MUMBAI, India, Aug. 10 -- Intellectual Property India has published a patent application (202641089949 A) filed by Madhankumar C; Ramadevi K. S.; Mrs. Sajida Nazneen; and Mrs. Shifa Anjum on July 23, 2026, for Machine Learning Based System For Identifying And Monitoring Neurological Disorders.
Inventors include Ramadevi K. S.; Mrs. Sajida Nazneen; and Mrs. Shifa Anjum.
The application for the patent was published on July 31, 2026, under issue no. 31/2026.
Abstract: Machine learning based system for identifying and monitoring neurological disorders Abstract Neurological disorders such as Alzheimer's disease, Parkinson's disease, epilepsy, multiple sclerosis, and stroke require early diagnosis and continuous monitoring to improve patient outcomes and reduce disease progression. This invention proposes a Machine Learning-Based System for Identifying and Monitoring Neurological Disorders that integrates artificial intelligence, wearable sensor technology, medical imaging, and clinical data analytics to provide accurate, real time detection and long-term disease monitoring. The proposed system collects multimodal patient data, including electroencephalography (EEG) signals, magnetic resonance imaging (MRI), computed tomography (CT) scans, speech patterns, gait analysis, physiological sensor readings, and electronic health records. Advanced preprocessing techniques remove noise and normalize the data before extracting meaningful features. Machine learning and deep learning algorithms, including Random Forest, Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Transformer-based models, analyze these features to identify neurological abnormalities and classify disease severity. The system continuously monitors patient health using wearable IoT devices and mobile applications, enabling remote tracking of motor function, cognitive performance, sleep quality, tremor intensity, seizure activity, and vital signs. Predictive analytics estimate disease progression, detect early warning signs of deterioration, and generate personalized treatment recommendations. Explainable AI (XAI) techniques provide transparent diagnostic results to assist neurologists in clinical decision-making. A secure cloud-based platform stores patient records while ensuring privacy through encryption, role-based access control, and regulatory compliance. Automated alerts are sent to healthcare providers and caregivers when abnormal neurological events or significant health changes are detected. The system also generates comprehensive analytical reports to support long-term patient management and clinical research. The proposed invention enhances diagnostic accuracy, enables continuous remote monitoring, facilitates timely medical intervention, reduces healthcare costs, and improves the quality of life for patients suffering from neurological disorders. It provides a scalable, intelligent, and reliable healthcare solution suitable for hospitals, telemedicine platforms, rehabilitation centers, and home-based patient care.
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