Quantification of Ventricular Repolarization Dispersion Using Digital Processing of the Surface ECG

Digital processing of electrocardiographic records was one of the first applications of signal processing on medicine. There are many ways to analyze and study electrical cardiac activity using the surface electrocardiogram (ECG) and nowadays a good clinical diagnostic and prevention of cardiac risk are the principal goal to be achieved. One aim of digital processing of ECG signals has been quantification of ventricular repolarization dispersion (VRD), phenomenon which mainly is determined by heterogeneity of action potential durations (APD) in different myocardial regions. The APD differs not only between myocytes of apex and the base of both ventricles, but those of endocardial and epicardial surfaces (transmural dispersion) and between both ventricles. Also, it was demonstrated that several electrophysiologically and functionally different myocardial cells, like epicardial, endocardial and mid-myocardial M cells. The APD inequalities develop global and/or local voltage gradients that play an important role in the inscription of ECG T-wave morphology. In this way, we can assume that T-wave is a direct expression of ventricular repolarization inhomogeneities on surface ECG. Experimental and clinical studies have demonstrated a relationship between VRD and severe ventricular arrhythmias. In addition, patients having increased VRD values have a higher risk of developing reentrant arrhythmias. Frequently the heart answer to several pathological states produced an increase of VRD; this phenomenon may develop into malignant ventricular arrhythmia (MVA) and/or sudden cardiac death (SCD). Moreover, it has been showed that the underlying mechanisms in MVA and/or SCD are cardiac re-entry, increased automation, influence of autonomic nervous system and arrhythmogenic substrates linked with cardiac pathologies. These cardiac alterations could presented ischemia, hypothermia, electrolyte imbalance, long QT syndrome, autonomic system effects and others. Digital processing of ECG has been proved to be useful for cardiac risk assessment, with additional advantages like of being non invasive treatments and applicable to the general population. With the aim to identify high cardiac risk patients, the researchers have been tried to quantify the VRD with different parameters obtained by mathematic-computational processing of the surface ECG. These parameters are based in detecting changes of T-wave intervals and T-wave morphology during cardiac pathologies, linking these changes with VRD. In this chapter, we have presented a review of VRD indexes based on digital processing of ECG signals to quantify cardiac risk. The chapter is organized as follows: Section 2 explains ECG preprocessing and delineation of fiducial points. In Section 3, indexes of VRD quantification, such as: QT interval dispersion, QT interval variability and T-wave duration, are described. In Section 4, different repolarization indexes describing T-wave morphology and energy are examined, including complexity of repolarization, T-wave residuum, angle between the depolarization and repolarization dominant vectors, micro T-wave alternans, T-wave area and amplitude and T-wave spectral variability. Finally, in Section 5 conclusions are presented.Fil: Vinzio Maggio, Ana Cecilia. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; ArgentinaFil: Bonomini, Maria Paula. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Laciar Leber, Eric. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de San Juan. Facultad de Ingeniería; ArgentinaFil: Arini, Pedro David. Universidad de Buenos Aires. Facultad de Ingeniería. Instituto de Ingeniería Biomédica; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Saavedra 15. Instituto Argentino de Matemática Alberto Calderón; Argentin

Physiological-based Driver Monitoring Systems: A Scoping Review

A physiological-based driver monitoring system (DMS) has attracted research interest and has great potential for providing more accurate and reliable monitoring of the driver’s state during a driving experience. Many driving monitoring systems are driver behavior-based or vehicle-based. When these non-physiological based DMS are coupled with physiological-based data analysis from electroencephalography (EEG), electrooculography (EOG), electrocardiography (ECG), and electromyography (EMG), the physical and emotional state of the driver may also be assessed. Drivers’ wellness can also be monitored, and hence, traffic collisions can be avoided. This paper highlights work that has been published in the past five years related to physiological-based DMS. Specifically, we focused on the physiological indicators applied in DMS design and development. Work utilizing key physiological indicators related to driver identification, driver alertness, driver drowsiness, driver fatigue, and drunk driver is identified and described based on the PRISMA Extension for Scoping Reviews (PRISMA-Sc) Framework. The relationship between selected papers is visualized using keyword co-occurrence. Findings were presented using a narrative review approach based on classifications of DMS. Finally, the challenges of physiological-based DMS are highlighted in the conclusion. Doi: 10.28991/CEJ-2022-08-12-020 Full Text: PD

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

ECG reduction for wearable sensor

The transmission, storage and analysis of electrocardiogram (ECG) data in real-time is essential for remote patient monitoring with wearable ECG devices and mobile ECG contexts. However, this remains a challenge to achieve within the processing power and the storage capacity of mobile devices. ECG reduction algorithms have an important role to play in reducing the processing requirements for mobile devices, however many existing ECG reduction and compression algorithms are computationally expensive to execute in mobile devices and have not been designed for real-time computation and incremental data arrival. In this paper, we describe a computationally naive, yet effective, algorithm that achieves high ECG reduction rates while maintaining key diagnostic features including PR, QRS, ST, QT and RR intervals. While reduction does not enable ECG waves to be reproduced, the ability to transmit key indicators (diagnostic features) using minimal computational resources, is particularly useful in mobile health contexts involving power constrained sensors and devices. Results of the proposed reduction algorithm indicate that the proposed algorithm outperforms other ECG reduction algorithms at a reduction/compression ratio (CR) of 5:1. If power or processing capacity is low, the algorithm can readily switch to a compression ratio of up to 10: 1 while still maintaining an error rate below 10%

VAR-AS, sustained attention detection system in the learning environment

This dissertation presents a system for monitoring heart rate variability (HRV) by electrocardiogram (ECG) and photoplethysmography (PPG) for the detection of attention state in a learning environment. The ECG and PPG signs were acquired and processed through the development of a multi-microcontroller embedded system. When the waves of these signals are extracted, the heart rate is calculated through the time intervals between the R peaks for the electrocardiogram and between the beats for photopletismography. Finally, in order to indicate the level of attention on the part of the user, an input was added in which the volunteer marks the periods he or she considers to be most attentive. This data is associated with a millisecond resolution time stamp and sent in real time via Internet WiFi to a database in the cloud. After this data is stored, the HRV is analysed based on algorithms developed with the Matlab tool. These algorithms allow the study of cardiac variability according to time and frequency domains and how non-linear HRV measurements were also considered. Finally, a module for measuring skin conductivity was added, relating it to the analysis of the level of stress during the learning process. In order to prove the reliability of the system, several volunteers were tested in real environments according to a stipulated protocol. These records were analysed as a starting point to classify situations of greater attention of the volunteer in an educational scenario.Esta dissertação apresenta um sistema de monitorização da variabilidade da frequência cardíaca (VFC) através da Eletrocardiograma (ECG) e Fotoplestimografria (PPG) para a deteção do estado de atenção num ambiente de aprendizagem. Os signais de ECG e PPG foram adquiridos e processados através do desenvolvimento de um sistema embebido multi-microcontrolador. Ao extrair-se as ondas destes sinais, calcula-se a frequência cardíaca através dos intervalos de tempo entre os picos R para o eletrocardiograma e entre os batimentos para a fotopletismografia. Por fim, para poder indicaro nível da atenção da parte do utente adicionou-se um input em que o voluntário marca os períodos que considera estar mais atento. Estes dados são associados a uma marca temporal com resolução ma base de dados na nuvém. Após o armazenamento destes dados, analisa-se a VFC com base em algoritmos desenvolvidos com a ferramenta Matlab. Estes algoritmos permitem estudar a variabilidade cardíaca segundo os domínios do tempo, da frequência e como tambem foram consideradas medidas VFC não lineares. Por fim, adicionou-se um módulo para a medição da condutividade da pele, relacionando-a com a avaliação do nível de stress durante o processo de aprendizagem. Para comprovar a fiabilidade do sistema realizaram-se testes a diversos voluntários em ambientes reais de acordo com um protocolo estipulado. Estes registos foram analisados como ponto de partida para classificar situações de maior atenção do voluntário perante um cenário educativo