Department Publications

This paper presents a methodology to diagnose some heart diseases which can be identified using heart sounds. As the first stage, audio files in . wav format are acquired using an electronic stethoscope and entered to a database with patient symptoms. Prominent features and statistical parameters needed for disease detection are extracted from the heart sound samples using some algorithm. Patient's heart sound fde and symptoms are given as inputs to the developed software for analysis to find out the diseases

Monitoring the ECG (Electrocardiogram) of patients with cardiac disorder is of paramount importance since they may undergo cardiac arrhythmias even without noticing it. Majority of existing devices for this purpose are only capable of recording ECG of patients which are analyzed later on by cardiologists. This paper presents a new system developed to continuously monitor the ECG of patients and analyze them in real time to identify any abnormalities. The new system consists of two parts: a portable device worn by the patient and a server side component. The arrhythmia detection algorithm used in the portable device shows 99.78% sensitivity, 99.65% positive predictivity for QRS complex detection and 93.48% sensitivity, 96.45% positive predictivity for ventricular beat classification when evaluated on MIT-BIH arrhythmia database. When an arrhythmia is detected the device automatically uploads the ECG data in …

In this paper EOG signal processing and developing a low cost signal interface for wheelchair with high accuracy and reliability for severely disabled people is presented. Here the signal processing is done on a microcontroller which reduces the cost drastically rather than using a computer. Eye movement is detected by processing EOG signal, and associates the eye movement to motion commands of the wheelchair such as forward, reverse, left and right. A 99% accurate classification has been demonstrated experimentally.

An electroencephalogram (EEG) instrument has been successfully developed and tested for capturing, visualizing and recording brain activity using both active and passive electrodes. The key idea is to capture a noise free composite brain wave and separate out its constituent components, namely delta, theta, alpha, beta and gamma. The system consists of three functional parts including EEG signal capturing, Digitizing and wireless transmission and PC end software for visualizing and post processing. EEG signal capturing part consists of electrodes, amplification circuit for each channel and Right Leg Driver circuit to eliminate 50 Hz power line noise. The digital band pass filters and the 50 Hz notch filter which confiscates any residual noise components are built in the PC software. Battery power and wireless communication gives the sovereignty to the user to move around with the system while being monitored …

This study presents an effective stimulus which enhances the auditory brainstem responses (ABRs) from the underlying EEG. A range of different types of stimuli are implemented and tested using a modified ABR model which is a mathematical approximation of the auditory pathway that generates the ABR. The optimised stimulus is based on a chirp signal which is designed to compensate the travelling delay caused by the anatomical structure of the cochlea, especially at sound intensity levels near the hearing threshold. Results suggest an average increase of 36% in the amplitude of wave V compared to the chirp which used to design the optimised stimulus.