Estudios de asociación de genoma completo (GWAS)

Despite the rich and growing literature that focus on NIFECG extraction and FQRS detection, few of those works are actually reproducible. This is mainly due to the lack of common dataset and open-source software. Due to its versatile framework, many authors make use of the EKF approaches (described in Section3.3.4). However, EKF heavily depends on a well-representative model and is sensitive to its initialization/calibration. The non-observance of these aspects leads to the undesired suppression of fetal peaks, either when MECG temporal overlap occurs (lack of trust in model) or partial suppression of the FECG due to (noise overestimation – remember that the FECG is treated as noise). In this work, those topics were further explored, particularly regarding the MECG/FECG modelling. Therefore, three aspects are further explored: i) the

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creation of the MECG template/model, ii) the varying presence of measurement noise, and iii) the ill-conditioned assumption that the FECG can be represented as WGN. A systematic calibration procedure is carried out to guarantee the general validity of the designed filters.

Regarding FQRS detection and FHR estimation (presented in Section 3.4-3.5), as can be seen on the PCINC 2013-related papers, current techniques are often faulty. This problem is addressed in this work by using two different multichannel FQRS/FHR correction methods, which take into consideration different signal quality metrics that weight down unreliable detections. One of those techniques is considered as responsible for the author’s first-place award and top-scores on PCINC 2013 competition [20,21], as further clarified.

3.8 Chapter Summary

In this chapter, current prenatal diagnostic techniques were presented and the benefits from NIFECG clearly stated. Further, an overview on NIFECG extraction algorithms was provided. Further in this section, the discrete-time KF and EKF algorithms were briefly derived. In Sec- tion3.3.4the current state-of-the-art on EKF for FECG extraction was presented. Particularly two models were presented, the 2-state EKF algorithm [370] (henceforth named EKF2), ex- tended states EKF [6,375] (henceforth calledEKF24 - i.e. 3·Nk+ 3 states, consideringNk= 7 as discussed in the next chapter of this work). In Sections3.4-3.6the metrics to assess FQRS, FHR and FECG morphology parameters were presented. In Section3.7, the current challenges on NIFECG signal processing that were addressed in the remaining of this work were clarified. In the next chapter, novel methods are proposed to deal with the limitation of current techniques.

4

Novel Approaches for Fetal ECG Analysis

In this chapter, newly developed approaches for NIFECG analysis are presented. These methods are divided into NIFECG extraction (Section4.2) and FQRS/FHR correction methods (Section4.3). Before proceeding with the proposed improvements on FECG signal extraction and FQRS detection/correction performed throughout this thesis, some preliminary issues about the available signals are presented in the following section.

4.1 Preliminary Considerations

Regarding NIFECG’s signal processing chain (see Fig. 3.6), preprocessing is an important step, on which FQRS detection results or morphological analysis strongly depend. For this reason, before proceeding, some definitions on the following aspects shall be considered:

• bandpass filtering range;

• MQRS reference and re-alignment;

• initialization window allowed and online/offline execution strategy; • channel selection.

The pre-filtering bands are a crucial aspect of NIFECG research on which depending on the application the final results may be heavily influenced. For instance, if the aim is to detect FQRS complexes, a narrower band is required than when the focus is on FECG morphological analysis. While dealing with FQRS detection accuracy, Beharet al.[54] evaluated different high-pass cut-offfrequencies using several extraction methods. For most approaches by raising the higher cut-offfrequencies to 10 Hz, an improvement of up to 3% in theF1(see Section3.4.2) occurred. On the order hand, for morphological analysis a higher cut-offwill lead to P and T-wave distortion and suppression (see Kligfieldet al.[231] guideline for adult electrocardiography).

In this work, two passing bands (henceforth called narrow and wide bands), were defined as in Andreottiet al.[18]. Both bands made use of a low-pass cutofffrequency at 100 Hz and used Butterworth zero-phase filters. The narrow band consists of a 3rd _{order low-pass and 5}th_order high-pass filter with cutoffat 3 Hz. Meanwhile the wide band made use of a 7th_{order low-pass} filter and 8thhigh-pass filter with cutoffat 0.5 Hz, in order to preserve most of the fetal T-wave. Both filters were designed to match a 20 dB attenuation at the stop-band and 0.1 dB gain at the pass-band) [18]. Additionally, aninfinite impulse response (IIR)notch filter was included for suppressing the powerline interference suppression at 50 or 60 Hz (±1 %).

With regards to MQRS locations, as mentioned in Section 3.3.4 EKF extraction strongly depends on a reliable MQRS reference annotation. As discussed in Section3.2.3, NIFECG analysis can greatly benefit from the presence of a MECG reference lead with little additional computational effort and increase in hardware complexity. Therefore, in this work it is assumed that a MQRS reference is available. From the author’s own experience on collecting this study’s clinical data, a MECG channel is not a too restrictive assumption and the benefits are valuable. In order to align this maternal reference to each abdominal channel’s peak a re-alignment was performed. During EKF’s initialization, the absolute maxima around each maternal references peak with a window of_±100 ms was sought. The average lag between reference and maxima location was then regarded as re-alignment factor and every MQRS annotation is shifted by this factor before the extraction takes place.

A common strategy for signal processing algorithms running on biomedical devices is to allow a short initialization period, after which algorithms should be able to run online. Aiming at producing online methods for NIFECG, throughout this work an initialization window of 60 seconds is used for every extraction method. Moreover, for the same reason no offline smoothing filter was applied. In the scope of NIFECG such initialization window is interesting since it allows a reproducible comparison between TS, AM and BSS extraction methods, by allowing that each technique:

• TS: generation of initial template;

• AM: initialization of adaptive filter coefficients or training period; • BSS: mixing matrix calculation;

Channel selection is another topic to be considered when dealing with multichannel record- ings of abdECG signals. Indeed the information obtained from bad quality channels should be weighted down (or discarded). However, to date there is no consensus for assessing the signal quality in NIFECG recordings. Moreover, adult SQI metrics are prone to confound be- tween FQRS and MQRS complexes. For this reason, in this work, no channel selection on the preprocessing step is performed. Instead, the extraction algorithms (developed in Section4.2) were applied on every available channel and, by using specifically designed fetal SQI metrics in association with novel FQRS detectors (further shown in Section4.3), the information from multiple channels is fused.

In document Riesgo Genético y Endofenotipos en Familias con Trastorno Bipolar (página 32-36)