Angela Agostinelli

Noninvasive fetal electrocardiography: an overview of the signal electrophysiological meaning, recording procedures, and processing techniques.

Noninvasive fetal electrocardiography (fECG), obtained positioning electrodes on the maternal abdomen, is important in safeguarding the life and the health of the unborn child. This study aims to provide a review of the state of the art of fECG, and includes a description of the parameters useful for fetus clinical evaluation; of the fECG recording procedures; and of the techniques to extract the fECG signal from the abdominal recordings.

Segmented beat modulation method for electrocardiogram estimation from noisy recordings

Clinical utility of an electrocardiogram (ECG) affected by too high levels of noise such as baseline wanders, electrode motion artifacts, muscular artifacts and power-line interference may be jeopardized if not opportunely processed. Template-based techniques have been proposed for ECG estimation from noisy recordings, but usually they do not reproduce physiological ECG variability, which, however, provides clinically useful information on the patients health. Thus, this study proposes the Segmented-Beat Modulation Method (SBMM) as a new template-based filtering procedure able to reproduce ECG variability, and assesses SBMM robustness to the aforementioned noises in comparison to a standard template method (STM). SBMM performs a unique ECG segmentation into QRS segment and TUP segment, and successively modulates/demodulates (by stretching or compressing) the former segments in order to adaptively adjust each estimated beat to its original morphology and duration. Consequently, SBMM estimates ECG with significantly lower estimation errors than STM when applied to recordings affected by various levels of the considered noises (SBMM: 176-232µV and 79-499µV; STM: 215-496µV and 93-1056µV, for QRS and TUP segments, respectively). Thus, SBMM is able to reproduce ECG variability and is more robust to noise than STM.

The segmented-beat modulation method for ECG estimation.

Electrocardiographic (ECG) tracings corrupted by noise with frequency components in the ECG frequency band, may result useless unless appropriately processed. The estimation of the clean ECG from such recordings, however, is quite challenging; being linear filtering inappropriate. In the common situations in which the R peaks are detectable, template-based techniques have been proposed to estimate the ECG by a template-beat concatenation. However, such techniques have the major limit of not being able to reproduce physiological heart-rate and morphological variability. Thus, the aim of the present study was to propose the segmented-beat modulation method (SBMM) as the technique that overcomes such limit. The SBMM is an improved template-based technique that provides good-quality estimations of ECG tracings characterized by some heart-rate and morphological variability. It segments the template ECG beat into QRS and TUP segments and then, before concatenation, it applies a modulation/demodulation process to the TUP-segment so that the estimated-beat duration and morphology adjust to those of the corresponding original-beat. To test its performance, the SBMM was applied to 19 ECG tracings from normal subjects. There were no errors in estimating the R peak location, and the errors in the QRS and TUP segments were low (≤65 μV and ≤30 μV, respectively), with the former ones being significantly higher than the latter ones. Eventually, TUP errors tended to increase with increasing heart-rate variability (correlation coefficient: 0.59, P<;10-2). In conclusion, the new SBMM proved to be a useful tool for providing good-quality ECG estimations of tracings characterized by heart-rate and morphological variability.

Statistical baseline assessment in cardiotocography

Cardiotocography (CTG) is the most common non-invasive diagnostic technique to evaluate fetal well-being. It consists in the recording of fetal heart rate (FHR; bpm) and maternal uterine contractions. Among the main parameters characterizing FHR, baseline (BL) is fundamental to determine fetal hypoxia and distress. In computerized applications, BL is typically computed as mean FHR±ΔFHR, with ΔFHR=8 bpm or ΔFHR=10 bpm, both values being experimentally fixed. In this context, the present work aims: to propose a statistical procedure for ΔFHR assessment; to quantitatively determine ΔFHR value by applying such procedure to clinical data; and to compare the statistically-determined ΔFHR value against the experimentally-determined ΔFHR values. To these aims, the 552 recordings of the “CTU-UHB intrapartum CTG database” from Physionet were submitted to an automatic procedure, which consisted in a FHR preprocessing phase and a statistical BL assessment. During preprocessing, FHR time series were divided into 20-min sliding windows, in which missing data were removed by linear interpolation. Only windows with a correction rate lower than 10% were further processed for BL assessment, according to which ΔFHR was computed as FHR standard deviation. Total number of accepted windows was 1192 (38.5%) over 383 recordings (69.4%) with at least an accepted window. Statistically-determined ΔFHR value was 9.7 bpm. Such value was statistically different from 8 bpm (P<10−19) but not from 10 bpm (P=0.16). Thus, ΔFHR=10 bpm is preferable over 8 bpm because both experimentally and statistically validated.

Heart Rate Detection Using Microsoft Kinect: Validation and Comparison to Wearable Devices

Contactless detection is one of the new frontiers of technological innovation in the field of healthcare, enabling unobtrusive measurements of biomedical parameters. Compared to conventional methods for Heart Rate (HR) detection that employ expensive and/or uncomfortable devices, such as the Electrocardiograph (ECG) or pulse oximeter, contactless HR detection offers fast and continuous monitoring of heart activities and provides support for clinical analysis without the need for the user to wear a device. This paper presents a validation study for a contactless HR estimation method exploiting RGB (Red, Green, Blue) data from a Microsoft Kinect v2 device. This method, based on Eulerian Video Magnification (EVM), Photoplethysmography (PPG) and Videoplethysmography (VPG), can achieve performance comparable to classical approaches exploiting wearable systems, under specific test conditions. The output given by a Holter, which represents the gold-standard device used in the test for ECG extraction, is considered as the ground-truth, while a comparison with a commercial smartwatch is also included. The validation process is conducted with two modalities that differ for the availability of a priori knowledge about the subjects’ normal HR. The two test modalities provide different results. In particular, the HR estimation differs from the ground-truth by 2% when the knowledge about the subject’s lifestyle and his/her HR is considered and by 3.4% if no information about the person is taken into account.

A New Segmented-Beat Modulation Algorithm for Maternal ECG Estimation from Abdominal Recordings

The noninvasive fetal electrocardiogram (fECG) provides precious information about the physiological fetus state. It is extracted from abdominal recordings, obtained positioning surface electrodes on the maternal abdomen, by subtraction of the maternal ECG (mECG), often roughly estimated by simply concatenating a maternal-beat template. Aim of the present study is to propose a new algorithm for the mECG estimation based on a segmented-beat modulation method (SBMM) that adjusts the template length to the maternal physiological heart-rate variability (HRV) and reduces the level of noise. According to the SBMM, each maternal cardiac cycle (CC) is segmented into two segments, QRS and TUP, respectively independent and proportional to preceding RR interval. The estimated mECG is the concatenation of the template-beat, obtained as the median of the maternal beat after modulation and demodulation of TUP segment. The algorithm was applied to two (ARec1 and ARec2) 4-channel abdominal recordings obtained from pregnant women. ARec1 and ARec2 were both 60 s long and characterized by similar heart rate (HR: 80 bpm and 82 bpm) but different HRV (42 ms vs. 139 ms). Results indicate that the error in the mECG estimation is always small (<2.5 µV) but increases with HRV (ARec1: 0.87–1.65 µV; ARec2: 1.98–2.37 µV). In conclusion, the proposed algorithm based on the SBMM allows a clean mECG estimation from abdominal recordings thanks to a modulation procedure introduced to track physiological variation in the maternal heart rhythm.

Robustness of the Segmented-Beat Modulation Method to noise

Typically, ECG is corrupted by baseline wander (BW), electrode motion artifact (EM) and muscular artifact (MA). To eliminate them, ECG is usually pre-filtered by application of linear techniques which, however, do not remove in-band components which may limit the ECG clinical usefulness if further processing is not performed. The Segmented-Beat Modulation Method (SBMM) is a template-based filtering technique which segments each cardiac beat into QRS and TUP segments, respectively independent and proportional to heart-rate, and adaptively adjusts each reconstructed beat to its original length by modulating and demodulating the TUP segments. The aim of the present study was to evaluate SBMM robustness to noise by applying it to one synthetic and 18 clinical ECG tracings before and after corruption with BW, EM and MA. Results indicate that, in all cases, clean ECGs are estimated with errors <;0.15 mV, typically greater in the QRS than in the TUP segments (0-123 μV μV vs 0-25 μV; P<;10-5). Moreover, MA little affected ECG estimation, while BW and EM caused higher errors especially in the QRS segment which however remained quite small. Thus, the SBMM resulted to be a filtering technique quite robust to noise.

Use of the dominant T wave to enhance reliability of T-wave offset identification

T-wave offset (Toff) identification may be jeopardized by the presence of a significant inter-method (IMV) and inter-lead (ILV) Toff variability. Thus, the aim of the present study was to investigate if the dominant T wave (DTW) may be used to enhance Toff-identification reliability. DTWs and 15-lead ECG T waves of 46 control healthy subjects (CHS) and 103 acute myocardial infarction patients (AMIP) were analyzed for Toff identification using Zhang et al.s (M1) and Daskalov and Christovs (M2) methods. Results indicate that IMV is significantly reduced when identifying Toff from the DTW rather than from single ECG leads in both populations (CHS: 5ms vs. 5-15ms; AMIP: 10ms vs. 10-20ms). Moreover, when analyzing ILV, Toff was found to be equivalent (correlation=0.71-0.98; P<10(-14)) to the median Toff among leads, but required only one identification instead of 15. Thus, the DTW can be used to enhance Toff-identification reliability.

Noninvasive Fetal Electrocardiography Part I: Pan-Tompkins' Algorithm Adaptation to Fetal R-peak Identification

Background: Indirect fetal electrocardiography is preferable to direct fetal electrocardiography because of being noninvasive and is applicable also during the end of pregnancy, besides labor. Still, the former is strongly affected by noise so that even R-peak detection (which is essential for fetal heart-rate evaluations and subsequent processing procedures) is challenging. Some fetal studies have applied the Pan-Tompkins’ algorithm that, however, was originally designed for adult applications. Thus, this work evaluated the Pan-Tompkins’ algorithm suitability for fetal applications, and proposed fetal adjustments and optimizations to improve it. Method: Both Pan-Tompkins’ algorithm and its improved version were applied to the “Abdominal and Direct Fetal Electrocardiogram Database” and to the “Noninvasive Fetal Electrocardiography Database” of Physionet. R-peak detection accuracy was quantified by computation of positive-predictive value, sensitivity and F1 score. Results: When applied to “Abdominal and Direct Fetal Electrocardiogram Database”, the accuracy of the improved fetal Pan-Tompkins’ algorithm was significantly higher than the standard (positive-predictive value: 0.94 vs. 0.79; sensitivity: 0.95 vs. 0.80; F1 score: 0.94 vs. 0.79; P<0.05 in all cases) on indirect fetal electrocardiograms, whereas both methods performed similarly on direct fetal electrocardiograms (positive-predictive value, sensitivity and F1 score all close to 1). Improved fetal Pan-Tompkins’ algorithm was found to be superior to the standard also when applied to “Noninvasive Fetal Electrocardiography Database” (positive-predictive value: 0.68 vs. 0.55, P<0.05; sensitivity: 0.56 vs. 0.46, P=0.23; F1 score: 0.60 vs. 0.47, P=0.11). Conclusion: In indirect fetal electrocardiographic applications, improved fetal Pan-Tompkins’ algorithm is to be preferred over the standard, since it provides higher R-peak detection accuracy for heart-rate evaluations and subsequent processing.

Noninvasive Fetal Electrocardiography Part II: Segmented-Beat Modulation Method for Signal Denoising

Background: Fetal well-being evaluation may be accomplished by monitoring cardiac activity through fetal electrocardiography. Direct fetal electrocardiography (acquired through scalp electrodes) is the gold standard but its invasiveness limits its clinical applicability. Instead, clinical use of indirect fetal electrocardiography (acquired through abdominal electrodes) is limited by its poor signal quality. Objective: Aim of this study was to evaluate the suitability of the Segmented-Beat Modulation Method to denoise indirect fetal electrocardiograms in order to achieve a signal-quality at least comparable to the direct ones. Method: Direct and indirect recordings, simultaneously acquired from 5 pregnant women during labor, were filtered with the Segmented-Beat Modulation Method and correlated in order to assess their morphological correspondence. Signal-to-noise ratio was used to quantify their quality. Results: Amplitude was higher in direct than indirect fetal electrocardiograms (median:104 µV vs. 22 µV; P=7.66·10-4), whereas noise was comparable (median:70 µV vs. 49 µV, P=0.45). Moreover, fetal electrocardiogram amplitude was significantly higher than affecting noise in direct recording (P=3.17·10-2) and significantly in indirect recording (P=1.90·10-3). Consequently, signal-to-noise ratio was initially higher for direct than indirect recordings (median:3.3 dB vs. -2.3 dB; P=3.90·10-3), but became lower after denoising of indirect ones (median:9.6 dB; P=9.84·10-4). Eventually, direct and indirect recordings were highly correlated (median: ρ=0.78; P<10-208), indicating that the two electrocardiograms were morphologically equivalent. Conclusion: Segmented-Beat Modulation Method is particularly useful for denoising of indirect fetal electrocardiogram and may contribute to the spread of this noninvasive technique in the clinical practice.