US-20260123842-A1 - SYSTEM AND METHOD FOR NON-INVASIVE CARDIOVASCULAR HEALTH ASSESSMENT

Abstract

A computer-implemented method for non-invasive Cardiovascular health assessment including, receiving, at a processor, physiological signals from a subject, wherein the physiological signals comprising at least electrocardiogram (ECG) signals and photoplethysmogram (PPG) signals, processing, by an artificial intelligence (AI) model executed by the processor, the ECG signals and PPG signals, and; estimating, by the AI model, one or more demographic and anthropometric parameters based on processing the ECG and PPG signals.

Inventors

• Zainab Jamil
• Ho Man Chan
• Liangyi Lyu
• Leong Ting LUI
• Zheng Gong

Assignees

• Hong Kong Centre for Cerebro-Cardiovascular Health Engineering Limited

Dates

Publication Date

20260507

Application Date

20251029

Claims (20)

1. 1 . A computer-implemented method for non-invasive Cardiovascular health assessment comprising: receiving, at a processor, physiological signals from a subject, wherein the physiological signals comprising at least electrocardiogram (ECG) signals and a photoplethysmogram (PPG) signals, processing, by an artificial intelligence (AI) model executed by the processor, the ECG signals and PPG signals, and; estimating, by the AI model, one or more demographic and anthropometric parameters based on processing the ECG and PPG signals.
2. 2 . The method of claim 1 , wherein the step of processing comprising: pre-processing the received ECG signals and PPG signals, performing feature extraction of the pre-processed ECG signals and PPG signals, selecting one or more optimal features, and; wherein the demographic and anthropometric parameters are estimated based on the one or more optimal features.
3. 3 . The method of claim 2 wherein the step of processing comprising the step of performing temporal processing of the pre-processed ECG signals and PPG signals.
4. 4 . The method of claim 3 , wherein temporal processing comprising capturing temporal dependencies and spatial relationships between the one or more optimal features.
5. 5 . The method of claim 1 wherein the demographic parameters comprising age, gender and the one or more anthropometric features comprising weight, height and BMI.
6. 6 . The method of claim 1 comprising calculating, by the AI model, a cardiovascular age for the subject based on the estimated demographic and anthropometric parameters; and outputting the cardiovascular age and/or comparing the calculated cardiovascular age to the subject's chronological age to identify a risk of vascular aging.
7. 7 . The method of claim 6 , further comprising tracking changes in estimated BMI or weight over time to detect abnormal trends and presenting estimated BMI or weight changes on a user interface.
8. 8 . The method of claim 1 , further comprising: generating a visual indicator to illustrate the estimate of the demographic and anthropomorphic parameters, presenting the visual indicator on a user interface, and; wherein the visual indicator comprising one or more of: a saliency map or heat map or attention heat map.
9. 9 . The method of claim 3 wherein the AI model comprising a recurrent neural network (RNN model), wherein the RNN model is trained to perform the steps of processing the received ECG and PPG signals and output an estimate of one or more demographic and anthropometric parameters.
10. 10 . A system for non-invasive cardiovascular health monitoring comprising: a computing apparatus comprising a processor and a memory unit, the processor and memory unit being operatively coupled to each other, a user interface operatively coupled to or integrated into the computing apparatus, the memory unit adapted to store an artificial intelligence (AI) model, wherein the AI model is executable by the processor, the memory unit further comprising instructions which, when executed by the processor cause the processor to: receive physiological signals from a subject, wherein the physiological signals comprising at least electrocardiogram (ECG) signals and a photoplethysmogram (PPG) signals, process, by the AI model, the ECG signals and PPG signals, and; estimate, by the AI model, one or more demographic and anthropometric parameters based on processing the ECG and PPG signals, and; display the estimated demographic and anthropometric parameters on the user interface.
11. 11 . The system of claim 10 , wherein the processor is programmed to: pre-process the received ECG signals and PPG signals, perform feature extraction of the pre-processed ECG signals and PPG signals, select one or more optimal features, and; wherein the demographic and anthropometric parameters are estimated based on the one or more optimal features.
12. 12 . The system of claim 11 , wherein the processor is programmed to perform temporal processing of the pre-processed ECG signals and PPG signals.
13. 13 . The system of claim 12 , wherein during temporal processing the processor is programmed to capture temporal dependencies and spatial relationships between the one or more optimal features.
14. 14 . The system of claim 10 , wherein the demographic parameters comprise age, gender and the one or more anthropometric features comprising weight, height and BMI.
15. 15 . The system of claim 10 , wherein the processor is programmed to: calculate, by applying the AI model, a cardiovascular age for the subject based on the estimated demographic and anthropometric parameters; and output the cardiovascular age and/or comparing the calculated cardiovascular age to the subject's chronological age to identify a risk of vascular aging.
16. 16 . The system of claim 15 , wherein the processor is programmed to track changes in estimated BMI or weight over time to detect abnormal trends and present estimated BMI or weight changes on the user interface.
17. 17 . The system of claim 10 , wherein the processor is further programmed to: generate a visual indicator to illustrate the estimate of the demographic and anthropomorphic parameters, present the visual indicator on the user interface, and; wherein the visual indicator comprising one or more of: a saliency map or heat map or attention heat map.
18. 18 . The system of claim 11 , wherein the AI model comprising a recurrent neural network (RNN model), wherein the RNN model is trained to perform the steps of processing the received ECG and PPG signals and output an estimate of one or more demographic and anthropometric parameters.
19. 19 . The system of claim 18 , wherein the AI model is pretrained using a self-supervised learning framework on a dataset of unlabeled ECG signals, PPG signals, or both and, wherein the self-supervised learning framework is a masked self-supervised learning framework wherein segments of the unlabeled signals are masked and the AI model is trained to reconstruct the masked segments.
20. 20 . A wearable device for non-invasive cardiovascular health monitoring, comprising: a sensor array configured to continuously acquire ECG and PPG signals from a user; a computing apparatus, the computing apparatus comprising a processor and a memory unit, the processor and memory unit being operatively coupled to each other, the computing apparatus being operatively coupled to the sensor array, the memory unit adapted to store an artificial intelligence (AI) model, wherein the AI model is executable by the processor, the memory unit further comprising instructions which, when executed by the processor cause the processor to: receive physiological signals from a subject, wherein the physiological signals comprising at least electrocardiogram (ECG) signals and a photoplethysmogram (PPG) signals, process, by the AI model, the ECG signals and PPG signals, and; estimate, by the AI model, one or more demographic and anthropometric parameters based on processing the ECG and PPG signals.

Description

TECHNICAL FIELD The present invention relates to a system and method for non-invasive cardiovascular health assessment. The present invention may also relate to a system and method for non-invasive cardiovascular health assessment based on ECG and PPG signals. BACKGROUND Cardiovascular health remains a central challenge in contemporary medicine, as it directly influences both life expectancy and quality of life. Accurate estimation of demographic and anthropometric features such as age, gender, height, weight, and body mass index (BMI) is not only critical for effective clinical monitoring but also for early detection of cardiovascular disease (CVD) risk, individualized prevention strategies, and tailored treatment plans. Despite their importance, current methods for acquiring these parameters are limited. Manual anthropometric measurements are subject to human error, inter-operator variability, and device-specific inconsistencies, making them unreliable in large-scale or remote healthcare settings. Invasive or laboratory-based measurements, while precise, are impractical for routine monitoring due to their associated discomfort, risks, and time requirements. Even where electrocardiogram (ECG) and photoplethysmogram (PPG) are employed in practice, their use is largely confined to blood pressure (BP) estimation, arrhythmia detection, or pulse analysis, with no established frameworks for demographic or anthropometric prediction. Furthermore, many existing AI systems in healthcare suffer from bias and fairness concerns, leading to uneven prediction quality across gender, age, or population subgroups. There is a need for an improved system that addresses the shortcomings of conventional methods of determining demographic and anthropometric parameters. SUMMARY The present invention seeks to provide a system and method for non-invasive cardiovascular health assessment, which will overcome or substantially ameliorate at least some of the deficiencies of the prior art, or to at least provide an alternative. In accordance with a first aspect, there is provided a computer-implemented method for non-invasive cardiovascular health assessment comprising: receiving, at a processor, physiological signals from a subject,wherein the physiological signals comprising at least electrocardiogram (ECG) signals and a photoplethysmogram (PPG) signals,processing, by an artificial intelligence (AI) model executed by the processor, the ECG signals and PPG signals, and;estimating, by the AI model, one or more demographic and anthropometric parameters based on processing the ECG and PPG signals. In one example, the step of processing comprising: pre-processing the received ECG signals and PPG signals,performing feature extraction of the pre-processed ECG signals and PPG signals,selecting one or more optimal features, and;wherein the demographic and anthropometric parameters are estimated based on the one or more optimal features. In one example, the step of processing comprising the step of performing temporal processing of the pre-processed ECG signals and PPG signals. In one example, temporal processing comprising capturing temporal dependencies and spatial relationships between the one or more optimal features. In one example, the demographic parameters comprising age, gender and the one or more anthropometric features comprising weight, height and BMI. In one example, the method further comprising calculating, by the AI model, a cardiovascular age for the subject based on the estimated demographic and anthropometric parameters, and;outputting the cardiovascular age and/or comparing the calculated cardiovascular age to the subject's chronological age to identify a risk of vascular aging. In one example, the method comprising tracking changes in estimated BMI or weight over time to detect abnormal trends and presenting estimated BMI or weight changes on a user interface. In one example, the method further comprising: generating a visual indicator to illustrate the estimate of the demographic and anthropomorphic parameters,presenting the visual indicator on a user interface, and;wherein the visual indicator comprising one or more of: a saliency map or heat map or attention heat map. In one example, the AI model comprising a recurrent neural network (RNN model), wherein the RNN model is trained to perform the steps of processing the received ECG and PPG signals and output an estimate of one or more demographic and anthropometric parameters. In one example, the method may optionally apply ensemble methods to combine predictions from diverse model architectures. This can improve robustness and reliability. In accordance with a further aspect, there is provided a system for non-invasive cardiovascular health monitoring comprising: a computing apparatus comprising a processor and a memory unit, the processor and memory unit being operatively coupled to each other,a user interface operatively coupled to or integrated into the computing appa