Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Advanced Signal Processing in Wearable Sensors for Health Monitoring

Smart, wearables devices on a miniature scale are becoming increasingly widely available, typically in the form of smart watches and other connected devices. Consequently, devices to assist in measurements such as electroencephalography (EEG), electrocardiogram (ECG), electromyography (EMG), blood pressure (BP), photoplethysmography (PPG), heart rhythm, respiration rate, apnoea, and motion detection are becoming more available, and play a significant role in healthcare monitoring. The industry is placing great emphasis on making these devices and technologies available on smart devices such as phones and watches. Such measurements are clinically and scientifically useful for real-time monitoring, long-term care, and diagnosis and therapeutic techniques. However, a pertaining issue is that recorded data are usually noisy, contain many artefacts, and are affected by external factors such as movements and physical conditions. In order to obtain accurate and meaningful indicators, the signal has to be processed and conditioned such that the measurements are accurate and free from noise and disturbances. In this context, many researchers have utilized recent technological advances in wearable sensors and signal processing to develop smart and accurate wearable devices for clinical applications. The processing and analysis of physiological signals is a key issue for these smart wearable devices. Consequently, ongoing work in this field of study includes research on filtration, quality checking, signal transformation and decomposition, feature extraction and, most recently, machine learning-based methods

Signal Processing Of An Ecg Signal In The Presence Of A Strong Static Magnetic Field

This dissertation addresses the problem of elevation of the T wave of an electrocardiogram (ECG) signal in the magnetic resonance imaging (MRI). In the MRI, due to the strong static magnetic field the interaction of the blood flow with this strong magnetic field induces a voltage in the body. This voltage appears as a superimposition at the locus of the T wave of the ECG signal. This looses important information required by the doctors to interpret the ST segment of the ECG and detect diseases such as myocardial infarction. This dissertation aims at finding a solution to the problem of elevation of the T wave of an ECG signal in the MRI. The first step is to simulate the entire situation and obtain the magnetic field dependent T wave elevation. This is achieved by building a model of the aorta and simulating the blood flow in it. This model is then subjected to a static magnetic field and the surface potential on the thorax is measured to observe the T wave elevation. The various parameters on which the T wave elevation is dependent are then analyzed. Different approaches are used to reduce this T wave elevation problem. The direct approach aims at computing the magnitude of T wave elevation using magneto-hydro-dynamic equations. The indirect approach uses digital signal processing tools like the least mean square adaptive filter to remove the T wave elevation and obtain artifact free ECG signal in the MRI. Excellent results are obtained from the simulation model. The model perfectly simulates the ECG signal in the MRI at all the 12 leads of the ECG. These results are compared with ECG signals measured in the MRI. A simulation package is developed in MATLAB based on the simulation model. This package is a graphical user interface allowing the user to change the strength of magnetic field, the radius of the aorta and the orientation of the aorta with respect to the heart and observe the ECG signals with the elevation at the 12 leads of the ECG. Also the artifacts introduced due to the magnetic field can be removed by the least mean square adaptive filter. The filter adapts the ECG signal in the MRI to the ECG signal of the patient outside the MRI. Before the adaptation, the heart rate of the ECG outside the MRI is matched to the ECG in the MRI by interpolation or decimation. The adaptive filter works excellently to remove the T wave artifacts. When the cardiac output of the patient changes, the simulation model is used along with the adaptive filter to obtain the artifact free ECG signal

Photonic Biosensors: Detection, Analysis and Medical Diagnostics

The role of nanotechnologies in personalized medicine is rising remarkably in the last decade because of the ability of these new sensing systems to diagnose diseases from early stages and the availability of continuous screenings to characterize the efficiency of drugs and therapies for each single patient. Recent technological advancements are allowing the development of biosensors in low-cost and user-friendly platforms, thereby overcoming the last obstacle for these systems, represented by limiting costs and low yield, until now. In this context, photonic biosensors represent one of the main emerging sensing modalities because of their ability to combine high sensitivity and selectivity together with real-time operation, integrability, and compatibility with microfluidics and electric circuitry for the readout, which is fundamental for the realization of lab-on-chip systems. This book, “Photonic Biosensors: Detection, Analysis and Medical Diagnostics”, has been published thanks to the contributions of the authors and collects research articles, the content of which is expected to assume an important role in the outbreak of biosensors in the biomedical field, considering the variety of the topics that it covers, from the improvement of sensors’ performance to new, emerging applications and strategies for on-chip integrability, aiming at providing a general overview for readers on the current advancements in the biosensing field

Camera-Based Heart Rate Extraction in Noisy Environments

Remote photoplethysmography (rPPG) is a non-invasive technique that benefits from video to measure vital signs such as the heart rate (HR). In rPPG estimation, noise can introduce artifacts that distort rPPG signal and jeopardize accurate HR measurement. Considering that most rPPG studies occurred in lab-controlled environments, the issue of noise in realistic conditions remains open. This thesis aims to examine the challenges of noise in rPPG estimation in realistic scenarios, specifically investigating the effect of noise arising from illumination variation and motion artifacts on the predicted rPPG HR. To mitigate the impact of noise, a modular rPPG measurement framework, comprising data preprocessing, region of interest, signal extraction, preparation, processing, and HR extraction is developed. The proposed pipeline is tested on the LGI-PPGI-Face-Video-Database public dataset, hosting four different candidates and real-life scenarios. In the RoI module, raw rPPG signals were extracted from the dataset using three machine learning-based face detectors, namely Haarcascade, Dlib, and MediaPipe, in parallel. Subsequently, the collected signals underwent preprocessing, independent component analysis, denoising, and frequency domain conversion for peak detection. Overall, the Dlib face detector leads to the most successful HR for the majority of scenarios. In 50% of all scenarios and candidates, the average predicted HR for Dlib is either in line or very close to the average reference HR. The extracted HRs from the Haarcascade and MediaPipe architectures make up 31.25% and 18.75% of plausible results, respectively. The analysis highlighted the importance of fixated facial landmarks in collecting quality raw data and reducing noise

VOLUNTARY CONTROL OF BREATHING ACCORDING TO THE BREATHING PATTERN DURING LISTENING TO MUSIC AND NON-CONTACT MEASUREMENT OF HEART RATE AND RESPIRATION

We investigated if listening to songs changes breathing pattern which changes autonomic responses such as heart rate (HR) and heart rate variability (HRV) or change in breathing pattern is a byproduct of listening to songs or change in breathing pattern as well as listening to songs causes changes in autonomic responses. Seven subjects (4 males and 3 females) participated in a pilot study where they listened to two types of songs and used a custom developed biofeedback program to control their breathing pattern to match the one recorded during listening to the songs. Coherencies between EEG, breathing pattern and RR intervals (RRI) were calculated to study the interaction with neural responses. Trends in HRV varied only during listening to songs, suggesting that autonomic response was affected by listening to songs irrespective of control of breathing. Effective coherence during songs while spontaneously breathing was more than during silence and during control of breathing. These results, although preliminary, suggest that listening to songs as well as change in breathing patterns changes the autonomic response but the effect of listening to songs may surpass the effect of changes in breathing. We explored feasibility of using non-contact measurements of HR and breathing rate (BR) by using recently developed Facemesh and other methods for tracking regions of interests from videos of faces of subjects. Performance was better for BR than HR, and over currently used methods. However, refinement of the approach would be needed to get the precision required for detecting subtle changes

Laser doppler vibrometry for cardiovascular monitoring and photoacoustic imaging

Nowadays, techniques for health monitoring mainly require physical contact with patients, which is not always ideal. Non-contact health monitoring has become an important research topic in the last decades. The non-contact detection of a patient's health condition represents a beneficial tool in different biomedical fields. Examples can be found in intensive care, home health care, the nursing of the elderly, the monitoring of physical efforts, and in human-machine interactions. Cardiovascular diseases (CV) are one of the most spread causes of death in developed countries. Their monitoring techniques involve physical contact with patients. A non-contact technique for cardiovascular monitoring could overcome problems related to the contact with the patient such as skin lesions. It could also expand the availability of monitoring to those cases where contact is not possible or should be avoided to reduce the exposure of medical personnel to biochemical hazard conditions.Several research groups have investigated different techniques for non-contact monitoring of health; among them, the laser Doppler Vibrometry (LDVy) has one of the highest accuracies and signal to noise ratios for cardiorespiratory signals detection. Moreover, the simplicity of data processing, the long-distance measurement range, and the high bandwidth make the laser Doppler vibrometer (LDV) suitable for daily measurements. LDVy is an interferometric technique employed for the measurements of displacement or velocity signals in various fields. In particular, it is deployed in the biomedical field for the extraction of several cardiovascular parameters, such as the PR-time. Generally, the extraction of these parameters requires ideal measuring conditions (measuring spot and laser direction), which are not realistic for daily monitoring in non-laboratory conditions, and especially in tracking applications. The first scientific hypothesis of this work is that the PR-time detected with LDV has an acceptable uncertainty for a realistic variety of measurement spot positions and angles of the incident laser beam. Therefore, I investigated the uncertainty contribution to the detection of the PR-time from LDV signals resulting from the laser beam direction and from the measurement point position; these investigations were carried out with a multipoint laser Doppler vibrometer. The uncertainties were evaluated according to the Guide to the Expression of Uncertainty in Measurement. Successively, the ranges of PR-time values where it is possible to state with 95% certainty that a diagnosis is correct are identified. Normal values of PR-time are included in the range 120 ms -200 ms. For single value measurements with precise alignment the reliable range for the detection of the healthy condition is 146.4 ms -173.6 ms. The detection of CV diseases is reliable for measured values lower than 93.6 ms and greater than 226.4 ms. For mean value measurements with precise alignment the reliable range for the detection of the healthy condition is 126.6 ms -193.4 ms. The detection of CV diseases is reliable for measured values lower than 113.4 ms and greater than 206.6 ms. Therefore, for measured values included in the mentioned ranges, the detection of the PR-time and relative diagnosis with the LDVy in non-laboratory conditions is reliable. The method for the estimation of the uncertainty contribution proposed in this work can be applied to other cardiovascular parameters extracted with the LDVy. Recently, the LDVy was employed for the detection of tumors in tissue-mimic phantoms as a noncontact alternative to the ultrasound sensors employed in photoacoustic imaging (PAI). A non-contact method has considerable advantages for photoacustic imaging, too. Several works present the possibility to perform PAI measurements with LDVy. However, a successful detection of the signals generated by a tumor depends on the metrological characteristics of the LDV, on the properties of the tumor and of the tissue. The conditions under which a tumor is detectable with the laser Doppler vibrometer has not been investigated yet. The second scientific hypothesis of this work is that, under certain conditions, photoacoustic imaging measurements with LDVy are feasible. Therefore, I identified those conditions to determine the detection limits of LDVy for PAI measurements. These limits were deduced by considering the metrological characteristics of a commercial LDV, the dimensions and the position of the tumor in the tissue. I derived a model for the generation and propagation of PA signals and its detection with an LDV. The model was validated by performing experiments on silicone tissue-micking phantoms. The validated model with breast-tissue parameters reveals the limits of tumor detection with LDVy-based PAI. The results show that commercial LDVs can detect tumors with a minimal radius of ≈350 μm reliably if they are located at a maximal depth in tissue of ≈2 cm. Depending on the position of the detection point, the maximal depth can diminish and depending on the absorption characteristics of the tumor, the detection range increases.Heutzutage erfordern Techniken zur Gesundheitsüberwachung hauptsächlich den physischen Kontakt mit dem Patienten, was nicht immer ideal ist. Die berührungslose Gesundheitsüberwachung hat sich in den letzten Jahrzehnten zu einem wichtigen Forschungsthema entwickelt. Die berührungslose Erkennung des Gesundheitszustands eines Patienten stellt ein nützliches Instrument in verschiedenen biomedizinischen Bereichen dar. Beispiele finden sich in der Intensivpflege, der häuslichen Krankenpflege, der Altenpflege, der Überwachung körperlicher Anstrengungen und in der MenschMaschine-Interaktion. Herz-Kreislauf-Erkrankungen sind eine der am weitesten verbreiteten Todesursachen in den Industrieländern. Ihre Überwachungstechniken erfordern einen physischen Kontakt mit den Patienten. Eine berührungslose Technik für die Überwachung von Herz-KreislaufErkrankungen könnte Probleme im Zusammenhang mit dem Kontakt mit dem Patienten, wie z. B. Hautverletzungen, überwinden. Verschiedene Messgeräte wurden für die berührungslose Überwachung der Gesundheit untersucht; unter ihnen hat das Laser-Doppler-Vibrometrer (LDV) eine der höchsten Genauigkeiten und Signal-Rausch-Verhältnisse für die Erkennung kardiorespiratorischer Signale. Darüber hinaus ist das Laser-Doppler-Vibrometer (LDV) aufgrund der einfachen Datenverarbeitung, des großen Messbereichs und der hohen Bandbreite für tägliche Messungen geeignet. LDV ist ein interferometrisches Verfahren, das zur Messung von Weg- oder Geschwindigkeitssignalen in verschiedenen Bereichen eingesetzt wird. Insbesondere wird es im biomedizinischen Bereich für die Extraktion verschiedener kardiovaskulärer Parameter, wie z. B. der PR-Zeit, eingesetzt. Im Allgemeinen erfordert die Extraktion dieser Parameter ideale Messbedingungen (Messfleck und Laserrichtung), die für die tägliche Überwachung unter Nicht-Laborbedingungen und insbesondere für TrackingAnwendungen nicht realistisch sind. Die erste wissenschaftliche Hypothese dieser Arbeit ist, dass die mit dem LDV ermittelte PR-Zeit eine akzeptable Unsicherheit für eine realistische Vielzahl von Messpunktpositionen und Winkeln des einfallenden Laserstrahls aufweist. Daher wurde der Unsicherheitsbeitrag zur Ermittlung der PR-Zeit aus LDV-Signalen untersucht, der sich aus der Laserstrahlrichtung und der Messpunktposition ergibt; diese Untersuchungen wurden mit einem Mehrpunkt-Laser-Doppler-Vibrometer durchgeführt. Die Unsicherheiten wurden gemäß der Technische Regel ISO/IEC Guide 98-3:2008-09 Messunsicherheit – Teil 3: Leitfaden zur Angabe der Unsicherheit beim Messen bewertet. Nacheinander werden die Bereiche der PR-Zeit-Werte ermittelt, in denen mit 95%iger Sicherheit eine korrekte Diagnose gestellt werden kann. Die in dieser Arbeit vorgeschlagene Methode zur Schätzung des Unsicherheitsbeitrags kann auch auf andere kardiovaskuläre Parameter angewendet werden, die mit dem LDV extrahiert werden. Kürzlich wurde das LDV zur Erkennung von Tumoren in gewebeähnlichen Phantomen als berührungslose Alternative zu den Ultraschallsensoren eingesetzt, die bei der photoakustischen Bildgebung (PAI) verwendet werden. Eine berührungslose Methode hat auch für die photoakustische Bildgebung erhebliche Vorteile. In mehreren Arbeiten wird die Möglichkeit vorgestellt, PAIMessungen mit LDV durchzuführen. Die erfolgreiche Erkennung der von einem Tumor erzeugten Signale hängt jedoch von den messtechnischen Eigenschaften des LDV sowie von den Eigenschaften des Tumors und des Gewebes ab. Die Bedingungen, unter denen ein Tumor mit dem LDV detektierbar ist, wurden bisher nicht untersucht. Die zweite wissenschaftliche Hypothese dieser Arbeit ist, dass unter bestimmten Bedingungen photoakustische Bildgebungsmessungen mit dem LDV möglich sind. Daher wurden diese Bedingungen ermittelt, um die Nachweisgrenzen von LDV für PAI-Messungen zu bestimmen. Diese Grenzen wurden unter Berücksichtigung der messtechnischen Eigenschaften eines handelsüblichen LDV, der Abmessungen und der Position des Tumors im Gewebe abgeleitet. In dieser Arbeit wurde ein Modell für die Erzeugung und Ausbreitung von PA-Signalen und deren Nachweis mit einem LDV abgeleitet. Das Modell wurde durch Experimente an Silikongewebe-Phantomen validiert. Das validierte Modell mit Parametern des Brustgewebes zeigt die Grenzen der Tumorerkennung mit LDV-basierter PAI auf. Die Ergebnisse zeigen, dass kommerzielle LDV Tumore mit einem minimalen Radius von ≈350 μm zuverlässig erkennen können

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

Emotion and Stress Recognition Related Sensors and Machine Learning Technologies

This book includes impactful chapters which present scientific concepts, frameworks, architectures and ideas on sensing technologies and machine learning techniques. These are relevant in tackling the following challenges: (i) the field readiness and use of intrusive sensor systems and devices for capturing biosignals, including EEG sensor systems, ECG sensor systems and electrodermal activity sensor systems; (ii) the quality assessment and management of sensor data; (iii) data preprocessing, noise filtering and calibration concepts for biosignals; (iv) the field readiness and use of nonintrusive sensor technologies, including visual sensors, acoustic sensors, vibration sensors and piezoelectric sensors; (v) emotion recognition using mobile phones and smartwatches; (vi) body area sensor networks for emotion and stress studies; (vii) the use of experimental datasets in emotion recognition, including dataset generation principles and concepts, quality insurance and emotion elicitation material and concepts; (viii) machine learning techniques for robust emotion recognition, including graphical models, neural network methods, deep learning methods, statistical learning and multivariate empirical mode decomposition; (ix) subject-independent emotion and stress recognition concepts and systems, including facial expression-based systems, speech-based systems, EEG-based systems, ECG-based systems, electrodermal activity-based systems, multimodal recognition systems and sensor fusion concepts and (x) emotion and stress estimation and forecasting from a nonlinear dynamical system perspective