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REAL-TIME WEARABLE DEVICE FOR PREDICTIVE DETECTION AND ALERTING OF EPILEPTIC SEIZURES USING BIOMARKERS
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Abstract
Information
Inventors
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Specification
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ORDINARY APPLICATION
Published
Filed on 18 November 2024
Abstract
The present invention relates to a novel wearable device designed for the real-time detection and prediction of epileptic seizures. This device integrates multiple biometric sensors, including an accelerometer, pulse oximeter, vibration sensor, reverse iontophoresis module, and absorbent swabs, to continuously monitor physiological parameters such as body movement, heart rate variability, oxygen saturation, blood lactate levels, and cortisol levels. Utilizing advanced machine learning algorithms, the device analyzes the collected data to identify patterns and anomalies that precede seizure events, enabling timely intervention. The integrated alert system communicates high seizure risk alerts to both the patient and caregivers via a Bluetooth connection to mobile devices. The system also incorporates adaptive learning capabilities, continuously improving its predictive accuracy based on real-time data and user feedback. This innovation aims to enhance the quality of life for individuals with epilepsy by providing peace of mind and minimizing seizure-related risks.
Patent Information
Application ID | 202441089028 |
Invention Field | BIO-MEDICAL ENGINEERING |
Date of Application | 18/11/2024 |
Publication Number | 47/2024 |
Inventors
Name | Address | Country | Nationality |
---|---|---|---|
Dr. Prasanna Mohan | Professor, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Dr. Zeeshan Ali | Associate Professor, Department of Physiology, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Saleena Khanum A | Student, Department of Physiotheraphy, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Goutami Shirodkar | Student, Department of Physiotheraphy, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Applicants
Name | Address | Country | Nationality |
---|---|---|---|
Krupanidhi College of Physiotherapy | Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Dr. Prasanna Mohan | Professor, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Dr. Zeeshan Ali | Associate Professor, Department of Physiology, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Saleena Khanum A | Student, Department of Physiotheraphy, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Goutami Shirodkar | Student, Department of Physiotheraphy, Krupanidhi College of Physiotherapy, Chikkabellandur, Carmelaram Post, Varthur Hobli, Bangalore – 560035, Karnataka, India. | India | India |
Specification
Description:[0017].The following description provides specific details of certain aspects of the disclosure illustrated in the drawings to provide a thorough understanding of those aspects. It should be recognized, however, that the present disclosure can be reflected in additional aspects and the disclosure may be practiced without some of the details in the following description.
[0018].The various aspects including the example aspects are now described more fully with reference to the accompanying drawings, in which the various aspects of the disclosure are shown. The disclosure may, however, be embodied in different forms and should not be construed as limited to the aspects set forth herein. Rather, these aspects are provided so that this disclosure is thorough and complete, and fully conveys the scope of the disclosure to those skilled in the art. In the drawings, the sizes of components may be exaggerated for clarity.
[0019].It is understood that when an element or layer is referred to as being "on," "connected to," or "coupled to" another element or layer, it can be directly on, connected to, or coupled to the other element or layer or intervening elements or layers that may be present. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
[0020].The subject matter of example aspects, as disclosed herein, is described with specificity to meet statutory requirements. However, the description itself is not intended to limit the scope of this patent. Rather, the inventor/inventors have contemplated that the claimed subject matter might also be embodied in other ways, to include different features or combinations of features similar to the ones described in this document, in conjunction with other technologies.
[0021].The present invention relates to a novel wearable device designed for the real-time detection and prediction of epileptic seizures. This device integrates multiple biometric sensors, including an accelerometer, pulse oximeter, vibration sensor, reverse iontophoresis module, and absorbent swabs, to continuously monitor physiological parameters such as body movement, heart rate variability, oxygen saturation, blood lactate levels, and cortisol levels. Utilizing advanced machine learning algorithms, the device analyzes the collected data to identify patterns and anomalies that precede seizure events, enabling timely intervention. The integrated alert system communicates high seizure risk alerts to both the patient and caregivers via a Bluetooth connection to mobile devices. The system also incorporates adaptive learning capabilities, continuously improving its predictive accuracy based on real-time data and user feedback. This innovation aims to enhance the quality of life for individuals with epilepsy by providing peace of mind and minimizing seizure-related risks.
[0022].Epilepsy is a chronic neurological disorder affecting millions of individuals worldwide, characterized by recurrent, unprovoked seizures due to abnormal electrical activity in the brain. According to the World Health Organization, approximately 50 million people live with epilepsy, with a significant percentage experiencing frequent seizures that can severely impact their daily lives, social interactions, and mental health. While many patients can effectively manage their condition through antiepileptic drugs, a considerable number suffer from pharmacoresistant epilepsy, where seizures persist despite treatment. This inadequacy of pharmacological interventions highlights an urgent need for innovative solutions that can improve seizure management and enhance patient safety.
[0023].Current methods for detecting seizures primarily rely on visual observation or traditional EEG monitoring, which can be cumbersome and often impractical for everyday use. In addition, many wearable seizure detection devices on the market today focus on single biometric parameters, such as motion or heart rate, which may not provide a comprehensive understanding of a patient's condition leading up to a seizure. Consequently, these devices may fail to detect seizures in a timely manner, resulting in potential injury or exacerbation of the condition.
[0024].With the rapid advancements in sensor technologies and artificial intelligence, there is a significant opportunity to develop a more effective solution. Modern wearable devices equipped with multi-modal sensors can continuously monitor various physiological parameters, including heart rate variability, oxygen saturation, metabolic markers, and cortisol levels, which can provide insights into the patient's physiological state before a seizure occurs. Integrating these diverse data streams allows for a holistic view of the individual's health, enabling more accurate and timely detection of seizure precursors.
[0025].Machine learning algorithms can play a crucial role in analyzing the rich datasets generated by these wearable devices. By utilizing advanced data cleaning, feature extraction, and anomaly detection techniques, these algorithms can identify subtle patterns and trends that precede seizures, thus enhancing predictive capabilities. The potential for developing personalized predictive models tailored to the unique physiological profiles of individual patients could further improve the accuracy and reliability of seizure predictions.
[0026].Moreover, real-time alert systems integrated within wearable devices can significantly enhance patient safety. By providing immediate notifications to patients and caregivers upon detecting a high likelihood of seizure occurrence, timely interventions can be initiated, potentially preventing injuries and minimizing the psychological burden associated with unpredictability in seizure events.
[0027].This invention aims to bridge the gap in current seizure detection methodologies by introducing a comprehensive, cost-effective, and user-friendly wearable device. By leveraging continuous monitoring of multiple biomarkers, sophisticated data analytics, and real-time alerting mechanisms, this innovative approach promises to empower individuals living with epilepsy, improve their quality of life, and reduce the risks associated with seizure episodes.
[0028].The present invention relates to a sophisticated wearable device specifically designed for the real-time detection and prediction of epileptic seizures, aiming to transform the management of epilepsy and enhance the quality of life for individuals living with this challenging condition. This device integrates an array of advanced sensors and sophisticated data processing algorithms to provide continuous monitoring of various physiological parameters indicative of imminent seizure activity.
[0029].At the core of the device is an accelerometer, which accurately tracks the user's body movements and orientation. By analyzing changes in movement patterns, the device can detect atypical behaviors commonly associated with seizures, such as sudden jerks or unusual postures. Complementing the accelerometer is a pulse oximeter, which measures heart rate variability and oxygen saturation levels. These metrics are critical, as fluctuations in heart rate and oxygen levels can precede seizures, providing vital data that enhances the predictive capabilities of the device.
[0030].In addition to these sensors, the device incorporates a vibration sensor to identify rapid, erratic movements that may signal an oncoming seizure. A reverse iontophoresis module is also included, which non-invasively measures blood lactate levels-an important metabolic indicator often elevated during stress or seizure events. Furthermore, absorbent swabs are utilized to collect samples for cortisol analysis, enabling the device to monitor stress hormones that may act as potential triggers for seizures.
[0031].The data collected from these diverse sensors is processed by an onboard microprocessor that employs cutting-edge machine learning algorithms. These algorithms are designed to perform multiple functions, starting with data cleaning to eliminate noise and irrelevant artifacts, ensuring that only high-quality data is analyzed. Advanced feature extraction techniques are then utilized to identify and isolate significant markers within the biometric data that correlate with seizure occurrences. By continuously analyzing both historical data and real-time inputs, the device's algorithms can recognize patterns and anomalies that indicate a high likelihood of an impending seizure, allowing it to predict seizures often several minutes before they occur.
[0032].The innovation of this device is further enhanced by its real-time alert system, which communicates via Bluetooth technology with mobile devices used by both patients and their caregivers. Upon detecting a heightened risk of seizure, the device promptly generates and sends notifications to connected smartphones. This immediate communication facilitates rapid intervention, empowering caregivers to respond quickly, potentially preventing injuries or complications associated with seizures. In addition to alerts, the device can provide users with reminders to engage in calming techniques or seek safe environments when a seizure is anticipated.
[0033].A significant feature of this invention is its continuous learning capability. The machine learning models are designed to evolve based on new data inputs and user feedback. As the device gathers more data over time, it refines its predictive algorithms, increasing the accuracy and personalization of seizure predictions. This adaptability is crucial for individuals with epilepsy, as their conditions may change over time due to factors such as medication adjustments, lifestyle changes, or the natural progression of the disorder.
[0034].Moreover, the device is engineered with user-friendliness in mind. It is designed to be lightweight, comfortable, and unobtrusive, allowing for continuous wear without disrupting daily activities. The data collected by the device can be visualized through a user-friendly mobile application, providing users and caregivers with intuitive access to health insights, historical data, and predictive analytics regarding seizure risk.
[0035].In summary, this invention represents a groundbreaking approach to seizure management, merging multi-modal biometric monitoring, advanced machine learning techniques, and real-time alert mechanisms into an integrated wearable device. By empowering individuals with epilepsy to anticipate and respond to seizure events, this technology not only aims to improve their safety and well-being but also strives to foster a sense of autonomy and confidence in managing their health. Ultimately, the invention has the potential to redefine the standard of care for epilepsy, offering a proactive solution that addresses the unmet needs of patients living with this complex neurological disorder.
[0036].In conclusion, the invention of the real-time wearable device for the detection and prediction of epileptic seizures represents a significant advancement in the field of epilepsy management. By integrating an array of sophisticated sensors and leveraging advanced machine learning algorithms, this device offers a comprehensive solution for continuous monitoring of key physiological parameters that can indicate impending seizures. The ability to provide timely alerts not only enhances patient safety but also empowers individuals with epilepsy to take proactive measures in managing their condition.
[0037].The continuous learning capabilities of the device further distinguish it from existing technologies, allowing it to adapt to each user's unique physiological patterns and improve its predictive accuracy over time. This adaptability is crucial in addressing the diverse and dynamic nature of epilepsy, ensuring that the device remains effective and relevant throughout the patient's journey.
[0038].This innovative approach not only aligns with the current trends in personalized medicine but also paves the way for future developments in wearable health technologies. The invention is positioned to become a transformative tool in the management of epilepsy, and we believe it holds substantial promise for improving the lives of countless individuals affected by this condition. , Claims:1.A wearable device for detecting and predicting epileptic seizures, comprising:
a) An accelerometer for monitoring body movement;
b) A pulse oximeter for measuring heart rate variability and oxygen saturation;
c) A vibration sensor for detecting jerky movements;
d) A reverse iontophoresis module for measuring blood lactate levels;
e) Absorbent swabs for determining cortisol levels;
f) A processor configured to continuously monitor biometric data from said sensors;
g) A machine learning model for analysing said biometric data to identify patterns indicative of imminent seizures.
2.The wearable device as claimed in Claim 1, further comprising a real-time alert system that notifies both the patient and caregivers via a Bluetooth module connected to mobile devices upon detecting a high likelihood of seizure occurrence.
3.The wearable device as claimed in Claim 1, wherein the machine learning model incorporates data cleaning algorithms to pre-process and filter noise from the biometric data, feature extraction techniques to identify significant biomarkers preceding seizure events, anomaly detection methods for recognizing unusual patterns within the monitored data.
4.The wearable device as claimed in Claim 1, wherein the processor is further configured to adaptively update the machine learning model based on new patient data, enhancing predictive accuracy over time.
5.A method for predicting epileptic seizures using the wearable device, comprising:
a) Continuously collecting biometric data from the sensors;
b) Analysing the data using machine learning algorithms to detect anomalies;
c) Predicting the likelihood of a seizure occurring and generating alerts in advance.
6.The method as claimed in Claim 5, wherein the predictions are tailored to individual users by developing personalized models based on historical biometric data.
Documents
Name | Date |
---|---|
202441089028-FORM-26 [19-11-2024(online)].pdf | 19/11/2024 |
202441089028-COMPLETE SPECIFICATION [18-11-2024(online)].pdf | 18/11/2024 |
202441089028-DRAWINGS [18-11-2024(online)].pdf | 18/11/2024 |
202441089028-ENDORSEMENT BY INVENTORS [18-11-2024(online)].pdf | 18/11/2024 |
202441089028-FORM 1 [18-11-2024(online)].pdf | 18/11/2024 |
202441089028-FORM 3 [18-11-2024(online)].pdf | 18/11/2024 |
202441089028-FORM-5 [18-11-2024(online)].pdf | 18/11/2024 |
202441089028-FORM-9 [18-11-2024(online)].pdf | 18/11/2024 |
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