorithm with other methods, like the combination proposed in Behar et al [52]. More practically, while in real life, the noise could be highly nonstationary, we may want to combine the notion of a signal quality index (SQI) to help guide us in selecting the "good" signal for analysis [13,66,67]. In general, if there is too much noise and it bypasses the information limitation, then there is no hope to recover anything.…”