Stojmenski, Aleksandar
Preferred name
Stojmenski, Aleksandar
Official Name
Stojmenski, Aleksandar
11 results
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Item type:Publication, Challenges for development of an ECG m-Health solution(FICT, 2016); ; This paper presents the challenges to develop a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless, easily wearable and mobile ECG biosensor, a cloud-based data centre, smartphone and web application. A significant part of the system is the 24h health monitoring and care provided by expert cardiac physicians. The system predicts potential heart attack and sends risk alerts to the medical experts for assessment. If a potential heart attack risk exists, an emergency ambulance is being called with the coordinates of the cardiac patient wearing the sensor. The timely reaction can prevent serious tissue damage or even death to the users of the system. Our goal in this paper is to elaborate the challenges we met and solutions we have developed for development of an m-Health mobile application for detection of abnormalities in the ECG and alerting of a heart attack. The problems analyzed address Low Power Bluetooth connections, number conversion and transmission, decision making on what to be locally processed and what computations to be offloaded to cloud, software architecture, type of initial filtering for obtaining sufficient quality of the ECG signal, and visualization approach with relatively small processing requirements. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ECGalert: A Heart Attack Alerting System(Springer, Cham, 2017-09-18); ; Gusheva, AnaThis article presents a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless and mobile ECG biosensor, a data center, smartphone and web applications, and a remote 24h health care. The scientific basis of this system is founded on the fact that a heart attack can be detected at least two hours before its onset, and that a timely medical attention can dramatically reduce the risk of death or serious tissue damage. So far, there are no commercial products matching the goals and functionalities proposed by this system, even though there are a number of proof-of-concept studies, and a number of similar products on the market. For the greater part, these currently offered solutions are specifically intended for conducting stress tests in modern hospitals, or as personal fitness devices. Most of them have limited battery power, do not use algorithms for heart attack detection, and/or require constant supervision by medical personnel. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of novel faculty e-services for workflow automatization(2019); ; ; This paper presents a brief overview of the concepts for collaboration between various systems developed for the Faculty of Computer Science and Engineering in Skopje. Web technologies such as the HTTP, originally designed for human-tomachine communication, is utilized for machine-to-machine communication, more specifically for transferring machine-readable data in web service formats such as JSON. By using this kind of web technology and communication we can create various software applications suitable for various needs.This kind of web based software applications enable automatization and drastically eased and accelerated the entire procedure whose initial steps in the past was manually. This paper gives a brief overview of two novel systems which are integrated in the faculty software architecture. Software’s for master thesis submission and student surveys are integrated as a part of the core systems. The system’s network is collaborating using web services, central authentication services and data sharing which is based on cross-platform interfaces. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Age and Gender Impact on Heart Rate Variability towards Noninvasive Glucose Measurement(MDPI, 2023-10-25); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Deep Learning Models for Accurate and Reproducible Cardiac Measurements in Echocardiography(IEEE, 2024-04-18); The primary metrics recorded during ultrasound sessions focused on cardiac assessment are focused on the size of the heart's features, its two chambers and pre-chambers. Presently, these measurements are predominantly conducted manually, relying on the subjective judgment of medical professionals. A sonographer takes a transthoracic echocardiogram by first applying a viscous ultrasound gel on the patient at five distinct physical locations and placing the transducer on these positions at various angles. Each angle captures slightly different perspectives of the heart called views. From each view we can make out cutouts of cardiac structures and see motion of valves and walls. This study introduces an innovative approach to cardiac assessment which delegates the evaluation task to a trained algorithm. This has the potential to expedite the measurement process and help clinicians in the interpretation of the results by proposing common cardiac issues based on pre-trained data. In the paper we provide a process for intelligent extraction of diameters along the left ventricular outflow tract (LVOT) and aortic apparatus using only the ultrasound image. We show that it is possible to successfully train a model which can extract the measurements of the diameter of the LVOT in mid systole, the diameter of the aorta annulus (AA) in mid-systole, the diameter at the aortic sinus of Valsalva (ASV) at end diastole, and the diameter of the Sino tubular junction (SJ) at end diastole. With these measures extracted, the model uses machine learning to automate the diagnostics process which can bring diagnostics closer to patients’ homes. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Challenges for development of an ECG m-Health solution(FICT, 2016); ; This paper presents the challenges to develop a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless, easily wearable and mobile ECG biosensor, a cloud-based data centre, smartphone and web application. A significant part of the system is the 24h health monitoring and care provided by expert cardiac physicians. The system predicts potential heart attack and sends risk alerts to the medical experts for assessment. If a potential heart attack risk exists, an emergency ambulance is being called with the coordinates of the cardiac patient wearing the sensor. The timely reaction can prevent serious tissue damage or even death to the users of the system. Our goal in this paper is to elaborate the challenges we met and solutions we have developed for development of an m-Health mobile application for detection of abnormalities in the ECG and alerting of a heart attack. The problems analyzed address Low Power Bluetooth connections, number conversion and transmission, decision making on what to be locally processed and what computations to be offloaded to cloud, software architecture, type of initial filtering for obtaining sufficient quality of the ECG signal, and visualization approach with relatively small processing requirements. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of a Cloud-Based Personal Health System for Cross-Border Collaboration(ICT inovations, 2021); ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, ValveVision AI - a multimodal language model for qualitative reporting of the aortic valve in echocardiography(Oxford University Press (OUP), 2024-10); Postolovski, DBackground The precise assessment of the aortic valve via echocardiography is critical for early detection and management of aortic valve diseases. Until recently, previous studies have examined machine learning models to estimate individual measurements and severity of aortic stenosis (AS) from echocardiographic images. These image processing algorithms, while precise in their narrow focus, fall short in mirroring the holistic and interconnected clinical judgment typical of human echocardiographers in producing a qualitative report. Large language models (LLMs), particularly image-to-text multi-modal LLMs, are a fundamental advance in the field of deep learning with implications for a host of applications in medical imaging. They promise to encapsulate not just discrete data points typical in traditional machine learning, but also the complex contextual interrelations in clinical diagnosis. Methods In this study, a large-scale heterogeneous database of echocardiographic images containing over 90,681 studies with textual descriptors of the aortic valve was used to train a single, image-to-text multimodal LLM called ValveVision AI. The ground truth textual summaries were drafted by level III echocardiographers in a clinical setting between 2015-2020. BLEU and ROUGE score was calculated. The models were retrospectively assessed on a holdout dataset. Reviewing physicians compared the generated summary to the ground truth and binarily agreed or rejected it. Receiver Operating Characteristics (ROC) for distinct pathologies were also assessed (Figure I). Results ValveVision AI performed with a BLEU score of 0.45 and a ROUGE score of 0.49. The performance of the model in reporting on classification of moderate/severe vs none/mild AS in concordance with the validation protocol described above achieved a specificity of 91.98% and a sensitivity of 83.89%, along with more precise qualitative description. Qualitatively, the model exhibited the capability of zero-shot learning in certain instances, however, this result remains an area of exploration. Conclusion This study represents to our knowledge, the first attempt at an image-representation to text-tokenizer deep learning model architecture to mimic the thought and subtlety of echocardiographic qualitative analysis of the aortic valve. The results suggest that this multimodal LLM has sufficient accuracy to create a preliminary textual summary of the aortic valve that, if paired with a point of care ultrasound (POCUS) device in a primary care setting, may facilitate case triage, increase efficiency, and determine a more precise care pathway for patients. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A mobile application for ECG detection and feature extraction(2016); ; This paper presents a system for early detection and alerting of the onset of a heart attack. The system consists of a wireless, easy wearable and mobile ECG biosensor, a cloud based data center, smartphone and web application. A significant part in the system is the 24h health monitoring and care provided by expert cardiac physicians. The system predicts potential heart attack and sends risk alerts to the medical experts for assessment. If a potential heart attack risk exists, ambulance is being called with the coordinates of the cardiac patient wearing the sensor. The timely reaction can prevent serious tissue damage or even death to the users of the system. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Implementation of a Cloud-Based Personal Health System for Cross-Border Collaboration(ICT inovations, 2021-09);