Removal of electrocardiographic corruption from electromyographic signals using a localized wavelet based approach.

Introduction: Approximately 12,000 new cases of spinal cord injury (SCI) are reported each year in the US. Currently, the most widely used method of assessing the recovery of voluntary capability after spinal cord injury is the American Spinal Injury Association Impairment Scale (AIS). However, this test is not objective and is not sensitive enough to detect small activities. Recent studies have been using surface electromyography (EMG) to develop objective and sensitive spinal cord injury (SCI) characterization protocols. EMG recordings from the trunk muscles are contaminated with the electrical activity of the heart (ECG, electrocardiography). Depending on the level, spinal cord injury may disrupt the control of the trunk muscles, and EMG recordings from these muscles will be very weak compared to those in non-injured individuals. The elimination of ECG artifacts play critical role in precise evaluation of the trunk muscles in these individuals. While common global digital filters may generally remove some of the ECG corruption from the signal, these filters also remove or alter valuable EMG signal, which makes the physiological importance of these signals irrelevant. Methods: Local filtering approach was developed to remove this ECG noise, without significantly altering the EMG signal. The local filtering approach uses externally recorded ECG signals, in a separate lead configuration, as a mask to locate the area of ECG spikes within the noisy EMG signal. The areas of the signal containing the ECG noise are decomposed into 128 sub-wavelets using custom-scaled Morlet Wavelet Transform. Sub-wavelets pertaining to ECG within the signal at the ECG spike location are then removed, and the signal is reconstructed to create a clean EMG signal. This process is analytically tested for robustness and accuracy, using customized validation metrics, on simulated phantom signals. It is compared with a global Morlet Wavelet filter that does not localize its filtering process on the ECG spikes. Results: It was found that the localized filtering significantly reduced the Root-Mean-Squared (RMS) of the area of the signal containing ECG spikes. The Localized Filter also significantly reduced the error produced from removal of EMG signal in the areas outside of ECG spikes compared to global filter. The proposed local filter doesn’t degrade the signal, even at low ECG amplitudes (~60% improvement), compared to the global filter, which destroys the signal at this low amplitude ECG (-100% improvement). The proposed local filter is also far more efficient at removing larger amplitude ECG (more critical) than the global filter, which has a narrow range of signals that it can efficiently remove ECG. Hence, the proposed local filter is more robust and clinical-ready than the global filter. Conclusion: Proposed approach is far superior in terms of ECG removal accuracy, and introduction of artifact error from processing, compared to comparable global filter. It provides a mean to improve analysis of EMG signals as a tool to assess recovery from SCI

Deep learning for surface electromyography artifact contamination type detection

The quality of surface Electromyography (sEMG) signals could be an issue if highly contaminated by Power Line Interference (PLI), Electrocardiogram signal (ECG), Movement Artifact (MOA) or White Gaussian Noise (WGN), that could lead to unsafe operation of devices that is controlled by sEMG data, such as electro-mechanical prothesis. There are some mitigation methods proposed for some specifics sEMG contaminants and to use these methods in an efficient way is important to identify the contaminant in the sEMG signal. In this work we propose the use of a Recurrent Neural Network (RNN) using Long Short-Term Memory (LSTM) units in the hidden layer with no need of features extraction with the objective to classify the signal directly from sequences of the band-pass filtered data. The method proposed use the NinaPro database with amputee and non-amputee subjects. Only non-amputee subjects are used for parameters selection and then tested on both databases. The results show that 98% of the non-contaminated sEMG data was corrected classified and more than 95% of the contaminants were identified inside the training SNR range. Also, in this work is presented a SNR sensibility control and the contamination analysis in the range from −40 dB to 40 dB in 10 dB steps. The conclusion is that is possible to classify the contamination type in sEMG signals with a RNN-LSTM with a 112.5 ms time window and to predicted with a small error the classification hit rate for each SNR level in some cases

Towards respiratory muscle-protective mechanical ventilation in the critically ill: technology to monitor and assist physiology

Inadequate delivery of ventilatory assist and unphysiological respiratory drive may severely worsen respiratory muscle function in mechanically ventilated critically ill patients. Diaphragm weakness in these patients is exceedingly common (>60% of patients) and associated with poor clinical outcomes, including difficult ventilator liberation, increased risks of intensive care unit (ICU) and hospital readmission, and mortality. The underlying mechanisms of diaphragm dysfunction were extensively discussed in this thesis. Pathways primarily include the development of diaphragm disuse atrophy due to muscle inactivity or low respiratory drive (strong clinical evidence), and diaphragm injury as a result of excessive breathing effort due to insufficient ventilator assist or excessive respiratory drive (moderate evidence, mostly from experimental work). Excessive breathing effort may also worsen lung injury through pathways that include high lung stress and strain, pendelluft, increased lung perfusion, and patient-ventilator dyssynchrony. Relatively little attention has been paid to the effects of critical illness and mechanical ventilation on the expiratory muscles; however, dysfunction of these muscles has been linked to inadequate central airway clearance and extubation failure. The motivation for performing the work presented in this thesis was the hypothesis that maintaining physiological levels of respiratory muscle activity under mechanical ventilation could prevent or attenuate the development respiratory muscle weakness, and hence, improve patient outcomes. This strategy, integrated with lung-protective ventilation, was recently proposed by international experts from different professional societies (this thesis), and is referred to as a combined lung and diaphragm-protective ventilation approach. Today, an important barrier for implementing and evaluating such an approach is the lack of feasible, reliable and well-understood modalities to assess breathing effort at the bedside, as well as strategies for assisting and restoring respiratory muscle function during mechanical ventilation. Furthermore, monitoring breathing effort is crucial to identify potential relationships between patient management and detrimental respiratory (muscle) function that can be targeted to improve clinical outcomes. In this thesis we identified and improved monitoring modalities for the diaphragm (Part I), we investigated the impact of mechanical ventilation on the respiratory pump, especially the diaphragm (Part II), and we evaluated a novel strategy for maintaining expiratory muscle activity under mechanical ventilation (Part III)

Development of a Novel Dataset and Tools for Non-Invasive Fetal Electrocardiography Research

This PhD thesis presents the development of a novel open multi-modal dataset for advanced studies on fetal cardiological assessment, along with a set of signal processing tools for its exploitation. The Non-Invasive Fetal Electrocardiography (ECG) Analysis (NInFEA) dataset features multi-channel electrophysiological recordings characterized by high sampling frequency and digital resolution, maternal respiration signal, synchronized fetal trans-abdominal pulsed-wave Doppler (PWD) recordings and clinical annotations provided by expert clinicians at the time of the signal collection. To the best of our knowledge, there are no similar dataset available. The signal processing tools targeted both the PWD and the non-invasive fetal ECG, exploiting the recorded dataset. About the former, the study focuses on the processing aimed at the preparation of the signal for the automatic measurement of relevant morphological features, already adopted in the clinical practice for cardiac assessment. To this aim, a relevant step is the automatic identification of the complete and measurable cardiac cycles in the PWD videos: a rigorous methodology was deployed for the analysis of the different processing steps involved in the automatic delineation of the PWD envelope, then implementing different approaches for the supervised classification of the cardiac cycles, discriminating between complete and measurable vs. malformed or incomplete ones. Finally, preliminary measurement algorithms were also developed in order to extract clinically relevant parameters from the PWD. About the fetal ECG, this thesis concentrated on the systematic analysis of the adaptive filters performance for non-invasive fetal ECG extraction processing, identified as the reference tool throughout the thesis. Then, two studies are reported: one on the wavelet-based denoising of the extracted fetal ECG and another one on the fetal ECG quality assessment from the analysis of the raw abdominal recordings. Overall, the thesis represents an important milestone in the field, by promoting the open-data approach and introducing automated analysis tools that could be easily integrated in future medical devices

Wired, wireless and wearable bioinstrumentation for high-precision recording of bioelectrical signals in bidirectional neural interfaces

It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Furthermore, electrical stimulation of specific target brain regions has been shown to alleviate symptoms of neurological disorders, such as Parkinson’s disease, essential tremor, dystonia, epilepsy etc. In recent years, the traditional practice of continuously stimulating the brain using static stimulation parameters has shifted to the use of disease biomarkers to determine the intensity and timing of stimulation. The main motivation behind closed-loop stimulation is minimization of treatment side effects by providing only the necessary stimulation required within a certain period of time, as determined from a guiding biomarker. Hence, it is clear that high-quality recording of local field potentials (LFPs) or electrocorticographic (ECoG) signals during deep brain stimulation (DBS) is necessary to investigate the instantaneous brain response to stimulation, minimize time delays for closed-loop neurostimulation and maximise the available neural data. To our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording ECoG signals, which are of particular importance in closed-loop DBS and epilepsy DBS. In addition, existing recording systems lack the ability to provide artefact-free high-frequency (> 100 Hz) LFP recordings during DBS in real time primarily because of the contamination of the neural signals of interest by the stimulation artefacts. To address the problem of limited mobility often encountered by patients in the clinic and to provide a wide variety of high-precision sensor data to a closed-loop neurostimulation platform, a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless multi-instrument (55 × 80 mm2) was designed and developed. The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5 – 500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile tool to be utilized in a wide range of applications and environments. Moreover, in order to offer the capability of sensing and stimulating via the same electrode, novel real-time artefact suppression methods that could be used in bidirectional (recording and stimulation) system architectures are proposed and validated. More specifically, a novel, low-noise and versatile analog front-end (AFE), which uses a high-order (8th) analog Chebyshev notch filter to suppress the artefacts originating from the stimulation frequency, is presented. After defining the system requirements for concurrent LFP recording and DBS artefact suppression, the performance of the realised AFE is assessed by conducting both in vitro and in vivo experiments using unipolar and bipolar DBS (monophasic pulses, amplitude ranging from 3 to 6 V peak-to-peak, frequency 140 Hz and pulse width 100 µs). Under both in vitro and in vivo experimental conditions, the proposed AFE provided real-time, low-noise and artefact-free LFP recordings (in the frequency range 0.5 – 250 Hz) during stimulation. Finally, a family of tunable hardware filter designs and a novel method for real-time artefact suppression that enables wide-bandwidth biosignal recordings during stimulation are also presented. This work paves the way for the development of miniaturized research tools for closed-loop neuromodulation that use a wide variety of bioelectrical signals as control signals.Open Acces

Improving Maternal and Fetal Cardiac Monitoring Using Artificial Intelligence

Early diagnosis of possible risks in the physiological status of fetus and mother during pregnancy and delivery is critical and can reduce mortality and morbidity. For example, early detection of life-threatening congenital heart disease may increase survival rate and reduce morbidity while allowing parents to make informed decisions. To study cardiac function, a variety of signals are required to be collected. In practice, several heart monitoring methods, such as electrocardiogram (ECG) and photoplethysmography (PPG), are commonly performed. Although there are several methods for monitoring fetal and maternal health, research is currently underway to enhance the mobility, accuracy, automation, and noise resistance of these methods to be used extensively, even at home. Artificial Intelligence (AI) can help to design a precise and convenient monitoring system. To achieve the goals, the following objectives are defined in this research: The first step for a signal acquisition system is to obtain high-quality signals. As the first objective, a signal processing scheme is explored to improve the signal-to-noise ratio (SNR) of signals and extract the desired signal from a noisy one with negative SNR (i.e., power of noise is greater than signal). It is worth mentioning that ECG and PPG signals are sensitive to noise from a variety of sources, increasing the risk of misunderstanding and interfering with the diagnostic process. The noises typically arise from power line interference, white noise, electrode contact noise, muscle contraction, baseline wandering, instrument noise, motion artifacts, electrosurgical noise. Even a slight variation in the obtained ECG waveform can impair the understanding of the patient's heart condition and affect the treatment procedure. Recent solutions, such as adaptive and blind source separation (BSS) algorithms, still have drawbacks, such as the need for noise or desired signal model, tuning and calibration, and inefficiency when dealing with excessively noisy signals. Therefore, the final goal of this step is to develop a robust algorithm that can estimate noise, even when SNR is negative, using the BSS method and remove it based on an adaptive filter. The second objective is defined for monitoring maternal and fetal ECG. Previous methods that were non-invasive used maternal abdominal ECG (MECG) for extracting fetal ECG (FECG). These methods need to be calibrated to generalize well. In other words, for each new subject, a calibration with a trustable device is required, which makes it difficult and time-consuming. The calibration is also susceptible to errors. We explore deep learning (DL) models for domain mapping, such as Cycle-Consistent Adversarial Networks, to map MECG to fetal ECG (FECG) and vice versa. The advantages of the proposed DL method over state-of-the-art approaches, such as adaptive filters or blind source separation, are that the proposed method is generalized well on unseen subjects. Moreover, it does not need calibration and is not sensitive to the heart rate variability of mother and fetal; it can also handle low signal-to-noise ratio (SNR) conditions. Thirdly, AI-based system that can measure continuous systolic blood pressure (SBP) and diastolic blood pressure (DBP) with minimum electrode requirements is explored. The most common method of measuring blood pressure is using cuff-based equipment, which cannot monitor blood pressure continuously, requires calibration, and is difficult to use. Other solutions use a synchronized ECG and PPG combination, which is still inconvenient and challenging to synchronize. The proposed method overcomes those issues and only uses PPG signal, comparing to other solutions. Using only PPG for blood pressure is more convenient since it is only one electrode on the finger where its acquisition is more resilient against error due to movement. The fourth objective is to detect anomalies on FECG data. The requirement of thousands of manually annotated samples is a concern for state-of-the-art detection systems, especially for fetal ECG (FECG), where there are few publicly available FECG datasets annotated for each FECG beat. Therefore, we will utilize active learning and transfer-learning concept to train a FECG anomaly detection system with the least training samples and high accuracy. In this part, a model is trained for detecting ECG anomalies in adults. Later this model is trained to detect anomalies on FECG. We only select more influential samples from the training set for training, which leads to training with the least effort. Because of physician shortages and rural geography, pregnant women's ability to get prenatal care might be improved through remote monitoring, especially when access to prenatal care is limited. Increased compliance with prenatal treatment and linked care amongst various providers are two possible benefits of remote monitoring. If recorded signals are transmitted correctly, maternal and fetal remote monitoring can be effective. Therefore, the last objective is to design a compression algorithm that can compress signals (like ECG) with a higher ratio than state-of-the-art and perform decompression fast without distortion. The proposed compression is fast thanks to the time domain B-Spline approach, and compressed data can be used for visualization and monitoring without decompression owing to the B-spline properties. Moreover, the stochastic optimization is designed to retain the signal quality and does not distort signal for diagnosis purposes while having a high compression ratio. In summary, components for creating an end-to-end system for day-to-day maternal and fetal cardiac monitoring can be envisioned as a mix of all tasks listed above. PPG and ECG recorded from the mother can be denoised using deconvolution strategy. Then, compression can be employed for transmitting signal. The trained CycleGAN model can be used for extracting FECG from MECG. Then, trained model using active transfer learning can detect anomaly on both MECG and FECG. Simultaneously, maternal BP is retrieved from the PPG signal. This information can be used for monitoring the cardiac status of mother and fetus, and also can be used for filling reports such as partogram

Non-invasive fetal electrocardiogram : analysis and interpretation

High-risk pregnancies are becoming more and more prevalent because of the progressively higher age at which women get pregnant. Nowadays about twenty percent of all pregnancies are complicated to some degree, for instance because of preterm delivery, fetal oxygen deficiency, fetal growth restriction, or hypertension. Early detection of these complications is critical to permit timely medical intervention, but is hampered by strong limitations of existing monitoring technology. This technology is either only applicable in hospital settings, is obtrusive, or is incapable of providing, in a robust way, reliable information for diagnosis of the well-being of the fetus. The most prominent method for monitoring of the fetal health condition is monitoring of heart rate variability in response to activity of the uterus (cardiotocography; CTG). Generally, in obstetrical practice, the heart rate is determined in either of two ways: unobtrusively with a (Doppler) ultrasound probe on the maternal abdomen, or obtrusively with an invasive electrode fixed onto the fetal scalp. The first method is relatively inaccurate but is non-invasive and applicable in all stages of pregnancy. The latter method is far more accurate but can only be applied following rupture of the membranes and sufficient dilatation, restricting its applicability to only the very last phase of pregnancy. Besides these accuracy and applicability issues, the use of CTG in obstetrical practice also has another limitation: despite its high sensitivity, the specificity of CTG is relatively low. This means that in most cases of fetal distress the CTG reveals specific patterns of heart rate variability, but that these specific patterns can also be encountered for healthy fetuses, complicating accurate diagnosis of the fetal condition. Hence, a prerequisite for preventing unnecessary interventions that are based on CTG alone, is the inclusion of additional information in diagnostics. Monitoring of the fetal electrocardiogram (ECG), as a supplement of CTG, has been demonstrated to have added value for monitoring of the fetal health condition. Unfortunately the application of the fetal ECG in obstetrical diagnostics is limited because at present the fetal ECG can only be measured reliably by means of an invasive scalp electrode. To overcome this limited applicability, many attempts have been made to record the fetal ECG non-invasively from the maternal abdomen, but these attempts have not yet led to approaches that permit widespread clinical application. One key difficulty is that the signal to noise ratio (SNR) of the transabdominal ECG recordings is relatively low. Perhaps even more importantly, the abdominal ECG recordings yield ECG signals for which the morphology depends strongly on the orientation of the fetus within the maternal uterus. Accordingly, for any fetal orientation, the ECG morphology is different. This renders correct clinical interpretation of the recorded ECG signals complicated, if not impossible. This thesis aims to address these difficulties and to provide new contributions on the clinical interpretation of the fetal ECG. At first the SNR of the recorded signals is enhanced through a series of signal processing steps that exploit specific and a priori known properties of the fetal ECG. More particularly, the dominant interference (i.e. the maternal ECG) is suppressed by exploiting the absence of temporal correlation between the maternal and fetal ECG. In this suppression, the maternal ECG complex is dynamically segmented into individual ECG waves and each of these waves is estimated through averaging corresponding waves from preceding ECG complexes. The maternal ECG template generated by combining the estimated waves is subsequently subtracted from the original signal to yield a non-invasive recording in which the maternal ECG has been suppressed. This suppression method is demonstrated to be more accurate than existing methods. Other interferences and noise are (partly) suppressed by exploiting the quasiperiodicity of the fetal ECG through averaging consecutive ECG complexes or by exploiting the spatial correlation of the ECG. The averaging of several consecutive ECG complexes, synchronized on their QRS complex, enhances the SNR of the ECG but also can suppress morphological variations in the ECG that are clinically relevant. The number of ECG complexes included in the average hence constitutes a trade-off between SNR enhancement on the one hand and loss of morphological variability on the other hand. To relax this trade-off, in this thesis a method is presented that can adaptively estimate the number of ECG complexes included in the average. In cases of morphological variations, this number is decreased ensuring that the variations are not suppressed. In cases of no morphological variability, this number is increased to ensure adequate SNR enhancement. The further suppression of noise by exploiting the spatial correlation of the ECG is based on the fact that all ECG signals recorded at several locations on the maternal abdomen originate from the same electrical source, namely the fetal heart. The electrical activity of the fetal heart at any point in time can be modeled as a single electrical field vector with stationary origin. This vector varies in both amplitude and orientation in three-dimensional space during the cardiac cycle and the time-path described by this vector is referred to as the fetal vectorcardiogram (VCG). In this model, the abdominal ECG constitutes the projection of the VCG onto the vector that describes the position of the abdominal electrode with respect to a reference electrode. This means that when the VCG is known, any desired ECG signal can be calculated. Equivalently, this also means that when enough ECG signals (i.e. at least three independent signals) are known, the VCG can be calculated. By using more than three ECG signals for the calculation of the VCG, redundancy in the ECG signals can be exploited for added noise suppression. Unfortunately, when calculating the fetal VCG from the ECG signals recorded from the maternal abdomen, the distance between the fetal heart and the electrodes is not the same for each electrode. Because the amplitude of the ECG signals decreases with propagation to the abdominal surface, these different distances yield a specific, unknown attenuation for each ECG signal. Existing methods for estimating the VCG operate with a fixed linear combination of the ECG signals and, hence, cannot account for variations in signal attenuation. To overcome this problem and be able to account for fetal movement, in this thesis a method is presented that estimates both the VCG and, to some extent, also the signal attenuation. This is done by determining for which VCG and signal attenuation the joint probability over both these variables is maximal given the observed ECG signals. The underlying joint probability distribution is determined by assuming the ECG signals to originate from scaled VCG projections and additive noise. With this method, a VCG, tailored to each specific patient, is determined. With respect to the fixed linear combinations, the presented method performs significantly better in the accurate estimation of the VCG. Besides describing the electrical activity of the fetal heart in three dimensions, the fetal VCG also provides a framework to account for the fetal orientation in the uterus. This framework enables the detection of the fetal orientation over time and allows for rotating the fetal VCG towards a prescribed orientation. From the normalized fetal VCG obtained in this manner, standardized ECG signals can be calculated, facilitating correct clinical interpretation of the non-invasive fetal ECG signals. The potential of the presented approach (i.e. the combination of all methods described above) is illustrated for three different clinical cases. In the first case, the fetal ECG is analyzed to demonstrate that the electrical behavior of the fetal heart differs significantly from the adult heart. In fact, this difference is so substantial that diagnostics based on the fetal ECG should be based on different guidelines than those for adult ECG diagnostics. In the second case, the fetal ECG is used to visualize the origin of fetal supraventricular extrasystoles and the results suggest that the fetal ECG might in future serve as diagnostic tool for relating fetal arrhythmia to congenital heart diseases. In the last case, the non-invasive fetal ECG is compared to the invasively recorded fetal ECG to gauge the SNR of the transabdominal recordings and to demonstrate the suitability of the non-invasive fetal ECG in clinical applications that, as yet, are only possible for the invasive fetal ECG

Algorithms and systems for home telemonitoring in biomedical applications

During the past decades, the interest of the healthcare community shifted from the simple treatment of the diseases towards the prevention and maintenance of a healthy lifestyle. This approach is associated to a reduced cost for the Health Systems, having to face the constantly increased expenditures due to the reduced mortality for chronical diseases and to the progressive population ageing. Nevertheless, the high costs related to hospitalization of patients for monitoring procedures that could be better performed at home hamper the full implementation of this approach in a traditional way. Information and Communication Technology can provide a solution to implement a care model closer to the patient, crossing the physical boundaries of the hospitals and thus allowing to reach also those patients that, for a geographical or social condition, could not access the health services as other luckier subjects. This is the case of telemonitoring systems, whose aim is that of providing monitoring services for some health-related parameters at a distance, by means of custom-designed electronic devices. In this thesis, the specific issues associated to two telemonitoring applications are presented, along with the proposed solutions and the achieved results. The first telemonitoring application considered is the fetal electrocardiography. Non-invasive fetal electrocardiography is the recording of the fetal heart electrical activity using electrodes placed on the maternal abdomen. It can provide important diagnostic parameters, such as the beat-to-beat heart rate variability, whose recurring analysis would be useful in assessing and monitoring fetal health during pregnancy. Long term electrocardiographic monitoring is sustained by the absence of any collateral effects for both the mother and the fetus. This application has been tackled from several perspectives, mainly acquisition and processing. From the acquisition viewpoint a study on different skin treatments, disposable commercial electrodes and textile electrodes has been performed with the aim of improving the signal acquisition quality, while simplifying the measurement setup. From the processing viewpoint, different algorithms have been developed to allow extracting the fetal ECG heart rate, starting from an on-line ICA algorithm or exploiting a subtractive approach to work on recordings acquired with a reduced number of electrodes. The latter, took part to the international "Physionet/Computing in Cardiology Challenge" in 2013 entering into the top ten best-performing open-source algorithms. The improved version of this algorithm is also presented, which would mark the 5th and 4th position in the final ranking related to the fetal heart rate and fetal RR interval measurements performance, reserved to the open-source challenge entries, taking into account both official and unofficial entrants. The research in this field has been carried out in collaboration with the Pediatric Cardiology Unit of the Hospital G. Brotzu in Cagliari, for the acquisition of non-invasive fetal ECG signals from pregnant voluntary patients. The second telemonitoring application considered is the telerehabilitation of the hand. The execution of rehabilitation exercises has been proven to be effective in recovering hand functionality in a wide variety of invalidating diseases, but the lack of standardization and continuous medical control cause the patients neglecting this therapeutic procedures. Telemonitoring the rehabilitation sessions would allow the physician to closely follow the patients' progresses and compliance to the prescribed adapted exercises. This application leads to the development of a sensorized telerehabilitation system for the execution and objective monitoring of therapeutic exercises at the patients' home and of the telemedicine infrastructure that give the physician the opportunity to monitor patients' progresses through parameters summarizing the patients' performance. The proposed non-CE marked medical device, patent pending, underwent a clinical trial, reviewed and approved by the Italian Public Health Department, involving 20 patients with Rheumatoid Arthritis and 20 with Systemic Sclerosis randomly assigned to the experimental or the control arm, enrolled for 12 weeks in a home rehabilitation program. The trial, carried out with the collaboration of the Rheumatology Department of the Policlinico Universitario of Cagliari, revealed promising results in terms of hand functionality recovering, highlighting greater improvements for the patients enrolled in the experimental arm, that use the proposed telerehabilitation system, with respect to those of the control arm, which perform similar rehabilitation exercises using common objects

Extracting ECG-based cardiac information from the upper arm

Cardiovascular disease (CVD) is the global number one cause of death. Therefore, there is an acute need for constantly monitoring cardiac conditions and/or cardiac monitoring for extended periods. The current clinical Electrocardiogram (ECG) recording systems require precise placement of electrodes on the patient’s body, often performed by trained medical professionals. These systems also have long wires that require repeated disinfection and can be easily tangled and interfered with clothing and garment. These limitations have severely restricted the possible application scenarios of ECG systems. To overcome these limitations, there is a need for wearable ECG devices with minimal wires to detect possible cardiac abnormalities with minimal intervention from healthcare professionals. Previous research on this topic has focused on extracting cardiac information from the body surface by investigating various electrode placements and developing ECG processing algorithms. Building on these studies, it is possible to develop devices and algorithms that can extract ECG-related information without the need for precise electrode placements on the body's surface. The present thesis aims to extract ECG-based cardiac information using signals recorded from the upper arm. Far-field ECG is prone to contamination by artifacts such as Electromyogram (EMG), which greatly reduces its clinical value. The current study examines how various state-of-the-art heartbeat detection algorithms perform in four levels of simulated EMG artifacts. The simulated EMG was added to Lead II from two different datasets: the MIT-BIH arrhythmia dataset (Dataset 1) and data we collected from 20 healthy participants (Dataset 2). Results show that Stationary Wavelet Transform (SWT) provided the most robust features against EMG intensity level increment among various algorithms. The next step involved recording bio-potential signals using a high-density bio-potential amplification system attached to the upper arm. The system used three high-density electrodes, each with 64 channels, in addition to the standard Lead II. Twenty participants, reported healthy, were asked to perform two tasks: Rest and Elbow Flexion (EF): holding three weights (C1: 1.2 kg, C2: 2.2 kg, and C3: 3.6 kg). The tasks were repeated 2 and 3 times, respectively. Firstly, I identified optimal electrode locations on the upper arm for each task. I then generated a synthesized ECG using the selected electrodes with generalized weights over subjects and trials. Considering the robustness of SWT to EMG intensity level increment, I next focused on optimizing SWT by addressing two of its drawbacks: introducing phase shift and the requirement of a pre-defined mother wavelet. Regarding the first drawback, zero-phase wavelet (Zephlet) was implemented to replace SWT filters with zero-phase filters for the matter of feature extraction from the synthesized ECG. Next, I incorporated the synchronized extracted features with a Multiagent Detection Scheme (MDS) for the means of heartbeat detection. The F1-score for the heartbeat detection was 0.94 ± 0.16, 0.86 ± 0.22, 0.79 ± 0.26, and 0.67 ± 0.31 for Rest and EF with three different levels of muscle contraction (C1 to C3), respectively. Changing the acceptable distance between the detected and actual heartbeats from 50 ms to 20 ms, the F1-score changed to 0.81 ± 0.20, 0.66 ± 0.26, 0.57 ± 0.26, and 0.44 ± 0.26 for Rest and C1 to C3, respectively. Regarding the second drawback, Lattice parametrization was used to optimize the mother wavelet for the means of PQRST delineation. The mother wavelet was generalized over subjects, trials, and tasks. The Pearson’s Correlation Coefficient (CC) between the averaged delineated PQRST from analyzing feature and the averaged PQRST from Lead II using this generalized mother wavelet was 0.88 ± 0.05, 0.85 ± 0.08, 0.83± 0.11, and 0.81 ± 0.12 for Rest and C1-C3, respectively. This thesis makes several contributions to the current literature. It introduces locations on the upper arm that can be used to place sensors in a wearable to capture cardiac activity with robustness across intra-subject, inter-subject and inter-contraction variabilities. It also identifies a robust method against noise increment for heartbeat detection. Zephlet was implemented for the first time that can replace SWT in many applications in which there is a need for synchrony with respect to the original signal or among components. And finally, this thesis introduces a generalized mother wavelet that can be used to extract PQRST and enhance SNR in many applications, such as ECG waveform extraction, arrhythmia detection, and denoising