Description:A.INVENTION TITLE

Adaptive Neural Framework for Low-Power Stroke Prediction and Cognitive State Assessment in Connected Health Systems
B. PROBLEM STATEMENT:
Stroke is a major cause of death and chronic disability in the world, and annually millions of individuals are affected. Immediate action and diagnosis are very essential in limiting the extent of neurological damage and enhancing patient outcomes. The conventional stroke prediction processes are very dependent on the hospital-based diagnostics, such as the brain imaging such as magnetic resonance imaging (MRI) and computed tomography (CT) scans. Although effective, the mechanisms need equipment and specially trained staff and controlled conditions in the clinic, hence it is almost impossible to monitor and detect symptoms early before a complete emergence outside a healthcare facility.
Also, current wearable-based health monitoring systems that seek to anticipate stroke or evaluate cognitive health have major drawbacks. Most of these systems are resource hungry and need lots of computational power and sustained connectivity to cloud servers to process data. This dependency on external servers makes them more sluggish, privacy-related and consumes a lot of energy, which has a severe negative impact on the batteries of wearables. Therefore, these systems do not work well in long-term real-time monitoring in low resource or mobile environments.
The other issue is that patient information is heterogeneous. The visiological measures such as electroencephalography (EEG), heart rate variability, blood pressure and motion recordings will vary in different individuals due to various age, lifestyle, medical record and environmental influences. The inter-individual differences are normally not considered in the already existing predictive models resulting in a less accurate, false alarm or missed detection. Moreover, the models available are usually rigid and they do not support long-term physiological/cognitive variations of a patient which is paramount to a long term, individualized monitoring.
In addition to these technical problems, there is not so much attention to stroke-prone cohort cognitive health assessment in the existing systems. These indicators as cognitive decline, level of stress, and attention deficits are likely to occur or precede stroke events but they are not usually used jointly on conventional monitoring systems. It has the impact of losing the chance of proactive interventions, which might prevent stroke or mitigate its impact.
Concisely, it is a multi-faceted issue: a low-power, adaptive and customized monitoring, which can enable wearable devices, analyze multi-modal physiological and cognitive data in real-time and follow the privacy of the user, and integrate into integrated health systems, is required. Without such a solution, continuous early stroke identification and real-time cognitive health evaluation may be significantly infeasible and leave the patient vulnerable to the risks of developing more adverse neurological dysfunction and poor clinical outcomes.

C. EXISTING SOLUTIONS

Existing methods of stroke prediction and AI-based cognitive health monitoring can be divided into broad categories of hospital-cantered diagnostic systems, wearable health devices, and cloud-based AI systems. Magnetic resonance imaging (MRI), computed tomography (CT), and digital subtraction angiography (DSA) are the most commonly used imaging technologies in hospital-based systems to identify cerebrovascular anomalies that may result in a stroke. Although these diagnostic methods offer precision in the diagnosis, it is also reactive and not proactive because patients normally move to seek hospital care once the symptoms appear.

Within the area of wearable technology, firms such as Apple, Fitbit, and Garmin have launched gadgets that can monitor the heart rate, blood oxygen, and activity trends. Certain medical-grade wearables (including the Empatica E4 wristband) use electrodermal activity and photoplethysmography sensors to track stress and cardiovascular activity. Nonetheless, they do not have stroke prediction specifically optimized, and do not offer built-in cognitive health assessments. Predictive models employed in these systems are usually simplistic, based on threshold-based alerting and not on adaptive multi-modal analytics.

There are a number of research-driven AI solutions to stroke risk monitoring and cognitive monitoring. They can be machine learning algorithms to process electroencephalography (EEG) or heart rate variability (HRV) data to identify neurological abnormalities early. Big data analytics Cloud-based platforms, including IBM Watson Health and Microsoft Azure Health AI, provide the ability to predict stroke risks at a population level using big datasets of hospitals or clinical trials. However, these solutions have troubles regarding real-time and low-power deployment, since it needs massive computational resources, constant connectivity, and central processing of data.

There are also some attempts to solve the issue of early stroke detection and cognitive monitoring disclosed in patent literature. The US Patent US20190387456A1 specifies a wearable system in which physiological sensors and machine learning models detect cardiovascular events. Another case, US Patent US20180234367A1 is devoted to cognitive evaluation using neurophysiological signals as a measure of monitoring, and predictive modelling. Although these patents lead to the creation of intelligent health monitoring systems, they are oriented to either one physiological modality, they are not real time adaptable, or they are not energy efficient in terms of enabling the wearable to operate over long periods.

Along with hospital-based diagnostics and wearable gadgets, mobile health apps are provided to track cardiovascular health and cognitive ability. Applications such as the CogniFit and Stroke Riskometer purport to provide stroke risk assessment and cognitive assessment respectively. Such apps typically use self-reported health data, a few questionnaires, or infrequent heart rate data captured by smart phone sensors. They are convenient, but do not have continuous monitoring, are not able to adjust to abrupt alterations in a users physiological state, and predictive accuracy is limited by use of fixed models as opposed to adaptive learning frameworks.

Hybrid AI models that integrate a number of physiological measurements (blood pressure, heart rate variability, EEG, and accelerator data) to predict stroke risk or cognitive decline have also been investigated in research studies. Indicatively, research has revealed that machine learning trained on electroencephalography (EEG) patterns are able to identify early cognitive impairment. On the same note, machine learning with integrated cardiovascular signals like heart rate variability has shown potential to forecast acute stroke incidences. Nonetheless, the vast majority of these models are verified in controlled laboratory settings, are computationally expensive and have not been implemented on energy-limited wearables in practice to monitor continuously.

Moreover, the traditional solutions tend to discuss stroke prediction and cognitive assessment as two independent problems. Hospital-based diagnostics aim at stroke diagnosis after the event, whereas cognitive health monitoring is mainly used to assess dementia or mild cognitive impairment via episodic diagnosis. The integration between these two fields is low leading to the absence of holism in health monitoring that can at the same time measure cardiovascular and neurological risks in real-time. This siloed system minimises the timeliness and relevance of interventions because important early warning signs may be missed until the patient has obvious signs.

Commercially speaking, the existing wearable platforms are constrained by battery and sensor. The majority of commercial wearables are not able to support multi-modal physiological and cognitive data collection over prolonged term without re-charging, which reduces their usefulness in continuous stroke and cognitive risk surveillance. Also, their proprietary software discourages customization as it does not offer the possibility to incorporate adaptive AI models that can be trained based on the specific user over time.

Intellectually, multiple patents have sought to solve stroke prediction and cognitive monitoring separately but hardly offer a unified adaptive, energy-efficient and wearable-compatible solution. Current patents tend to concentrate either on a particular sensor modality or a particular predictive model, not taking into account the difficulties of continuity and low-power operation in connected health systems. Besides, cloud-based solutions are capable of providing analytics at scale, but they usually require continuous internet access and cannot be used in situations where it is essential to predict and act quickly.

In summary, despite the existing range of products, applications and research models, a technological gap in offering an integrated, adaptive, real-time, low-power solution to stroke prediction and cognitive health assessment is evident. Existing solutions do not combine multi-modal physiological monitoring and cognitive assessment, adapt to user patterns and can run effectively in wearable devices to support continuous and real-world operations. It is this gap that gives rise to the suggested innovation in lightweight neural structure that is optimized to connected health systems.

1. In what way(s) do the presently available solutions fall short of fully solving the problem?
Ans.
The existing solutions to stroke prediction and cognitive health assessment have a number of fundamental shortcomings that make them ineffective in the management of the issue. One, the majority of commercial products and applications are based on episodic measurements or self-reported data, which do not offer continuous monitoring but only snapshots of the physiological or mental condition of a user. Such intermittent method is not enough to monitor the early warning signs of the stroke or mild cognitive impairment as it is needed to observe the patient in real time and provide the timely interventions.

Second, a lot of currently existing AI or machine learning models applied in studies are computationally heavy and optimized to run on a high-performance computing system. In trying to apply these models to wearable or low-power devices, their performance is compromised because of memory, processing, and energy limitations. Consequently, wearable computers, unless recharged regularly, tend to be unable to execute complex predictive algorithms in real-time, and cannot be reliably used to perform continuous health monitoring on a physical basis.

Third, unification of several physiological indicators and cognitive measurements has not been addressed in existing solutions. Currently, cardiovascular monitoring (e.g., blood pressure, heart rate variability), and cognitive tests (e.g., memory, attention tests) are discussed on different areas. That isolated strategy does not allow developing a comprehensive model that can capture both interactions between neurological and cardiovascular risks at once, which is essential to the accurate prediction of stroke and the overall assessment of cognitive health.

Fourth, existing systems have a significant limitation in the form of adaptability. The majority of AI models work based on generalized data and do not allow customizing predictions to specific physiological characteristics of a person or cognitive reactions in the long term. In the absence of adaptive learning, such systems are incapable of considering changes in the health of a user over time, which reduces the predictive capabilities of the system and clinical applicability.

Lastly, present solutions are further impeded by connectivity and deployment limitations. Cloud-oriented models demand that they always have access to the internet to compute the data and make predictions. The use of cloud computation in the real world would add some delay in the reaction to an emergency where speed is vital like in the case of noticing the onset of a stroke; this would compromise immediate response. Also, proprietary wearable devices tend to limit data access and personalization, so researchers or clinicians cannot incorporate sophisticated adaptive models that can greatly enhance prediction potential.

Overall, although current solutions post-offer partial monitoring and predictive capabilities, it lacks real-time, low-power, adaptive, and integrated stroke prediction and cognitive health assessment, especially within wearable and connected health systems. These deficiencies bring out the importance of a new framework that can close these gaps adequately.

Abstract

The invention suggests an Adaptive Neural Framework of low-power prediction in the stroke and cognitive assessment of states that is particularly aimed at the connected health systems. Stroke is a major cause of disability and death and early response to the condition is essential in enhancing the outcome of the patient. The existing systems are based on the extensive use of hospital-based diagnostics or high-power wearing, which are not efficient in terms of real-time and continuous monitoring in resource-limited settings. The invention overcomes these issues by incorporating the multi-modal sensor data (EEG, heart rate variability (HRV), motion sensors and oxygen saturation) into an adaptive neural network that consumes ultra-low power. The system is capable of supporting personalized and continuous monitoring and battery life is preserved which ensures its long-term application in both home and clinical environments.

The structure has a dynamic processing intensity that varies according to the incoming data and personal patterns of the baseline, maximizing the accuracy of prediction without sacrificing the energy efficiency. Also, the system integrates identity-based remote data integrity checking in place to guarantee authenticity and privacy of the health information sent, such as medical privacy laws such as the HIPAA and GDPR. The architecture of the system which is composed of wearable sensors, edge processing units and secure cloud communication enables stroke prediction and cognitive health measurements in real time. It also facilitates longitudinal health trend analysis, which provides delivery of healthcare providers with timely alerts and recommendations on the personalized intervention. This is an invention that offers a scalable, secure and efficient means of stroke prediction and cognitive state examination to overcome the shortcomings of current technologies.

key words: Stroke prediction, Cognitive assessment, Adaptive neural framework, Low-power wearable devices, Connected health systems

D.DESCRIPTION OF PROPOSED INVENTION:
A. Identity Based Remote Data Integrity Checking
The addressed topic of concern is the proposed invention, Adaptive Neural Framework to Low-Power Stroke Prediction and Cognitive State Assessment in Connected Health Systems, which is presented as a solution to the problem of an early and appropriate identification of the stroke incident and constant observation of the condition of cognitive health. Stroke is recognized as one of the major causes of disability and mortality on the global scene and early detection plays a major role in enhancing patient outcomes. The existing solutions either utilize a monitoring system based in hospitals (that is intermittent) or high-power wearable devices (that are not adaptable or real-time intelligent). Our invention combines identity-based remote data integrity checking, and adaptive neural processing to offer a strong, low-power and secure system capable of continuous health monitoring under real-life conditions.

Its structure is comprised of low-weight wearable devices of low energy content with accents, EEG (electroencephalogram) and other indicator of physiological signals. These sensors continuously record the appropriate biomarkers, including heart rate variability, limb motions pattern, brainwave activity, and level of oxygen saturation. To reduce transmission overhead and battery consumption, the collected data is locally preprocessed with energy-efficient algorithms to remove noise and normalize signals, and preserve battery life.

The most important part of the system is an adaptive neural network which dynamically adapts the structure and processing intensity according to the incoming data and the patient-specific baseline patterns. This is to guarantee a precise prediction of the strokes with minimum calculation load. The neural network is trained based on a mixture of past patient information, current sensor data streams, and medical labels in order to detect early signs of ischemic or hemorrhagic stroke.

The identity-based remote data integrity checking element assures that all patient information sent to the wearable devices to the health platform connected to them are authentic, unchanged and safely associated to the appropriate patient identity. Cryptographic methods are used to verify each piece of data packet in real-time, guaranteeing that throughout the process the data in the packets meet medical data privacy regulations like HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation).

The system raises instant alarms to the healthcare providers and caregivers in case abnormal patterns that may denote stroke or decrease in cognition are detected. Further, it is also possible to conduct longitudinal analysis of the cognitive health trends using cloud integration to adapt the therapy and make personalized interventions and predictive maintenance of the patient health. The proposed invention would be a useful and scalable method of real-time stroke prediction and cognitive state evaluation in the integrated health systems by means of adaptive neural processing and secure and low-power data management.

B. System Components
The suggested Adaptive Neural Framework of Low-Power Stroke Prediction and Cognitive State Assessment is a complex of several integrated elements, and each of them is important to provide the high accuracy of monitoring in real-time and energy efficiency and data protection. The first is the wearable sensor module that comprises of miniature, lightweight sensors that include accelerators, gyroscopes, electroencephalogram (EEG) electrodes, pulse oximeters and heart rate sensors. The sensors are intended to intercept physiological variables and motor activity that are related to stroke risk and cognitive functioning continuously. The wearable units have been made in an ergonomic way so that they can be non-invasive and the patient can comfortably use it long-term without impairing their day to day lives.
The second component is the edge processing component within the wearable or a nearby gateway device. This unit performs real-time sensor data preprocessing, such as noise reduction, normalization and feature extraction. Such local processing of data minimizes the volume of information sent across the air and thus consumes less battery power and less latency. Adaptive neural inference is also realised with the edge unit which dynamically varies the level of computational intensity of the neural network in accordance with the complexity of the data it receives, and with the individual baseline of the patient, achieving efficiency and accuracy.
The third component is the secure communications interface that transmit the processed data to a cloud-hosted healthcare platform. This interface employs remote data integrity checking that employs identity that verifies each data packet to confirm that it has not been compromised, lost, or impersonated. To assure privacy of sensitive health data in its transmission over wireless networks, secure communications protocols in line with the HIPAA and GDPR requirements are employed.
The fourth component is the cloud-based analytics and storage platform, which incorporates the data of numerous patients to achieve longitudinal data cognitive and cardiovascular analyses. Here, predictive models are optimized by using sophisticated machine learning algorithms that find subtle trends that may indicate a risk of stroke or cognitive impairment and generate actionable insights. The platform also supports dashboards which can enable healthcare providers to receive timely alerts, monitor remotely and plan an intervention that is personal.
Finally, the system possesses patient, caregiver and clinician user interfaces. These have interfaces with the capacity to send real-time notifications, health summaries and visualization of vital physiological measurements. It is connected to mobile and web applications to make it available and to ensure more patient engagement in their care. These elements are brought together as an integrated, low-powered, adaptive and secure real-time stroke prediction and cognitive state assessment system in interconnected health environments.

C. System Architecture and Workflow
The Adaptive Neural Framework of Low-Power Stroke Prediction and Cognitive State Assessment proposed system architecture consists of wearable sensing, edge computing, secure data transmission and cloud-based analytics, in a seamless pipeline. The wearable sensor module is the starting point of the workflow because it collects physiological and motion data of the patient constantly. This raw data is initially operated locally by the edge processing unit where noise reduction, normalization and extraction of features are done in real time. These features are then analysed by the adaptive neural network to determine any early signs of stroke risk or changes in cognitive states and the computational intensity varies according to the baseline of the patient and the complexity of the received data.

The identity-based remote data integrity checking module receives and transmits processed and verified data safely to ensure that every packet is not altered along the way. This data is consolidated in the cloud platform and advanced predictive models are applied to them and longitudinal records kept on each patient. Thereafter, user interface modules generate alerts and visualizations which are sent to the healthcare providers and patients in order to make informed decisions, implement personalized intervention, and monitor in a remote manner. The whole architecture is low-power friendly, accurate, and secure, which is why it can be used in a continuous monitoring in networked health systems.

Fig 1. system architecture and workflow

The figure illustrates the data flow in the proposed framework step by step. The Wearable Sensor Module (A) is the starting point of data capturing, which involves the vital physiological and motion parameters. This information is transmitted at once to the Edge Processing Unit (B), where preprocessing and feature extraction is performed. The Adaptive Neural Network Inference (C) element is then applied to analyse the processed data to identify the evidence of stroke risk or a change in the cognitive state in real-time.

Secure Communication Interface (D) is designed to make sure that no processed data is altered during transmission to the Cloud Analytics and Storage Platform (E) where predictive modeling and longitudinal analysis are conducted. Healthcare Providers (F) can then access the results, insights, and alerts to make medical decisions and Patients and Caregivers (G) to receive a real-time feedback and monitor. Each module will be built to be low-power consuming, very reliable, and data secure to keep operating in a connected health ecosystem continuously, accurately and safely.

D. Key Functional Modules and Algorithms:
The offered Adaptive Neural Framework will be organized around a few functional modules, which are in charge of a decisive part of the process of stroke prediction and cognitive state evaluation. These modules are all linked so that real-time monitoring, efficient computation and secure data handling are achieved, thus continuous health monitoring in a connected environment.
• Wearable Sensor Module: The wearable sensor module is non-invasive wearable sensors in the shape of wristbands, headbands, or clothing to measure physiological data of electrocardiograms (ECG), photoplethysmograms (PPG), blood pressure, oxygen saturation, and motion-related signals by using accelerometers and gyroscopes. The sensors are configured to operate at ultra-low power to extend battery life and at higher sampling rates in order to enhance the precise detection. Data acquisition algorithms provide initial signal filtering to remove noise and motion artifact and provide clean input to the subsequent processing.
• Edge Processing Unit: Once the raw signals are captured, the edge processing unit is then responsible of processing the signal in real-time through pre-processing which includes normalization, denoising, heart rate variability, pulse wave velocity, movement patterns and cognitive response measurements. This unit utilizes lightweight real time processable microcontroller friendly fog-free low-power algorithms that can do real time processing with no delay on cloud dependency. Dimensionality reduction algorithms such as the principal component analysis (PCA) are used to determine the most relevant features which in turn will lower the computational load to the neural network.
• Applying Adaptive Neural Network Inference: The learned features are passed through a dynamically tuned adaptive neural network whose architecture is adjusted dynamically as the characteristics of the incoming data evolve. As an example, when abnormal patterns of potential occurrence of stroke are identified, the network increases the number of neurons or layers in the network, and the reverse happens when the body is in normal operation to conserve energy. Neural network employs hybrid networks including convolutional layers to extract the spatial features and recurrent networks (e.g., LSTM) to extract temporal features. Another important need of wearable health devices is where the adaptive mechanism not only ensures high levels of prediction accuracy, but also low power.
• Checking Remote Data Integrity by Identity: To prevent the situation when the information that the wearable sends to the cloud is compromised and changed, the system will include identity-based cryptographic protocol. Each data packet is ciphered with the identity-based hash appended and this is verified by the cloud server before it is processed. This enables patient information to be genuine and undistorted by the ill-intentioned intent of altering such information, unauthorized individuals, or loss of information and assists in addressing privacy requirements in the medical care such as HIPAA or GDPR.
• Cloud Storage and Analytics: The cloud platform is supplied with preprocessed and integrity tested data of multiple patients. Longitudinal trend analysis, anomaly detection and stratification of risks are state of the art analytics. A full patient history is also available in the cloud module, and also it enables predictive modeling on the populations and actionable information to the healthcare provider.
• User Interface Module: finally, the framework avails findings, alerts and visualization to the healthcare nurses and patients in form of user friendly dashboards. Real time early warning of uncharacteristic cognitive progress or high risk of stroke is being observed that would allow action to be taken. The network also supports feedback loops where medical care professionals gain the opportunity to make notes on information to improve model learning and model personalization over time.
This inbuilt design will ensure that the proposed invention is robust, energy efficient, secure, and adaptable to present a comprehensive solution to real-time stroke prediction and cognitive health status tracking in integrated health systems.

E.NOVELTY:
The proposed Adaptive Neural Framework incorporates in a unique way ultra-low-power wearable sensing, dynamic neural network adaptation, and identity-based remote data integrity verification to support real-time, secure stroke prediction and state of cognition that is not considered in any previous work.

F. COMPARISON:
The Adaptive Neural Framework of Low-Power Stroke Prediction and Cognitive State Assessment in Connected Health Systems have a number of important benefits over the current solutions, both in feature and efficiency of implementation. In contrast to an old-fashioned wearable or cloud-based health monitoring system, which can use strong sensors or upload data to a remote server periodically, the suggested framework can use neural networks that operate at ultra-low power on the edge device. This allows the real-time and constant monitoring of vital signs and neurological status, without being quickly depleted by battery life, and thus the system can be used in the long term in either home or clinical settings.
The other distinguishing attribute is the adaptive ability of neural network. Unlike most of the current systems, which are run on less complex models that are trained on generalized data, our construct employs an adaptive learning system to personalize the predictions based on the historical and context-sensitive physiological details of the user. These assurances have enhanced predictability of stroke events and alterations of the brain condition despite the inter-patient variations and noises in the surroundings.
Besides it, the provided system includes identity-based remote data integrity checking that is the most significant gap of the deployed connected health solutions. The information about patients can be sent to the cloud with older methods with no efficient procedures of authenticity or detecting manipulation. The solution will guarantee that data being transmitted or stored can be authenticated in real-time and this will enhance better security and compliance with regulations like privacy policies like HIPAA.
Furthermore, the framework permits multi-modal sensor fusion that uses EEG, heart rate variability (HRV), motion sensors, and other vital sensors. Commercial solutions available tend to be single-modality-based and this limits predictive reliability. The algorithm can provide a more precise and comprehensive stroke risk and cognitive health estimate since it uses several physiological data points. Lastly, regarding the implement ability of the framework, the latter can work effectively on a broad selection of devices, including both low-cost wearable devices and edge-enabled hospital-monitoring units, without compromising the quality of predictions or the security of the obtained data. By contrast, most previous art systems either require large amounts of computation or trade predictive performance in lightweight applications.
Altogether, the proposed framework stands out due to the energy efficiency, adaptive intelligence, multi-modal integration, real-time prediction and secure data handling, which outpaced the shortcomings of the traditional stroke prediction and cognitive assessment systems.

G. ADDITIONAL INFORMATION:
In order to supplement our patent application of the Adaptive Neural Framework of Low-Power Stroke Prediction and Cognitive State Assessment in Connected Health Systems, we submit some supporting materials, such as claim set, system diagrams, and details of software implementation. All these are factors that depict the novelty of the invention, the implementation plan, and the possible effects it may have on the related healthcare systems.
, C , C , Claims:CLAIMS:
• Claim 1: A low-power adaptive neural architecture to real-time prediction of stroke incident and cognitive condition measurement, which includes an edge-based device with multi-modal sensor, an adaptable neural network execution engine and a distant data integrity verification engine.

• Claimed 2: The structure of claim 1, where the adaptive neural network engine is continuously updated on prediction models using individual user physiological data, such as heart rate variability, electroencephalogram (EEG) signals, motion parameters.

• Claim 3: The structure of claim 1, in which the identity-based remote data integrity verification guarantees that the transmissions of physiological data are verified and unalterable.

• Claim 4: The structure of claim 1, where the edge-based device is used under ultra-low-power conditions, allows long-periodic continuous operation without the frequent replacement of batteries.

• Claim 5: An algorithm to fuse multi-modal sensor data on-the-fly in predicting stroke, including finding the data of EEG, HRV, motion sensors, processing the signals, inputting them into the adaptive neural network, and producing real-time warnings of stroke threat and cognitive state abnormalities.