Araghni Bandyopadhyay

A proficient electronics and communication engineering student with broad-range experience in voice and data network support. Willingness to learn, follow instructions, and work cooperatively within team environments. Computer competencies include various CAD and MATLAB programs, along with a strong background in the Internet of Things (IoT).

A street photographer in my free time, exhibiting a true love for the craft, art and science of the photography process. Consistently enhancing creative insight through projects and professional workshops.

Introduction to IoT, NPTEL- IIT Kharagpur

IoT-Based System to Detect Sleep Apnea using Deep Learning

Collaborated with a team of four in the development of an IoT-based system to detect sleep apnea using Deep Learning.

Using single-lead 1D electrocardiogram (ECG) readings, our study proposed a sleep apnea detection method based on a one-dimensional (1D) deep convolutional neural network (CNN) model. We created an oxygen sensor system and a single-lead ECG sensor. The Raspberry Pi has been used as a platform for home care applications in its implementation. The CNN model was made up of four similar classification layers made mostly of fully connected networks, a flattening layer, ten identical CNN-based feature extraction layers, and a softmax classification layer. The suggested CNN model was trained using 35 released and withheld ECG recordings from the MIT PhysioNet Apnea-ECG Database, and its accuracy in identifying apnea occurrences was verified. The findings demonstrate that the suggested model attains an accuracy of 85.71%.