Corrado Giuliani

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.

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.

Abnormal Repolarization in the Acute Myocardial Infarction Patients: A Frequency-Based Characterization

Despite ST elevation having poor sensitivity for acute myocardial infarction (AMI), it remains the main electrocardiographic (ECG) repolarization index for AMI diagnosis. Aim of the present study was to propose a new f99 index, defined as the frequency at which the repolarization normalized cumulative energy reaches 99%, for ECG AMI discrimination from health with good sensitivity and good specificity. Evaluation of such f99 index was performed on 12-standard-lead (I, II, III, aV1, aVr, aVf, V1 to V6) ECG recordings of 47 healthy controls and 108 acute myocardial infarction (AMI) patients. Repolarization dispersion caused f99 distributions to be significantly lead dependent. In most leads (leads I, II, aVl, aVr, V2-V6), f99 median value was lower in the healthy controls (10-17 Hz) than in the AMI patients (12-38 Hz) indicating higher frequency components (i.e. a more fragmented repolarization) in the latter population. AMI patients from healthy controls discrimination by f99, evaluated in terms of sensitivity (Se) and specificity (Sp), was also lead dependent. Single-lead analysis indicated leads I (Se=80%, Sp=77%) and aVl (Se=84%, Sp=74%) as optimal. Instead, lead-system analysis, performed to overcome dispersion issues, provided the best results when averaging over the 6 precordial leads (Se= 81% and Sp=74%). In conclusion, our new f99 index appears as a promising tool for non-invasively and reliably discriminate AMI patients from healthy subjects.

Predictive Power of f99 Repolarization Index for the Occurrence of Ventricular Arrhythmias

Defects of cardiac repolarization, noninvasively identifiable by analyzing the electrocardiographic (ECG) ST segment and T wave, are among the major causes of sudden cardiac death. Still, no repolarization‐based index has so far shown sufficient sensitivity and specificity to justify preventive treatments. Thus, the aim of this work was to evaluate the predictive power of our recently proposed f99 index for the occurrence of ventricular arrhythmias.

Automatic Identification of the Repolarization Endpoint by Computing the Dominant T-wave on a Reduced Number of Leads

Electrocardiographic (ECG) T-wave endpoint (Tend) identification suffers lack of reliability due to the presence of noise and variability among leads. Tend identification can be improved by using global repolarization waveforms obtained by combining several leads. The dominant T-wave (DTW) is a global repolarization waveform that proved to improve Tend identification when computed using the 15 (I to III, aVr, aVl, aVf, V1 to V6, X, Y, Z) leads usually available in clinics, of which only 8 (I, II, V1 to V6) are independent. The aim of the present study was to evaluate if the 8 independent leads are sufficient to obtain a DTW which allows a reliable Tend identification. To this aim Tend measures automatically identified from 15-dependent-lead DTWs of 46 control healthy subjects (CHS) and 103 acute myocardial infarction patients (AMIP) were compared with those obtained from 8-independent-lead DTWs. Results indicate that Tend distributions have not statistically different median values (CHS: 340 ms vs. 340 ms, respectively; AMIP: 325 ms vs. 320 ms, respectively), besides being strongly correlated (CHS: ρ=0.97, AMIP: 0.88; P<10-27). Thus, measuring Tend from the 15-dependent-lead DTWs is statistically equivalent to measuring Tend from the 8-independent-lead DTWs. In conclusion, for the clinical purpose of automatic Tend identification from DTW, the 8 independent leads can be used without a statistically significant loss of accuracy but with a significant decrement of computational effort. The lead dependence of 7 out of 15 leads does not introduce a significant bias in the Tend determination from 15 dependent lead DTWs.

Health Monitoring in Sport Through Wearable Sensors: A Novel Approach Based on Heart-Rate Variability

Sudden cardiac death (SCD) is one of the leading cause of death during sport activities. Heart rate (HR) and HR variability (HRV) provide a measure of how the organism adapts to physical fatigue, and can be monitored by commercial wearable sensors. Still, HR and HRV, widely used to optimize a training session, were rarely used to evaluate the athlete’s health-status, even though widely known to provide indexes of risk for SCD. This work, developed in collaboration with Bio-Medical Engineering Development Srl, aims to provide a contribution to the problem of preventive identification of athletes at increased risk of SCD, by developing and testing a low-cost, large-scale procedure for HR and HRV monitoring from signals obtained using comfortable wearable sensors. To this aim a new protocol for the acquisition of the tachogram was proposed. It included recordings of the signals during resting, exercise and recovery phases, to allow evaluation of prevention as well as performance indexes. The procedure was tested on 10 sedentary subjects (SS) and 10 amateur athletes (AA). Compared to SS, AA showed a better health-status, quantified in a lower resting HR (63 bpm vs. 73 bpm; P < 0.005) and a higher resting HRV (29 ms vs. 23 ms; P < 0.05), and a better performance level, quantifies in a lower recovery time (130 ms vs. 174 ms; P < 0.05). Thus, the proposed procedure allows evaluation of both the health-status and the performance level of an athlete, and represents a valuable tool to contrast SCD in sport.