HHT-based Analysis of ECG Signals of Patients with Borderline Mental Disorders

This paper describes a solution for borderline mental disorders detection. This approach is based on the ECG processing by Hilbert-Huang Transformation. The described approach allows to develop an additional module for mental disorders diagnostic systems. The research is based on the fact that in the conditions of borderline mental disorders there are changes in patients' heart function. Detection of significant ECG informative parameters is based on the effective and accurate measurement of amplitude, time, frequency and energy parameters of the ECG signal. A verified and registered database of 780 ECG signals of patients with borderline mental disorders and healthy people is used. The proposed method is described and the results are shown. The errors of the method with current sampling do not exceed 4%. The developed approach using volumetric spectral surfaces has showed a high probability of determining the period of occurrence of psycho-traumatic situations in various patients using the ECG

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease

Background Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. Methods ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. Results The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. Conclusions ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.The current study was supported by the Bio & Medical Technology Development Program of the National Research Foundation (NRF) funded by the Korean government (MSIT) (2019M3E5D1A0206962012), the NRF funded by MIST (NRF-2022R1F1A1071574), (2022H1D8A3037396), INHA UNIVERSITY Research Grant, and Institute of Information & communications Technology Planning & Evaluation (IITP) funded by MSIT (RS-2022-00155915, Artificial Intelligence Convergence Innovation Human Resources Development (Inha University)). The funders had no role in study design, data collection, analysis, decision to publish, or manuscript preparation

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH

ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT

Physical Diagnosis and Rehabilitation Technologies

The book focuses on the diagnosis, evaluation, and assistance of gait disorders; all the papers have been contributed by research groups related to assistive robotics, instrumentations, and augmentative devices

Pulse Rate Variability for the Assessment of Cardiovascular Changes

Pulse rate variability (PRV) describes the way pulse rate changes through time and is measured from pulsatile signals such as the photoplethysmogram (PPG). It has been proposed as a surrogate for heart rate variability (HRV). Nonetheless, the relationship between these variables is not entirely clear, probably due to both physiological and technical aspects involved in the extraction of PRV. Moreover, the effects of cardiovascular changes on PRV have not been elucidated. In this thesis, four studies were performed to (1) determine the best combination of some technical aspects for the extraction of PRV from PPG signals; (2) evaluate the relationship between PRV and HRV under different cardiovascular conditions; and (3) explore the effects of cardiovascular changes on PRV. First, PRV extraction gave lower errors when (1) signals were acquired for at least 120 s with a 256 Hz sampling rate and filtered with lower low cut-off frequencies and elliptic, equiripple or Parks-McClellan filter; (2) cardiac cycles were determined using the D2max algorithm and the a fiducial points; and (3) the Fast Fourier Transform was applied to obtain frequency spectra. Secondly, the relationship between HRV and PRV was found to be affected by cold exposure and changes in blood pressure, while PRV was found to be different at different body sites. Finally, PRV was affected by haemodynamic changes, such as target flow, stroke rate and blood pressure, both in an in-vitro model and in-vivo data. Additionally, PRV was found to be a potential tool for the estimation of blood pressure, with errors as low as 1:54 ± 0:17 mmHg, 1:07 ± 0:06 mmHg and 1:22 ± 0:09 mmHg for the estimation of systolic, diastolic and mean arterial pressure. Although more studies are needed to fully understand PRV and its clinical potential, PRV should not be regarded as the same as HRV, and it could be consider as a potential valuable biomarker for cardiovascular health

Clinical and genetic aspects of Marfan syndrome and familial thoracic aortic aneurysms and dissections

This thesis concerns the clinical and genetic aspects of familial thoracic aortic aneurysms and dissections, in particular in Marfan syndrome. It includes the Dutch multidisciplinary guidelines for diagnosis and management of Marfan syndrome. These guidelines contain practical directions for referring physicians and specialists involved in the recognition, diagnosis, monitoring and treatment of Marfan syndrome. Furthermore, the revised Ghent nosology for Marfan syndrome, established by an international panel of experts, is presented. One chapter concerns a specific subgroup of missense mutations in FBN1 that are predicted to substitute the first aspartic acid of various calcium-binding Epidermal Growth Factor-like (cbEGF) fibrillin-1 domains. One of the mutations was found in a homozygous state in three cases from a large consanguineous family. A series of ten patients carrying a whole-gene deletion of one allele of FBN1 is described in another chapter. In a further chapter a three-generational family is discussed with family members at risk for serious aortic disease as a result of an interstitial deletion of chromosome 15 that disrupts SMAD3. Finally two unrelated children with classic Marfan syndrome and recurrent intracranial hypertension are described.The development of the practical guidelines for the diagnosis and management of Marfan syndrome (Chapter 2)was supported by Stichting Kwaliteitsgelden Medisch Specialisten (SKMS).UBL - phd migration 201

Hypothalamic and medullar mechanisms for long-term autonomic regulation of arterial blood pressure

Tese de doutoramento, Ciências Biomédicas (Fisiologia), Universidade de Lisboa, Faculdade de Medicina, 2014Fundação para a Ciência e a Tecnologia (FCT