Magnetic Resonance Imaging (MRI) Analysis of Tissue Sodium Concentration in Chronic Kidney Disease.

Human body sodium is regulated by the kidneys and extrarenal mechanisms. Stored skin and muscle tissue sodium accumulation is associated with kidney function decline, hypertension, and a pro-inflammatory and cardiovascular disease profile. In this chapter, we describe the use of sodium-hydrogen magnetic resonance imaging (23Na/1H MRI) to dynamically quantify tissue sodium concentration in the lower limb of humans. Real-time quantification of tissue sodium is calibrated against known sodium chloride aqueous concentrations. This method may be useful for investigating in vivo (patho-)physiological conditions associated with tissue sodium deposition and metabolism (including in relation to water regulation) to enlighten our understanding of sodium physiology.

eCTG: an automatic procedure to extract digital cardiotocographic signals from digital images.

BACKGROUND AND OBJECTIVE: Cardiotocography (CTG), consisting in the simultaneous recording of fetal heart rate (FHR) and maternal uterine contractions (UC), is a popular clinical test to assess fetal health status. Typically, CTG machines provide paper reports that are visually interpreted by clinicians. Consequently, visual CTG interpretation depends on clinician's experience and has a poor reproducibility. The lack of databases containing digital CTG signals has limited number and importance of retrospective studies finalized to set up procedures for automatic CTG analysis that could contrast visual CTG interpretation subjectivity. In order to help overcoming this problem, this study proposes an electronic procedure, termed eCTG, to extract digital CTG signals from digital CTG images, possibly obtainable by scanning paper CTG reports. METHODS: eCTG was specifically designed to extract digital CTG signals from digital CTG images. It includes four main steps: pre-processing, Otsu's global thresholding, signal extraction and signal calibration. Its validation was performed by means of the "CTU-UHB Intrapartum Cardiotocography Database" by Physionet, that contains digital signals of 552 CTG recordings. Using MATLAB, each signal was plotted and saved as a digital image that was then submitted to eCTG. Digital CTG signals extracted by eCTG were eventually compared to corresponding signals directly available in the database. Comparison occurred in terms of signal similarity (evaluated by the correlation coefficient ρ, and the mean signal error MSE) and clinical features (including FHR baseline and variability; number, amplitude and duration of tachycardia, bradycardia, acceleration and deceleration episodes; number of early, variable, late and prolonged decelerations; and UC number, amplitude, duration and period). RESULTS: The value of ρ between eCTG and reference signals was 0.85 (P < 10-560) for FHR and 0.97 (P < 10-560) for UC. On average, MSE value was 0.00 for both FHR and UC. No CTG feature was found significantly different when measured in eCTG vs. reference signals. CONCLUSIONS: eCTG procedure is a promising useful tool to accurately extract digital FHR and UC signals from digital CTG images.

CTG Analyzer: A graphical user interface for cardiotocography.

Cardiotocography (CTG) is the most commonly used test for establishing the good health of the fetus during pregnancy and labor. CTG consists in the recording of fetal heart rate (FHR; bpm) and maternal uterine contractions (UC; mmHg). FHR is characterized by baseline, baseline variability, tachycardia, bradycardia, acceleration and decelerations. Instead, UC signal is characterized by presence of contractions and contractions period. Such parameters are usually evaluated by visual inspection. However, visual analysis of CTG recordings has a well-demonstrated poor reproducibility, due to the complexity of physiological phenomena affecting fetal heart rhythm and being related to clinician's experience. Computerized tools in support of clinicians represents a possible solution for improving correctness in CTG interpretation. This paper proposes CTG Analyzer as a graphical tool for automatic and objective analysis of CTG tracings. CTG Analyzer was developed under MATLAB®; it is a very intuitive and user friendly graphical user interface. FHR time series and UC signal are represented one under the other, on a grid with reference lines, as usually done for CTG reports printed on paper. Colors help identification of FHR and UC features. Automatic analysis is based on some unchangeable features definitions provided by the FIGO guidelines, and other arbitrary settings whose default values can be changed by the user. Eventually, CTG Analyzer provides a report file listing all the quantitative results of the analysis. Thus, CTG Analyzer represents a potentially useful graphical tool for automatic and objective analysis of CTG tracings.

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.

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.

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 patient's 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.

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

BACKGROUND: 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. METHODS: Our study populations included 170 patients with implanted cardiac defibrillator (ICD), 44 of which developed ventricular tachycardia and/or fibrillation during the 4-year follow-up (ICD_Cases) and 126 did not (ICD_Controls). The f99 index, defined as the frequency at which the repolarization normalized cumulative energy reaches 99%, was computed in each of the 15 (I to III, aVl, aVr, aVf, V1 -V6 , X, Y, Z) available ECG leads independently, and then maximized over the 6 precordial leads (f99_MaxV1 -V6 ), 12 standard leads (f99_Max12STD) and three orthogonal leads (f99_MaxXYZ) to avoid dispersion-related issues. Each index predictive power was quantified as the area under the receiving operating characteristic curve (AUC). RESULTS: Median f99_MaxV1 -V6 , f99_Max12STD and f99_MaxXYZ values were significantly higher in the ICD_Cases than in the ICD_Controls (48 Hz vs. 35 Hz, P<0.05; 51 Hz vs. 43 Hz, P<0.05; 45 Hz vs. 31 Hz, P<10(-3) ; respectively), indicating a more fragmented repolarization in the former group. The AUC values were 0.62, 0.63 and 0.68, respectively. CONCLUSIONS: The f99 represents a promising risk index for the occurrence of ventricular arrhythmias, especially when maximized over the three orthogonal leads.

Evaluation of the low-frequency components in surface electromyography.

The surface electromyogram (SEMG) is a signal noninvasively (through electrodes located on the body surface) acquired for evaluating the electrical activity produced by skeletal muscles. In thoracic acquisitions, SEMG is typically affected by the electrocardiographic (ECG) signal, representing the electrical activity of the heart. SEMG and ECG power spectra mainly fall within 5-450 Hz and 0.05-50 Hz, respectively. Consequently, SEMG and ECG components overlap in the 5-50 Hz range. Although removing SEMG low spectral components, high-pass linear filtering (LF) with a cut-off frequency of 20 Hz remains the standard technique to clean SEMG from ECG. Thus, the aim of the present study was to propose the Segmented Beat Modulation Method (SBMM) as a tool to clean SEMG from ECG without removing SEMG components below 20 Hz. A SEMG recording was first acquired from the left rectus abdominis of a subject, and then filtered using both SBMM and LF. Filtered SEMGs obtained with the two techniques were compared. Results indicate that SBMM eliminates ECG interference from SEMG better than LF, since the latter procedure maintains ECG components between 20 and 50 Hz. In addition, after ECG removal by SBMM, SEMG showed a significant amount of spectral components (up to 20%) in the low-frequency range. Maintaining such low-frequency components, which go lost when applying LF, is desirable since they likely associate to the firing rates of the active motor units. In conclusion, SBMM represents a useful tool to clean SEMG from ECG without loss of frequency components.

Co-activation periods of gastrocnemius and vastus lateralis during walking evaluated by surface electromyography.

"In vivo" studies reported that the co-activation of gastrocnemius and quadriceps femoris (QF) muscles produces ACL strain values greater than those caused by an isolated activation of either muscle. Aim of this study was to assess the co-activation of gastrocnemius (lateral head, GL) and vastus lateralis (VL) in healthy and young adults during walking. To this purpose the Statistical Gait Analysis was performed, that allows a characterization of gait considering hundreds of strides belonging to the same walking trial. Three GL/VL co-activations were detected during a single gait cycle: in foot-contact phase, from 6.8±8.5% to 22.9±23.3% of gait cycle, (FC co-activation), in push-off phase, from 33.0±11.9% to 41.5±13.4% (PO co-activation), and in swing phase, from 86.5±6.7% to 93.2±5.9% (SW co-activation). FC co-activation was the most recurrent (in 100% of the strides, P<;0.001) and longest (16% of gait cycle) one. Thus, the ACL strain due to the co-activation between GL and VL is longer and more frequently during FC phase, than in all the others gait phases. Moreover, the position of the knee and the amount of the weight-bearing on knee, achieved in this gait phase, suggested that FC co-activation is the one that produces a highest strain value of anterior cruciate ligament (ACL). These findings could help to better understand risk factors of the ACL injuries and to design more focused preventive and rehabilitative strategies.

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

BACKGROUND: 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. METHODS: The fetus clinical status is inferred by analyzing growth parameters, supraventricular arrhythmias, ST-segment variability, and fetal-movement parameters from the fECG signal. This can be extracted from an abdominal recording obtained using one of the following two electrode-types configurations: pure-abdominal and mixed. Differently from the former, the latter also provides pure maternal ECG tracings. From a mathematical point of view, the abdominal recording is a summation of three signal components: the fECG signal (i.e., the signal of interest to be extracted), the abdominal maternal ECG (amECG), and the noise. Automatic extraction of fECG includes noise removal by abdominal signal prefiltration (0.5-45 Hz bandpass filter) and amECG cancellation. CONCLUSIONS: Differences among methods rely on different techniques used to extract fECG. If pure abdominal electrode configurations are used, fECG is extracted directly from the abdominal recording using independent component analysis or template subtraction. Eventually, if mixed electrode configurations are used, the fECG can be extracted using the adaptive filtering fed with the maternal ECG recorded by the electrodes located in the woman thorax or shoulder.

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.

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 Christov's (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.