An Approach to Develop Cerebra-Vascular-Haemato-Cardiac Detector using Machine Learning Techniques

Abstract

The "Design of Cerebra-Vascular-Haemato-Cardiac Detector using Machine Learning Approaches" project is a groundbreaking initiative that seeks to revolutionize medical diagnostics by integrating advanced machine learning techniques. This multifaceted project encompasses the development of a comprehensive detector capable of analysing cerebrovascular, haematological, and cardiac data to provide accurate and timely health assessments. The integration of diverse data modalities and the creation of a real-time detection system will contribute to early disease detection and improved patient outcomes. [1][2][3]

Cerebrovascular stroke and heart attack stand as prominent causes of disability and global mortality, underscoring the critical need for early detection to facilitate effective treatment. Traditional diagnostic systems for Electroencephalograph (EEG) and Electrocardiograph (ECG) are non-wearable due to their unwieldy instruments, posing a significant challenge. In response, we propose a Cerebra-Vascular-Haemato-Cardiac (CVHC) detector designed to detect strokes and cardiac arrests. The key elements of the CVHC detector include EEG and ECG, with additional integration of an oxygen sensor and blood pressure sensor. This comprehensive system aims to identify anomalies in the human body associated with strokes, cardiac arrests, respiratory issues, and heat strokes. Leveraging Machine Learning, the CVHC device provides immediate emergency alerts.

The signals originating from ECG, EEG, blood pressure sensor, and oxygen sensor undergo processing through a simulator, Discrete Wavelet Transformation (DWT), and Convolutional Neural Network (CNN). This processing involves convolution, noise reduction, and wavelet differentiation of EEG and ECG signals, ultimately amplifying them to attain usable voltage. The proposed system is adept at detecting Ischemic Stroke, Haemorrhagic Stroke, and Cardiopulmonary Arrest by comparing the acquired signals with predefined patterns of normal brain and heart activities. This innovative CVHC solution thus addresses the limitations of traditional diagnostic instruments, offering a wearable and efficient alternative for early detection of life-threatening conditions.